add-missing-cache-token-to-calculation

Match on forwarded model id not model

.github/workflows/container.yml

@@ -37,6 +37,7 @@ jobs:
         uses: docker/build-push-action@v4
         with:
           context: .
+          file: Dockerfile.full
           push: true
           build-args: |
             GIT_COMMIT=${{ steps.gitmeta.outputs.sha }}

docs/provider/deployment.md

@@ -2,6 +2,57 @@
 
 Production deployment guide for Routstr Provider nodes.
 
+## All-in-One Docker Image (Preferred)
+
+The easiest way to deploy Routstr is using the all-in-one Docker image from Docker Hub, which includes both the FastAPI backend and the Next.js admin dashboard in a single container.
+
+### Quick Start
+
+```bash
+docker run -d \
+  --name routstr \
+  -p 8000:8000 \
+  -v routstr-data:/app/data \
+  -e DATABASE_URL="sqlite:////app/data/routstr.db" \
+  9qeklajc/routstr:latest
+```
+
+Access your node:
+- **API & Admin Dashboard**: http://localhost:8000
+
+### Docker Compose Setup
+
+Create `docker-compose.yml`:
+
+```yaml
+version: '3.8'
+
+services:
+  routstr:
+    image: 9qeklajc/routstr:latest
+    container_name: routstr
+    restart: unless-stopped
+    ports:
+      - "8000:8000"
+    volumes:
+      - routstr-data:/app/data
+    environment:
+      DATABASE_URL: "sqlite:////app/data/routstr.db"
+      ADMIN_KEY: "your-secure-admin-key"
+      LOG_LEVEL: "info"
+
+volumes:
+  routstr-data:
+```
+
+Start it:
+
+```bash
+docker compose up -d
+```
+
+---
+
 ## Docker Compose (Recommended)
 
 For production, use Docker Compose with persistent storage and optional Tor support.

pyproject.toml

@@ -13,7 +13,7 @@ dependencies = [
     "greenlet>=3.2.1",
     "alembic>=1.13",
     "python-json-logger>=2.0.0",
-    "cashu",
+    "cashu>=0.20",
     "secp256k1",
     "marshmallow>=3.13,<4.0",
     "websockets>=12.0",
@@ -21,6 +21,7 @@ dependencies = [
     "mdurl==0.1.2",
     "pillow>=10",
     "openai>=1.98.0",
+    "litellm>=1.55.0",
 ]
 
 [dependency-groups]

routstr/algorithm.py

@@ -195,14 +195,15 @@ def create_model_mappings(
 
             # Add to unique models
             base_id = get_base_model_id(model_to_use.id)
-            if not is_openrouter or base_id not in unique_models:
+            unique_key = model_to_use.forwarded_model_id or base_id
+            if not is_openrouter or unique_key not in unique_models:
                 unique_model = model_to_use.copy(
                     update={
                         "id": base_id,
                         "upstream_provider_id": upstream.provider_type,
                     }
                 )
-                unique_models[base_id] = unique_model
+                unique_models[unique_key] = unique_model
 
             # Get all aliases for this model
             aliases = resolve_model_alias(
@@ -272,18 +273,19 @@ def create_model_mappings(
             continue
 
         base_id = get_base_model_id(model_to_use.id)
+        unique_key = model_to_use.forwarded_model_id or base_id
         is_openrouter = (
             getattr(upstream_for_override, "base_url", "")
             == "https://openrouter.ai/api/v1"
         )
-        if not is_openrouter or base_id not in unique_models:
+        if not is_openrouter or unique_key not in unique_models:
             unique_model = model_to_use.copy(
                 update={
                     "id": base_id,
                     "upstream_provider_id": upstream_for_override.provider_type,
                 }
             )
-            unique_models[base_id] = unique_model
+            unique_models[unique_key] = unique_model
 
         try:
             aliases = resolve_model_alias(
@@ -322,7 +324,26 @@ def create_model_mappings(
     provider_map: dict[str, list["BaseUpstreamProvider"]] = {}
 
     def alias_priority(model: "Model", alias: str) -> int:
-        """Rank how strong the mapping of alias->model is."""
+        """Rank how strong the mapping of alias->model is.
+
+        forwarded_model_id is the most specific identifier (set per-provider
+        instance), so a match there should beat a model_id match. This way,
+        when multiple providers have the same model_id but different
+        forwarded_model_ids, the one whose forwarded_model_id equals the
+        requested alias wins.
+        """
+        if (
+            model.forwarded_model_id
+            and model.forwarded_model_id.lower() == alias
+        ):
+            return 5
+
+        if (
+            model.id
+            and model.id.lower() == alias
+        ):
+            return 4
+
         model_base = get_base_model_id(model.id)
         if model_base == alias:
             return 3

routstr/auth.py

@@ -317,13 +317,13 @@ async def validate_bearer_key(
                 extra={"key_hash": hashed_key[:8] + "..."},
             )
 
-            logger.info(
+            logger.debug(
                 "AUTH: About to call credit_balance",
                 extra={"token_preview": bearer_key[:50]},
             )
             try:
                 msats = await credit_balance(bearer_key, new_key, session)
-                logger.info(
+                logger.debug(
                     "AUTH: credit_balance returned successfully", extra={"msats": msats}
                 )
             except Exception as credit_error:

routstr/core/admin.py

@@ -5,7 +5,7 @@ from datetime import datetime, timezone
 from pathlib import Path
 
 from fastapi import APIRouter, Depends, HTTPException, Query, Request
-from pydantic import BaseModel
+from pydantic import BaseModel, RootModel
 from sqlmodel import select
 
 from ..payment.models import _row_to_model, list_models
@@ -142,8 +142,8 @@ async def get_settings(request: Request) -> dict:
     return data
 
 
-class SettingsUpdate(BaseModel):
-    __root__: dict[str, object]
+class SettingsUpdate(RootModel[dict[str, object]]):
+    pass
 
 
 class PasswordUpdate(BaseModel):
@@ -154,7 +154,7 @@ class PasswordUpdate(BaseModel):
 @admin_router.patch("/api/settings", dependencies=[Depends(require_admin_api)])
 async def update_settings(request: Request, update: SettingsUpdate) -> dict:
     # Remove sensitive fields from general settings update
-    settings_data = update.__root__.copy()
+    settings_data = update.root.copy()
     sensitive_fields = ["admin_password", "upstream_api_key", "nsec"]
     for field in sensitive_fields:
         if field in settings_data:

routstr/core/db.py

@@ -79,11 +79,10 @@ class ApiKey(SQLModel, table=True):  # type: ignore
 
 
 async def reset_all_reserved_balances(session: AsyncSession) -> None:
-    logger.info("Resetting all reserved balances to 0")
     stmt = update(ApiKey).values(reserved_balance=0)
     await session.exec(stmt)  # type: ignore[call-overload]
     await session.commit()
-    logger.info("Reserved balances reset successfully")
+    logger.info("Reset reserved balances on startup")
 
 
 class ModelRow(SQLModel, table=True):  # type: ignore

routstr/core/exceptions.py

@@ -22,16 +22,20 @@ async def http_exception_handler(request: Request, exc: Exception) -> JSONRespon
     # Get status code and detail - works for both FastAPI and Starlette HTTPException
     status_code = getattr(exc, "status_code", 500)
     detail = getattr(exc, "detail", str(exc))
+    path = request.url.path
 
-    logger.warning(
-        "HTTP exception",
-        extra={
-            "request_id": request_id,
-            "status_code": status_code,
-            "detail": detail,
-            "path": request.url.path,
-        },
-    )
+    # 4xx is client behaviour; the uvicorn access log already records it.
+    # Only 5xx warrants a server-side warning/error log here.
+    if status_code >= 500:
+        logger.error(
+            f"HTTP {status_code} on {path}: {detail}",
+            extra={
+                "request_id": request_id,
+                "status_code": status_code,
+                "detail": detail,
+                "path": path,
+            },
+        )
 
     return JSONResponse(
         status_code=status_code,

routstr/core/logging.py

@@ -41,14 +41,23 @@ import logging.config
 import logging.handlers
 import os
 import re
+import sys
 import tomllib
 from datetime import datetime
 from pathlib import Path
 from typing import Any
 
 from pythonjsonlogger import jsonlogger
+from rich.console import Console
 from rich.logging import RichHandler
 
+# Only use RichHandler when stdout is a real TTY. In non-TTY contexts
+# (docker logs, pipes, CI) Rich pads every line to width and wraps long
+# records, producing visually-empty trailing whitespace and split records.
+# A plain StreamHandler avoids both problems.
+_stdout_is_tty = sys.stdout.isatty()
+_console = Console(soft_wrap=True) if _stdout_is_tty else None
+
 # Define custom TRACE level
 TRACE_LEVEL = 5
 logging.addLevelName(TRACE_LEVEL, "TRACE")
@@ -261,6 +270,26 @@ def setup_logging() -> None:
     if console_enabled:
         handlers.append("console")
 
+    if _stdout_is_tty:
+        console_handler: dict[str, Any] = {
+            "()": RichHandler,
+            "level": log_level,
+            "show_time": False,
+            "show_path": False,
+            "rich_tracebacks": True,
+            "markup": True,
+            "console": _console,
+            "filters": ["request_id_filter", "security_filter"],
+        }
+    else:
+        console_handler = {
+            "class": "logging.StreamHandler",
+            "level": log_level,
+            "formatter": "plain",
+            "stream": "ext://sys.stdout",
+            "filters": ["request_id_filter", "security_filter"],
+        }
+
     LOGGING_CONFIG = {
         "version": 1,
         "disable_existing_loggers": False,
@@ -270,6 +299,10 @@ def setup_logging() -> None:
                 "format": "%(asctime)s %(name)s %(levelname)s %(message)s %(pathname)s %(lineno)d %(version)s %(request_id)s",
                 "datefmt": "%Y-%m-%d %H:%M:%S",
             },
+            "plain": {
+                "format": "%(asctime)s %(levelname)-7s %(name)s %(message)s",
+                "datefmt": "%Y-%m-%d %H:%M:%S",
+            },
         },
         "filters": {
             "version_filter": {"()": VersionFilter},
@@ -277,15 +310,7 @@ def setup_logging() -> None:
             "security_filter": {"()": SecurityFilter},
         },
         "handlers": {
-            "console": {
-                "()": RichHandler,
-                "level": log_level,
-                "show_time": False,
-                "show_path": False,
-                "rich_tracebacks": True,
-                "markup": True,
-                "filters": ["request_id_filter", "security_filter"],
-            },
+            "console": console_handler,
             "file": {
                 "()": DailyRotatingFileHandler,
                 "level": log_level,

routstr/core/main.py

@@ -23,6 +23,7 @@ from ..payment.models import models_router, update_sats_pricing
 from ..payment.price import update_prices_periodically
 from ..proxy import initialize_upstreams, proxy_router, refresh_model_maps_periodically
 from ..upstream.auto_topup import periodic_auto_topup
+from ..upstream.litellm_routing import configure_litellm
 from ..wallet import periodic_payout, periodic_refund_sweep, periodic_routstr_fee_payout
 from .admin import admin_router
 from .db import create_session, init_db, run_migrations
@@ -56,6 +57,10 @@ async def lifespan(_: FastAPI) -> AsyncGenerator[None, None]:
     routstr_fee_task = None
 
     try:
+        # Apply litellm-wide settings (drop_params, chat-completions URL,
+        # debug logging) before any upstream provider dispatches a request.
+        configure_litellm()
+
         # Run database migrations on startup
         run_migrations()
 
@@ -272,109 +277,63 @@ if UI_DIST_PATH.exists() and UI_DIST_PATH.is_dir():
             UI_DIST_PATH / "index.txt", media_type="text/x-component"
         )
 
+    # Next.js is built with `trailingSlash: true`, so all UI page URLs end
+    # with a slash (e.g. `/login/`). The proxy router catches `/{path:path}`
+    # before FastAPI's `redirect_slashes` logic can normalize the URL, so we
+    # must register both the with-slash and without-slash variants here.
+    UI_PAGES = (
+        "dashboard",
+        "login",
+        "model",
+        "providers",
+        "settings",
+        "transactions",
+        "balances",
+        "logs",
+        "usage",
+        "unauthorized",
+    )
+
+    def _register_ui_page(name: str) -> None:
+        page_dir = UI_DIST_PATH / name
+        index_html = page_dir / "index.html"
+        index_txt = page_dir / "index.txt"
+
+        async def serve_page() -> FileResponse:
+            return FileResponse(index_html)
+
+        async def serve_page_rsc() -> FileResponse:
+            return FileResponse(index_txt, media_type="text/x-component")
+
+        app.add_api_route(
+            f"/{name}",
+            serve_page,
+            methods=["GET"],
+            include_in_schema=False,
+            name=f"serve_{name}_ui",
+        )
+        app.add_api_route(
+            f"/{name}/",
+            serve_page,
+            methods=["GET"],
+            include_in_schema=False,
+            name=f"serve_{name}_ui_slash",
+        )
+        app.add_api_route(
+            f"/{name}/index.txt",
+            serve_page_rsc,
+            methods=["GET"],
+            include_in_schema=False,
+            name=f"serve_{name}_rsc",
+        )
+
+    for _page in UI_PAGES:
+        _register_ui_page(_page)
+
     @app.get("/admin")
     async def admin_redirect() -> FileResponse:
         return FileResponse(UI_DIST_PATH / "index.html")
 
-    @app.get("/dashboard", include_in_schema=False)
-    async def serve_dashboard_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "index.html")
-
-    @app.get("/login", include_in_schema=False)
-    async def serve_login_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "login" / "index.html")
-
-    @app.get("/login/index.txt", include_in_schema=False)
-    async def serve_login_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "login" / "index.txt", media_type="text/x-component"
-        )
-
-    @app.get("/model", include_in_schema=False)
-    async def serve_models_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "model" / "index.html")
-
-    @app.get("/model/index.txt", include_in_schema=False)
-    async def serve_model_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "model" / "index.txt", media_type="text/x-component"
-        )
-
-    @app.get("/providers", include_in_schema=False)
-    async def serve_providers_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "providers" / "index.html")
-
-    @app.get("/providers/index.txt", include_in_schema=False)
-    async def serve_providers_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "providers" / "index.txt",
-            media_type="text/x-component",
-        )
-
-    @app.get("/settings", include_in_schema=False)
-    async def serve_settings_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "settings" / "index.html")
-
-    @app.get("/settings/index.txt", include_in_schema=False)
-    async def serve_settings_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "settings" / "index.txt",
-            media_type="text/x-component",
-        )
-
-    @app.get("/transactions", include_in_schema=False)
-    async def serve_transactions_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "transactions" / "index.html")
-
-    @app.get("/transactions/index.txt", include_in_schema=False)
-    async def serve_transactions_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "transactions" / "index.txt",
-            media_type="text/x-component",
-        )
-
-    @app.get("/balances", include_in_schema=False)
-    async def serve_balances_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "balances" / "index.html")
-
-    @app.get("/balances/index.txt", include_in_schema=False)
-    async def serve_balances_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "balances" / "index.txt",
-            media_type="text/x-component",
-        )
-
-    @app.get("/logs", include_in_schema=False)
-    async def serve_logs_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "logs" / "index.html")
-
-    @app.get("/logs/index.txt", include_in_schema=False)
-    async def serve_logs_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "logs" / "index.txt", media_type="text/x-component"
-        )
-
-    @app.get("/usage", include_in_schema=False)
-    async def serve_usage_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "usage" / "index.html")
-
-    @app.get("/usage/index.txt", include_in_schema=False)
-    async def serve_usage_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "usage" / "index.txt", media_type="text/x-component"
-        )
-
-    @app.get("/unauthorized", include_in_schema=False)
-    async def serve_unauthorized_ui() -> FileResponse:
-        return FileResponse(UI_DIST_PATH / "unauthorized" / "index.html")
-
-    @app.get("/unauthorized/index.txt", include_in_schema=False)
-    async def serve_unauthorized_rsc() -> FileResponse:
-        return FileResponse(
-            UI_DIST_PATH / "unauthorized" / "index.txt",
-            media_type="text/x-component",
-        )
-
     @app.get("/favicon.ico", include_in_schema=False)
     async def serve_favicon() -> FileResponse:
         icon_path = UI_DIST_PATH / "icon.ico"

routstr/payment/cost_calculation.py

@@ -18,6 +18,10 @@ class CostData(BaseModel):
     total_usd: float = 0.0
     input_tokens: int = 0
     output_tokens: int = 0
+    cache_read_input_tokens: int = 0
+    cache_creation_input_tokens: int = 0
+    cache_read_msats: int = 0
+    cache_creation_msats: int = 0
 
 
 class MaxCostData(CostData):
@@ -29,11 +33,10 @@ class CostDataError(BaseModel):
     code: str
 
 
-async def calculate_cost(  # todo: can be sync
+async def calculate_cost(
     response_data: dict, max_cost: int, session: AsyncSession
 ) -> CostData | MaxCostData | CostDataError:
-    """
-    Calculate the cost of an API request based on token usage.
+    """Calculate the cost of an API request based on token usage.
 
     Args:
         response_data: Response data containing usage information
@@ -51,12 +54,20 @@ async def calculate_cost(  # todo: can be sync
         },
     )
 
+    # Check for usage data
     if "usage" not in response_data or response_data["usage"] is None:
         logger.warning(
-            "No usage data in response, using base cost only",
+            "No usage data in response — billing at MaxCostData with zero "
+            "tokens. Dashboard will show this request as `(0+0)`. Most "
+            "common cause: upstream stream did not include a final usage "
+            "chunk (OpenAI-compat backends require "
+            "`stream_options.include_usage=true`).",
             extra={
                 "max_cost_msats": max_cost,
                 "model": response_data.get("model", "unknown"),
+                "response_keys": sorted(response_data.keys())
+                if isinstance(response_data, dict)
+                else None,
             },
         )
         return MaxCostData(
@@ -67,116 +78,58 @@ async def calculate_cost(  # todo: can be sync
             total_usd=0.0,
             input_tokens=0,
             output_tokens=0,
+            cache_read_input_tokens=0,
+            cache_creation_input_tokens=0,
+            cache_read_msats=0,
+            cache_creation_msats=0,
         )
 
     usage_data = response_data["usage"]
 
-    def parse_token_count(value: object) -> int:
-        if isinstance(value, bool):
-            return 0
-        if isinstance(value, int):
-            return max(0, value)
-        if isinstance(value, float):
-            return max(0, int(value))
-        if isinstance(value, str):
-            try:
-                return max(0, int(float(value)))
-            except ValueError:
-                return 0
-        return 0
+    # Extract token counts
+    input_tokens = _extract_token_pair(usage_data, "prompt_tokens", "input_tokens")
+    output_tokens = _extract_token_pair(usage_data, "completion_tokens", "output_tokens")
 
-    input_tokens = parse_token_count(usage_data.get("prompt_tokens", 0))
-    output_tokens = parse_token_count(usage_data.get("completion_tokens", 0))
-    input_tokens = (
-        input_tokens
-        if input_tokens != 0
-        else parse_token_count(usage_data.get("input_tokens", 0))
-    )
-    output_tokens = (
-        output_tokens
-        if output_tokens != 0
-        else parse_token_count(usage_data.get("output_tokens", 0))
-    )
-    input_tokens = (
-        input_tokens
-        if input_tokens != 0
-        else parse_token_count(response_data.get("usage", {}).get("input_tokens", 0))
-    )
-    output_tokens = (
-        output_tokens
-        if output_tokens != 0
-        else parse_token_count(response_data.get("usage", {}).get("output_tokens", 0))
-    )
-
-    usd_cost = 0.0
-    input_usd = 0.0
-    output_usd = 0.0
-
-    if "cost_details" in usage_data:
-        usd_cost = float(
-            usage_data["cost_details"].get("upstream_inference_cost", 0) or 0
-        )
-        input_usd = float(
-            usage_data["cost_details"].get("upstream_inference_prompt_cost", 0) or 0
-        )
-        output_usd = float(
-            usage_data["cost_details"].get("upstream_inference_completions_cost", 0)
-            or 0
-        )
-
-    # Fallback to cost field if upstream_inference_cost is 0
-    if usd_cost == 0 and "cost" in usage_data:
-        try:
-            usd_cost = float(usage_data.get("cost", 0) or 0)
-        except Exception:
-            pass
-
-    MSATS_PER_1K_INPUT_TOKENS: float = (
-        float(settings.fixed_per_1k_input_tokens) * 1000.0
-    )
-    MSATS_PER_1K_OUTPUT_TOKENS: float = (
-        float(settings.fixed_per_1k_output_tokens) * 1000.0
+    # Extract cache tokens (handles OpenAI vs Anthropic formats)
+    cache_read_tokens, cache_creation_tokens, input_tokens = _extract_cache_tokens(
+        usage_data, input_tokens
     )
 
+    # Try USD cost first
+    usd_cost = _resolve_usd_cost(usage_data, response_data)
     if usd_cost > 0:
-        try:
-            sats_per_usd = 1.0 / sats_usd_price()
-            cost_in_sats = usd_cost * sats_per_usd
-            cost_in_msats = math.ceil(cost_in_sats * 1000)
-
-            input_msats = 0
-            output_msats = 0
-
-            if input_usd > 0 or output_usd > 0:
-                input_msats = int((input_usd * sats_per_usd) * 1000)
-                output_msats = int((output_usd * sats_per_usd) * 1000)
-            else:
-                total_tokens = input_tokens + output_tokens
-                if total_tokens > 0:
-                    input_ratio = input_tokens / total_tokens
-                    input_msats = int(cost_in_msats * input_ratio)
-                    output_msats = cost_in_msats - input_msats
-                else:
-                    output_msats = cost_in_msats
-
-            logger.info(
-                "Using cost from usage data/details",
+        if input_tokens == 0 and output_tokens == 0:
+            logger.warning(
+                "Upstream reported a USD cost but no token counts — "
+                "billing the USD-derived cost while the dashboard will "
+                "show this request as `(0+0)` tokens. Check that the "
+                "upstream actually emits `usage.input_tokens` and "
+                "`usage.output_tokens` (OpenAI-compat streams require "
+                "`stream_options.include_usage=true`).",
                 extra={
-                    "usd_cost": usd_cost,
-                    "cost_in_sats": cost_in_sats,
-                    "cost_in_msats": cost_in_msats,
                     "model": response_data.get("model", "unknown"),
+                    "usd_cost": usd_cost,
+                    "usage_keys": sorted(usage_data.keys())
+                    if isinstance(usage_data, dict)
+                    else None,
                 },
             )
-
-            return CostData(
-                base_msats=0,
-                input_msats=input_msats,
-                output_msats=output_msats,
-                total_msats=cost_in_msats,
-                total_usd=usd_cost,
-                input_tokens=input_tokens,
-                output_tokens=output_tokens,
+        try:
+            input_usd = _coerce_usd(
+                usage_data.get("cost_details", {}).get("input_cost", 0)
+            )
+            output_usd = _coerce_usd(
+                usage_data.get("cost_details", {}).get("output_cost", 0)
+            )
+            return _calculate_from_usd_cost(
+                usd_cost,
+                input_usd,
+                output_usd,
+                input_tokens,
+                cache_read_tokens,
+                cache_creation_tokens,
+                output_tokens,
+                response_data,
             )
         except Exception as e:
             logger.warning(
@@ -187,62 +140,35 @@ async def calculate_cost(  # todo: can be sync
                     "model": response_data.get("model", "unknown"),
                 },
             )
-            # Fall through to token-based calculation
 
-    if not settings.fixed_pricing:
-        response_model = response_data.get("model", "")
-        logger.debug(
-            "Using model-based pricing",
-            extra={"model": response_model},
-        )
+    # Fall back to token-based pricing
+    try:
+        pricing_rates = _get_pricing_rates(response_data)
+    except ValueError as e:
+        return CostDataError(message=str(e), code="pricing_error")
 
-        from ..proxy import get_model_instance
+    if pricing_rates is None:
+        input_rate = float(settings.fixed_per_1k_input_tokens) * 1000.0
+        output_rate = float(settings.fixed_per_1k_output_tokens) * 1000.0
+        cache_read_rate = input_rate
+        cache_creation_rate = input_rate
+    else:
+        input_rate, output_rate, cache_read_rate, cache_creation_rate = pricing_rates
 
-        model_obj = get_model_instance(response_model)
-
-        if not model_obj:
-            logger.error(
-                "Invalid model in response",
-                extra={"response_model": response_model},
-            )
-            return CostDataError(
-                message=f"Invalid model in response: {response_model}",
-                code="model_not_found",
-            )
-
-        if not model_obj.sats_pricing:
-            logger.error(
-                "Model pricing not defined",
-                extra={"model": response_model, "model_id": response_model},
-            )
-            return CostDataError(
-                message="Model pricing not defined", code="pricing_not_found"
-            )
-
-        try:
-            mspp = float(model_obj.sats_pricing.prompt)
-            mspc = float(model_obj.sats_pricing.completion)
-        except Exception:
-            return CostDataError(message="Invalid pricing data", code="pricing_invalid")
-
-        MSATS_PER_1K_INPUT_TOKENS = mspp * 1_000_000.0
-        MSATS_PER_1K_OUTPUT_TOKENS = mspc * 1_000_000.0
-
-        logger.info(
-            "Applied model-specific pricing",
-            extra={
-                "model": response_model,
-                "input_price_msats_per_1k": MSATS_PER_1K_INPUT_TOKENS,
-                "output_price_msats_per_1k": MSATS_PER_1K_OUTPUT_TOKENS,
-            },
-        )
-
-    if not (MSATS_PER_1K_OUTPUT_TOKENS and MSATS_PER_1K_INPUT_TOKENS):
+    if not (input_rate and output_rate):
         logger.warning(
-            "No token pricing configured, using base cost",
+            "No token pricing configured — billing at flat MaxCostData. "
+            "Token counts %s in the upstream response but cannot be "
+            "priced; the request will appear in dashboards with the "
+            "raw counts and a fixed max-cost charge.",
+            "are present"
+            if (input_tokens > 0 or output_tokens > 0)
+            else "are zero",
             extra={
                 "base_cost_msats": max_cost,
                 "model": response_data.get("model", "unknown"),
+                "input_tokens": input_tokens,
+                "output_tokens": output_tokens,
             },
         )
         return MaxCostData(
@@ -252,12 +178,243 @@ async def calculate_cost(  # todo: can be sync
             total_msats=max_cost,
             input_tokens=input_tokens,
             output_tokens=output_tokens,
+            cache_read_input_tokens=cache_read_tokens,
+            cache_creation_input_tokens=cache_creation_tokens,
+            cache_read_msats=0,
+            cache_creation_msats=0,
         )
 
-    calc_input_msats = round(input_tokens / 1000 * MSATS_PER_1K_INPUT_TOKENS, 3)
+    return _calculate_from_tokens(
+        input_tokens,
+        output_tokens,
+        cache_read_tokens,
+        cache_creation_tokens,
+        input_rate,
+        output_rate,
+        cache_read_rate,
+        cache_creation_rate,
+        response_data,
+    )
 
-    calc_output_msats = round(output_tokens / 1000 * MSATS_PER_1K_OUTPUT_TOKENS, 3)
-    token_based_cost = math.ceil(calc_input_msats + calc_output_msats)
+
+# ============================================================================
+# Helper Functions (ordered by call sequence in calculate_cost)
+# ============================================================================
+
+
+def parse_token_count(value: object) -> int:
+    """Parse a token count from various formats (int, float, str, bool)."""
+    if isinstance(value, bool):
+        return 0
+    if isinstance(value, int):
+        return max(0, value)
+    if isinstance(value, float):
+        return max(0, int(value))
+    if isinstance(value, str):
+        try:
+            return max(0, int(float(value)))
+        except ValueError:
+            return 0
+    return 0
+
+
+def _coerce_usd(value: object) -> float:
+    """Coerce a value to USD float, handling various formats safely."""
+    if value is None or isinstance(value, bool):
+        return 0.0
+    if not isinstance(value, (int, float, str)):
+        return 0.0
+    try:
+        return max(0.0, float(value))
+    except (TypeError, ValueError):
+        return 0.0
+
+
+def _extract_token_pair(
+    usage_data: dict, standard_field: str, alt_field: str
+) -> int:
+    """Extract token count trying two field names in order."""
+    value = parse_token_count(usage_data.get(standard_field, 0))
+    if value > 0:
+        return value
+    return parse_token_count(usage_data.get(alt_field, 0))
+
+
+def _extract_cache_tokens(usage_data: dict, input_tokens: int) -> tuple[int, int, int]:
+    """Extract cache tokens, handling OpenAI vs Anthropic formats.
+
+    Returns: (cache_read_tokens, cache_creation_tokens, adjusted_input_tokens)
+    """
+    cache_read = parse_token_count(usage_data.get("cache_read_input_tokens", 0))
+    cache_creation = parse_token_count(
+        usage_data.get("cache_creation_input_tokens", 0)
+    )
+
+    # OpenAI: cache is included in input_tokens, subtract it
+    prompt_details = usage_data.get("prompt_tokens_details")
+    if isinstance(prompt_details, dict) and not cache_read:
+        openai_cached = parse_token_count(prompt_details.get("cached_tokens", 0))
+        if openai_cached:
+            cache_read = openai_cached
+            input_tokens = max(0, input_tokens - cache_read)
+
+    return cache_read, cache_creation, input_tokens
+
+
+def _resolve_usd_cost(usage_data: dict, response_data: dict) -> float:
+    """Resolve USD cost with clear priority order.
+
+    Priority: cost_details.total_cost → total_cost → cost (in both usage and response).
+    """
+    cost_details = usage_data.get("cost_details")
+    if isinstance(cost_details, dict):
+        cost = _coerce_usd(cost_details.get("total_cost"))
+        if cost > 0:
+            return cost
+
+    for source in [usage_data, response_data]:
+        if not isinstance(source, dict):
+            continue
+        for field in ("total_cost", "cost"):
+            cost = _coerce_usd(source.get(field))
+            if cost > 0:
+                return cost
+
+    return 0.0
+
+
+def _get_pricing_rates(
+    response_data: dict,
+) -> tuple[float, float, float, float] | None:
+    """Get model-based pricing rates or None if using fixed pricing.
+
+    Returns: (input_rate, output_rate, cache_read_rate, cache_write_rate)
+    """
+    if settings.fixed_pricing:
+        return None
+
+    from ..proxy import get_model_instance
+
+    response_model = response_data.get("model", "")
+    model_obj = get_model_instance(response_model)
+
+    if not model_obj:
+        logger.error("Invalid model in response", extra={"response_model": response_model})
+        raise ValueError(f"Invalid model: {response_model}")
+
+    if not model_obj.sats_pricing:
+        logger.error(
+            "Model pricing not defined",
+            extra={"model": response_model, "model_id": response_model},
+        )
+        raise ValueError("Model pricing not defined")
+
+    try:
+        mspp = float(model_obj.sats_pricing.prompt)
+        mspc = float(model_obj.sats_pricing.completion)
+        mscr = float(model_obj.sats_pricing.input_cache_read or 0)
+        mscw = float(model_obj.sats_pricing.input_cache_write or 0)
+
+        mspp_1k = mspp * 1_000_000.0
+        mspc_1k = mspc * 1_000_000.0
+        mscr_1k = mscr * 1_000_000.0 if mscr > 0 else mspp_1k
+        mscw_1k = mscw * 1_000_000.0 if mscw > 0 else mspp_1k
+
+        logger.info(
+            "Applied model-specific pricing",
+            extra={
+                "model": response_model,
+                "input_price_msats_per_1k": mspp_1k,
+                "output_price_msats_per_1k": mspc_1k,
+                "cache_read_price_msats_per_1k": mscr_1k,
+                "cache_write_price_msats_per_1k": mscw_1k,
+            },
+        )
+        return mspp_1k, mspc_1k, mscr_1k, mscw_1k
+    except Exception as e:
+        logger.error("Invalid pricing data", extra={"error": str(e)})
+        raise ValueError("Invalid pricing data") from e
+
+
+def _calculate_from_usd_cost(
+    usd_cost: float,
+    input_usd: float,
+    output_usd: float,
+    input_tokens: int,
+    cache_read_tokens: int,
+    cache_creation_tokens: int,
+    output_tokens: int,
+    response_data: dict,
+) -> CostData:
+    """Calculate cost from USD figures, deriving input/output split from tokens."""
+    sats_per_usd = 1.0 / sats_usd_price()
+    cost_in_sats = usd_cost * sats_per_usd
+    cost_in_msats = math.ceil(cost_in_sats * 1000)
+
+    if input_usd > 0 or output_usd > 0:
+        input_msats = int((input_usd * sats_per_usd) * 1000)
+        output_msats = int((output_usd * sats_per_usd) * 1000)
+    else:
+        effective_input_tokens = (
+            input_tokens + cache_read_tokens + cache_creation_tokens
+        )
+        total_tokens = effective_input_tokens + output_tokens
+        input_msats = (
+            int(cost_in_msats * effective_input_tokens / total_tokens)
+            if total_tokens > 0
+            else 0
+        )
+        output_msats = cost_in_msats - input_msats
+
+    logger.info(
+        "Using cost from usage data/details",
+        extra={
+            "usd_cost": usd_cost,
+            "cost_in_sats": cost_in_sats,
+            "cost_in_msats": cost_in_msats,
+            "model": response_data.get("model", "unknown"),
+        },
+    )
+
+    return CostData(
+        base_msats=0,
+        input_msats=input_msats,
+        output_msats=output_msats,
+        total_msats=cost_in_msats,
+        total_usd=usd_cost,
+        input_tokens=input_tokens,
+        output_tokens=output_tokens,
+        cache_read_input_tokens=cache_read_tokens,
+        cache_creation_input_tokens=cache_creation_tokens,
+        cache_read_msats=0,
+        cache_creation_msats=0,
+    )
+
+
+def _calculate_from_tokens(
+    input_tokens: int,
+    output_tokens: int,
+    cache_read_tokens: int,
+    cache_creation_tokens: int,
+    input_rate: float,
+    output_rate: float,
+    cache_read_rate: float,
+    cache_creation_rate: float,
+    response_data: dict,
+) -> CostData:
+    """Calculate cost from token counts using pricing rates."""
+    calc_input_msats = round(input_tokens / 1000 * input_rate, 3)
+    calc_output_msats = round(output_tokens / 1000 * output_rate, 3)
+    calc_cache_read_msats = round(cache_read_tokens / 1000 * cache_read_rate, 3)
+    calc_cache_write_msats = round(
+        cache_creation_tokens / 1000 * cache_creation_rate, 3
+    )
+    token_based_cost = math.ceil(
+        calc_input_msats
+        + calc_output_msats
+        + calc_cache_read_msats
+        + calc_cache_write_msats
+    )
     total_usd = (token_based_cost / 1000.0) * sats_usd_price()
 
     logger.info(
@@ -265,8 +422,12 @@ async def calculate_cost(  # todo: can be sync
         extra={
             "input_tokens": input_tokens,
             "output_tokens": output_tokens,
+            "cache_read_input_tokens": cache_read_tokens,
+            "cache_creation_input_tokens": cache_creation_tokens,
             "input_cost_msats": calc_input_msats,
             "output_cost_msats": calc_output_msats,
+            "cache_read_cost_msats": calc_cache_read_msats,
+            "cache_creation_cost_msats": calc_cache_write_msats,
             "total_cost_msats": token_based_cost,
             "total_usd": total_usd,
             "model": response_data.get("model", "unknown"),
@@ -281,4 +442,8 @@ async def calculate_cost(  # todo: can be sync
         total_usd=total_usd,
         input_tokens=input_tokens,
         output_tokens=output_tokens,
+        cache_read_input_tokens=cache_read_tokens,
+        cache_creation_input_tokens=cache_creation_tokens,
+        cache_read_msats=int(calc_cache_read_msats),
+        cache_creation_msats=int(calc_cache_write_msats),
     )

routstr/payment/models.py

@@ -4,6 +4,7 @@ import random
 
 import httpx
 from fastapi import APIRouter, Depends
+from pydantic import BaseModel as V2BaseModel
 from pydantic.v1 import BaseModel
 from sqlmodel.ext.asyncio.session import AsyncSession
 
@@ -350,6 +351,9 @@ async def _update_sats_pricing_once() -> None:
     from ..proxy import get_upstreams, refresh_model_maps
 
     upstreams = get_upstreams()
+    if not upstreams:
+        return
+
     sats_to_usd = sats_usd_price()
 
     updated_count = 0
@@ -359,11 +363,14 @@ async def _update_sats_pricing_once() -> None:
             for m in upstream.get_cached_models()
         ]
         upstream._models_cache = updated_models
-        upstream._models_by_id = {m.id: m for m in updated_models}
+        upstream._models_by_id = {m.forwarded_model_id or m.id: m for m in updated_models}
         updated_count += len(updated_models)
 
     if updated_count > 0:
-        logger.info("Updated sats pricing", extra={"models_updated": updated_count})
+        logger.info(
+            f"Updated sats pricing for {updated_count} models",
+            extra={"models_updated": updated_count},
+        )
         await refresh_model_maps()
 
 
@@ -405,7 +412,7 @@ async def update_sats_pricing() -> None:
             logger.error(f"Error updating sats pricing: {e}")
 
 
-class ModelTestRequest(BaseModel):
+class ModelTestRequest(V2BaseModel):
     model_id: str
     endpoint_type: str
     request_data: dict

routstr/upstream/anthropic.py

@@ -13,6 +13,8 @@ class AnthropicUpstreamProvider(BaseUpstreamProvider):
     provider_type = "anthropic"
     default_base_url = "https://api.anthropic.com/v1"
     platform_url = "https://console.anthropic.com/settings/keys"
+    supports_anthropic_messages = True
+    litellm_provider_prefix = "anthropic/"
 
     def __init__(self, api_key: str, provider_fee: float = 1.01):
         super().__init__(

routstr/upstream/azure.py

@@ -13,6 +13,7 @@ class AzureUpstreamProvider(BaseUpstreamProvider):
     provider_type = "azure"
     default_base_url = None
     platform_url = "https://portal.azure.com/"
+    litellm_provider_prefix = "azure/"
 
     def __init__(
         self,

routstr/upstream/base.py

@@ -6,13 +6,13 @@ import json
 import re
 import traceback
 import uuid
-from collections.abc import AsyncGenerator
-from typing import Mapping
+from collections.abc import AsyncGenerator, AsyncIterator
+from typing import Any, Mapping, cast
 
 import httpx
 from fastapi import BackgroundTasks, HTTPException, Request
 from fastapi.responses import Response, StreamingResponse
-from pydantic import BaseModel
+from pydantic.v1 import BaseModel
 from sqlmodel import select
 
 from ..auth import adjust_payment_for_tokens
@@ -41,6 +41,8 @@ from ..payment.models import (
 )
 from ..payment.price import sats_usd_price
 from ..wallet import recieve_token, send_token
+from . import messages_dispatch
+from .litellm_routing import detect_litellm_prefix
 
 logger = get_logger(__name__)
 
@@ -63,6 +65,12 @@ class BaseUpstreamProvider:
     default_base_url: str | None = None
     platform_url: str | None = None
 
+    supports_anthropic_messages: bool = False
+    # When None, the prefix is detected from `base_url` at dispatch time
+    # (see `get_litellm_provider_prefix`). Subclasses set this to lock the
+    # provider regardless of URL.
+    litellm_provider_prefix: str | None = None
+
     base_url: str
     api_key: str
     provider_fee: float = 1.05
@@ -83,6 +91,19 @@ class BaseUpstreamProvider:
         self._models_cache = []
         self._models_by_id = {}
 
+    def get_litellm_provider_prefix(self) -> str:
+        """Resolve the litellm provider prefix for this provider instance.
+
+        1. If the subclass pinned `litellm_provider_prefix`, use it.
+        2. Otherwise infer from `base_url` (e.g. ``api.fireworks.ai`` →
+           ``fireworks_ai/``) so custom/generic rows reach the correct
+           litellm backend instead of falling back to ``openai/``.
+        3. Default ``openai/`` for unknown OpenAI-compatible servers.
+        """
+        if self.__class__.litellm_provider_prefix:
+            return self.__class__.litellm_provider_prefix
+        return detect_litellm_prefix(self.base_url)
+
     @classmethod
     def from_db_row(
         cls, provider_row: "UpstreamProviderRow"
@@ -119,6 +140,51 @@ class BaseUpstreamProvider:
             "can_show_balance": False,
         }
 
+    @staticmethod
+    def _fold_cache_into_input_tokens(usage: object) -> None:
+        """Fold cache token counts into ``input_tokens`` / ``prompt_tokens``.
+
+        Cost calculation has already used the per-bucket counts to bill the
+        request correctly; what the client sees in the visible token total
+        should be a single rolled-up prompt count *including* the cache
+        portion. The standalone ``cache_read_input_tokens`` /
+        ``cache_creation_input_tokens`` fields are left in place for clients
+        that want the breakdown.
+
+        For Anthropic-shaped responses (``input_tokens`` present), the cache
+        fields are forced to ``0`` when the upstream omitted them, so the
+        client always sees a consistent shape.
+        """
+        if not isinstance(usage, dict):
+            return
+
+        # Normalise missing cache fields to 0 on Anthropic-shaped usage so
+        # downstream consumers can rely on them being present.
+        if "input_tokens" in usage:
+            usage.setdefault("cache_read_input_tokens", 0)
+            usage.setdefault("cache_creation_input_tokens", 0)
+
+        try:
+            cache_read = int(usage.get("cache_read_input_tokens") or 0)
+            cache_creation = int(usage.get("cache_creation_input_tokens") or 0)
+        except (TypeError, ValueError):
+            return
+        extra = cache_read + cache_creation
+        if extra <= 0:
+            return
+        if "input_tokens" in usage:
+            try:
+                usage["input_tokens"] = int(usage.get("input_tokens") or 0) + extra
+            except (TypeError, ValueError):
+                pass
+        if "prompt_tokens" in usage:
+            try:
+                usage["prompt_tokens"] = (
+                    int(usage.get("prompt_tokens") or 0) + extra
+                )
+            except (TypeError, ValueError):
+                pass
+
     def inject_cost_metadata(
         self,
         response_json: dict,
@@ -142,6 +208,7 @@ class BaseUpstreamProvider:
             response_json["usage"]["cost"] = total_usd
             response_json["usage"]["cost_sats"] = sats_cost
             response_json["usage"]["remaining_balance_msats"] = key.balance
+            self._fold_cache_into_input_tokens(response_json["usage"])
 
         # Inject into Anthropic nested usage block if present
         if (
@@ -150,6 +217,7 @@ class BaseUpstreamProvider:
             and "usage" in response_json["message"]
         ):
             response_json["message"]["usage"]["sats_cost"] = sats_cost
+            self._fold_cache_into_input_tokens(response_json["message"]["usage"])
 
         # Unified Routstr metadata
         response_json["metadata"] = response_json.get("metadata", {})
@@ -330,7 +398,10 @@ class BaseUpstreamProvider:
     ) -> bytes | None:
         """Transform request body for provider-specific requirements.
 
-        Automatically transforms model names in the request body.
+        Automatically transforms model names and, for streaming chat
+        completions, opts the upstream into emitting per-chunk ``usage``
+        so cost tracking can read real token counts instead of falling
+        back to ``MaxCostData``.
 
         Args:
             body: Original request body bytes
@@ -343,9 +414,25 @@ class BaseUpstreamProvider:
 
         try:
             data = json.loads(body)
-            if isinstance(data, dict) and "model" in data:
-                original_model = model_obj.id
-                transformed_model = self.transform_model_name(original_model)
+        except Exception as e:
+            logger.debug(
+                "Could not parse request body for transformation",
+                extra={
+                    "error": str(e),
+                    "provider": self.provider_type or self.base_url,
+                },
+            )
+            return body
+
+        if not isinstance(data, dict):
+            return body
+
+        changed = False
+
+        if "model" in data:
+            original_model = model_obj.id
+            transformed_model = self.transform_model_name(original_model)
+            if data["model"] != transformed_model:
                 data["model"] = transformed_model
                 logger.debug(
                     "Transformed model name in request",
@@ -355,16 +442,28 @@ class BaseUpstreamProvider:
                         "provider": self.provider_type or self.base_url,
                     },
                 )
-                return json.dumps(data).encode()
-        except Exception as e:
-            logger.debug(
-                "Could not transform request body",
-                extra={
-                    "error": str(e),
-                    "provider": self.provider_type or self.base_url,
-                },
-            )
+                changed = True
 
+        # OpenAI-compatible streaming responses omit ``usage`` unless the
+        # request sets ``stream_options.include_usage = true``. Without it
+        # we can't reconcile token counts at end of stream and the
+        # request gets billed at max-cost with zero tokens. Discriminate
+        # chat-completions-shaped requests by the ``messages`` field so we
+        # don't poke unrelated endpoints.
+        if (
+            data.get("stream") is True
+            and "messages" in data
+            and isinstance(data.get("messages"), list)
+        ):
+            existing = data.get("stream_options")
+            merged = dict(existing) if isinstance(existing, dict) else {}
+            if merged.get("include_usage") is not True:
+                merged["include_usage"] = True
+                data["stream_options"] = merged
+                changed = True
+
+        if changed:
+            return json.dumps(data).encode()
         return body
 
     def _extract_upstream_error_message(
@@ -774,6 +873,7 @@ class BaseUpstreamProvider:
                 response_json["usage"]["remaining_balance_msats"] = (
                     remaining_balance_msats
                 )
+                self._fold_cache_into_input_tokens(response_json["usage"])
 
             # Keep detailed cost
             response_json["metadata"] = response_json.get("metadata", {})
@@ -1148,6 +1248,7 @@ class BaseUpstreamProvider:
                 response_json["usage"]["remaining_balance_msats"] = (
                     remaining_balance_msats
                 )
+                self._fold_cache_into_input_tokens(response_json["usage"])
 
             # Keep detailed cost
             response_json["metadata"] = response_json.get("metadata", {})
@@ -1275,6 +1376,42 @@ class BaseUpstreamProvider:
             last_model_seen: str | None = None
             input_tokens: int = 0
             output_tokens: int = 0
+            cache_read_input_tokens: int = 0
+            cache_creation_input_tokens: int = 0
+            total_cost: float = 0.0
+            input_cost: float = 0.0
+            output_cost: float = 0.0
+
+            def _coerce_usd(value: object) -> float:
+                if value is None or isinstance(value, bool):
+                    return 0.0
+                if not isinstance(value, (int, float, str)):
+                    return 0.0
+                try:
+                    return max(0.0, float(value))
+                except (TypeError, ValueError):
+                    return 0.0
+
+            def _absorb_usd(usage_or_root: dict) -> None:
+                nonlocal total_cost, input_cost, output_cost
+                cd = usage_or_root.get("cost_details")
+                if isinstance(cd, dict):
+                    total_cost = max(
+                        total_cost,
+                        _coerce_usd(cd.get("total_cost")),
+                    )
+                    input_cost = max(
+                        input_cost,
+                        _coerce_usd(cd.get("input_cost")),
+                    )
+                    output_cost = max(
+                        output_cost,
+                        _coerce_usd(cd.get("output_cost")),
+                    )
+                for field in ("total_cost", "cost"):
+                    total_cost = max(
+                        total_cost, _coerce_usd(usage_or_root.get(field))
+                    )
 
             async def finalize_without_usage() -> bytes | None:
                 nonlocal usage_finalized
@@ -1334,12 +1471,63 @@ class BaseUpstreamProvider:
                                             output_tokens += usage.get(
                                                 "output_tokens", 0
                                             )
+                                            # Anthropic's `message_start.usage`
+                                            # carries the cumulative cache
+                                            # snapshot for the prompt — pick
+                                            # the max() so subsequent
+                                            # `message_delta.usage` events
+                                            # (which only restate the same
+                                            # numbers) don't double-count.
+                                            cache_read_input_tokens = max(
+                                                cache_read_input_tokens,
+                                                int(
+                                                    usage.get(
+                                                        "cache_read_input_tokens", 0
+                                                    )
+                                                    or 0
+                                                ),
+                                            )
+                                            cache_creation_input_tokens = max(
+                                                cache_creation_input_tokens,
+                                                int(
+                                                    usage.get(
+                                                        "cache_creation_input_tokens",
+                                                        0,
+                                                    )
+                                                    or 0
+                                                ),
+                                            )
+                                            _absorb_usd(usage)
 
                                         if usage := data.get("usage"):
                                             input_tokens += usage.get("input_tokens", 0)
                                             output_tokens += usage.get(
                                                 "output_tokens", 0
                                             )
+                                            cache_read_input_tokens = max(
+                                                cache_read_input_tokens,
+                                                int(
+                                                    usage.get(
+                                                        "cache_read_input_tokens", 0
+                                                    )
+                                                    or 0
+                                                ),
+                                            )
+                                            cache_creation_input_tokens = max(
+                                                cache_creation_input_tokens,
+                                                int(
+                                                    usage.get(
+                                                        "cache_creation_input_tokens",
+                                                        0,
+                                                    )
+                                                    or 0
+                                                ),
+                                            )
+                                            _absorb_usd(usage)
+                                        # Some upstreams attach cost fields at
+                                        # the event root rather than nested
+                                        # under `usage`.
+                                        _absorb_usd(data)
                                 except json.JSONDecodeError:
                                     pass
                             modified_lines.append(line)
@@ -1354,9 +1542,23 @@ class BaseUpstreamProvider:
                 usage_data = {
                     "input_tokens": input_tokens,
                     "output_tokens": output_tokens,
+                    "cache_read_input_tokens": cache_read_input_tokens,
+                    "cache_creation_input_tokens": cache_creation_input_tokens,
                 }
+                messages_dispatch.embed_usd_costs(
+                    usage_data,
+                    total_cost,
+                    input_cost,
+                    output_cost,
+                )
 
-                if input_tokens > 0 or output_tokens > 0:
+                if (
+                    input_tokens > 0
+                    or output_tokens > 0
+                    or cache_read_input_tokens > 0
+                    or cache_creation_input_tokens > 0
+                    or total_cost > 0
+                ):
                     async with create_session() as new_session:
                         fresh_key = await new_session.get(key.__class__, key.hashed_key)
                         if fresh_key:
@@ -1470,6 +1672,466 @@ class BaseUpstreamProvider:
         except Exception:
             raise
 
+    # ------------------------------------------------------------------
+    # Litellm /v1/messages dispatch (thin wrappers)
+    #
+    # The actual translation logic lives in ``messages_dispatch``. These
+    # method shims exist so subclasses and tests can keep the original
+    # provider-bound API.
+    # ------------------------------------------------------------------
+
+    _coerce_litellm_payload = staticmethod(messages_dispatch.coerce_litellm_payload)
+    _parse_sse_blocks = staticmethod(messages_dispatch.parse_sse_blocks)
+    _events_from_chunk = staticmethod(messages_dispatch.events_from_chunk)
+
+    async def _aggregate_anthropic_events_to_message(
+        self, iterator: AsyncIterator[Any]
+    ) -> dict:
+        return await messages_dispatch.aggregate_anthropic_events_to_message(
+            iterator
+        )
+
+    async def _dispatch_anthropic_messages(
+        self,
+        request_body: bytes | None,
+        model_obj: Model,
+        *,
+        log_extra: dict[str, Any] | None = None,
+    ) -> tuple[bool, Any, str | None]:
+        return await messages_dispatch.dispatch_anthropic_messages(
+            request_body=request_body,
+            model_obj=model_obj,
+            base_url=self.base_url,
+            api_key=self.api_key,
+            provider_prefix=self.get_litellm_provider_prefix(),
+            transform_model_name=self.transform_model_name,
+            log_extra=log_extra,
+        )
+
+    async def _forward_messages_via_litellm(
+        self,
+        request_body: bytes | None,
+        key: ApiKey,
+        session: AsyncSession,
+        max_cost_for_model: int,
+        model_obj: Model,
+    ) -> Response | StreamingResponse:
+        """Translate /v1/messages to upstream chat/completions via litellm.
+
+        Used when the upstream provider does not natively serve Anthropic
+        Messages (i.e. supports_anthropic_messages is False). Cost
+        tracking and metadata injection mirror the native messages path.
+        """
+        stream, result, requested_model = await self._dispatch_anthropic_messages(
+            request_body,
+            model_obj,
+            log_extra={"key_hash": key.hashed_key[:8] + "..."},
+        )
+
+        if stream:
+            return self._stream_litellm_messages(
+                cast(AsyncIterator[Any], result),
+                key,
+                max_cost_for_model,
+                requested_model,
+            )
+
+        response_json = messages_dispatch.coerce_litellm_payload(result)
+        if requested_model and "model" in response_json:
+            response_json["model"] = requested_model
+
+        cost_data = await adjust_payment_for_tokens(
+            key, response_json, session, max_cost_for_model
+        )
+        self.inject_cost_metadata(response_json, cost_data, key)
+
+        return Response(
+            content=json.dumps(response_json).encode(),
+            status_code=200,
+            media_type="application/json",
+        )
+
+    async def _forward_x_cashu_messages_via_litellm(
+        self,
+        request_body: bytes,
+        amount: int,
+        unit: str,
+        max_cost_for_model: int,
+        model_obj: Model,
+        mint: str | None = None,
+        payment_token_hash: str | None = None,
+        request_id: str | None = None,
+    ) -> Response | StreamingResponse:
+        """Dispatch /v1/messages via litellm for x-cashu payments.
+
+        Computes cost from upstream usage, refunds the unspent balance via
+        an X-Cashu response header, and returns the Anthropic-shaped body.
+        """
+        stream, result, requested_model = await self._dispatch_anthropic_messages(
+            request_body,
+            model_obj,
+            log_extra={"payment_unit": unit, "payment_amount": amount},
+        )
+
+        if stream:
+            return await self._stream_x_cashu_litellm_messages(
+                cast(AsyncIterator[Any], result),
+                amount,
+                unit,
+                max_cost_for_model,
+                requested_model,
+                mint,
+                payment_token_hash,
+                request_id,
+            )
+
+        response_json = messages_dispatch.coerce_litellm_payload(result)
+        if requested_model and "model" in response_json:
+            response_json["model"] = requested_model
+
+        cost_data = await self.get_x_cashu_cost(response_json, max_cost_for_model)
+
+        if cost_data and "usage" in response_json and isinstance(
+            response_json["usage"], dict
+        ):
+            response_json["usage"]["cost_sats"] = cost_data.total_msats // 1000
+            self._fold_cache_into_input_tokens(response_json["usage"])
+
+        response_headers: dict[str, str] = {}
+        if cost_data:
+            refund_amount = messages_dispatch.compute_refund(
+                amount, unit, cost_data.total_msats
+            )
+            if refund_amount > 0:
+                refund_token = await self.send_refund(
+                    refund_amount,
+                    unit,
+                    mint,
+                    payment_token_hash,
+                    request_id=request_id,
+                )
+                response_headers["X-Cashu"] = refund_token
+                logger.info(
+                    "Refund processed for non-streaming /v1/messages via litellm",
+                    extra={
+                        "refund_amount": refund_amount,
+                        "unit": unit,
+                        "model": response_json.get("model", "unknown"),
+                    },
+                )
+
+        return Response(
+            content=json.dumps(response_json).encode(),
+            status_code=200,
+            headers=response_headers,
+            media_type="application/json",
+        )
+
+    _compute_refund = staticmethod(messages_dispatch.compute_refund)
+
+    def _stream_litellm_messages(
+        self,
+        iterator: AsyncIterator[Any],
+        key: ApiKey,
+        max_cost_for_model: int,
+        requested_model: str | None,
+    ) -> StreamingResponse:
+        """Re-emit a litellm Anthropic-event iterator as live SSE bytes
+        with cost reconciliation appended at end of stream."""
+
+        async def stream_with_cost() -> AsyncGenerator[bytes, None]:
+            usage_finalized = False
+            last_model_seen: str | None = None
+            input_tokens = 0
+            output_tokens = 0
+            cache_read_input_tokens = 0
+            cache_creation_input_tokens = 0
+            total_cost = 0.0
+            input_cost = 0.0
+            output_cost = 0.0
+
+            async def finalize_without_usage() -> bytes | None:
+                nonlocal usage_finalized
+                if usage_finalized:
+                    return None
+                logger.warning(
+                    "Finalizing /v1/messages stream with no usage data — "
+                    "client will be billed at max-cost with zero tokens. "
+                    "Likely cause: upstream omitted `usage` from the SSE "
+                    "stream (check that the request includes "
+                    "`stream_options.include_usage=true` and that the "
+                    "upstream actually emits a final usage chunk).",
+                    extra={
+                        "key_hash": key.hashed_key[:8] + "...",
+                        "model": last_model_seen or "unknown",
+                        "provider": self.provider_type or self.base_url,
+                        "max_cost_msats": max_cost_for_model,
+                    },
+                )
+                async with create_session() as new_session:
+                    fresh_key = await new_session.get(
+                        key.__class__, key.hashed_key
+                    )
+                    if not fresh_key:
+                        usage_finalized = True
+                        return None
+                    try:
+                        fallback: dict = {
+                            "model": last_model_seen or "unknown",
+                            "usage": None,
+                        }
+                        cost_data = await adjust_payment_for_tokens(
+                            fresh_key,
+                            fallback,
+                            new_session,
+                            max_cost_for_model,
+                        )
+                        usage_finalized = True
+                        return (
+                            f"event: cost\ndata: "
+                            f"{json.dumps({'cost': cost_data})}\n\n"
+                        ).encode()
+                    except Exception:
+                        usage_finalized = True
+                        return None
+
+            try:
+                async for annotated in messages_dispatch.stream_annotated_events(
+                    iterator, requested_model
+                ):
+                    if annotated.model:
+                        last_model_seen = annotated.model
+                    # Anthropic SSE reports usage cumulatively across
+                    # message_start + message_delta — take the max snapshot
+                    # rather than summing, otherwise input tokens
+                    # double-count.
+                    input_tokens = max(input_tokens, annotated.input_tokens)
+                    output_tokens = max(output_tokens, annotated.output_tokens)
+                    cache_read_input_tokens = max(
+                        cache_read_input_tokens,
+                        annotated.cache_read_input_tokens,
+                    )
+                    cache_creation_input_tokens = max(
+                        cache_creation_input_tokens,
+                        annotated.cache_creation_input_tokens,
+                    )
+                    total_cost = max(total_cost, annotated.total_cost)
+                    input_cost = max(input_cost, annotated.input_cost)
+                    output_cost = max(output_cost, annotated.output_cost)
+                    yield annotated.sse_bytes
+
+                if (
+                    input_tokens > 0
+                    or output_tokens > 0
+                    or cache_read_input_tokens > 0
+                    or cache_creation_input_tokens > 0
+                    or total_cost > 0
+                ):
+                    async with create_session() as new_session:
+                        fresh_key = await new_session.get(
+                            key.__class__, key.hashed_key
+                        )
+                        if fresh_key:
+                            try:
+                                rebuilt_usage: dict = {
+                                    "input_tokens": input_tokens,
+                                    "output_tokens": output_tokens,
+                                    "cache_read_input_tokens": (
+                                        cache_read_input_tokens
+                                    ),
+                                    "cache_creation_input_tokens": (
+                                        cache_creation_input_tokens
+                                    ),
+                                }
+                                messages_dispatch.embed_usd_costs(
+                                    rebuilt_usage,
+                                    total_cost,
+                                    input_cost,
+                                    output_cost,
+                                )
+                                combined_data: dict = {
+                                    "model": last_model_seen or "unknown",
+                                    "usage": rebuilt_usage,
+                                }
+                                cost_data = await adjust_payment_for_tokens(
+                                    fresh_key,
+                                    combined_data,
+                                    new_session,
+                                    max_cost_for_model,
+                                )
+                                self.inject_cost_metadata(
+                                    combined_data, cost_data, fresh_key
+                                )
+                                usage_finalized = True
+                                yield (
+                                    f"event: cost\ndata: "
+                                    f"{json.dumps({'cost': cost_data})}\n\n"
+                                ).encode()
+                            except Exception:
+                                pass
+
+                if not usage_finalized:
+                    cost_event = await finalize_without_usage()
+                    if cost_event is not None:
+                        yield cost_event
+
+            except Exception:
+                if not usage_finalized:
+                    await finalize_without_usage()
+                raise
+
+        return StreamingResponse(
+            stream_with_cost(),
+            media_type="text/event-stream",
+            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+        )
+
+    async def _stream_x_cashu_litellm_messages(
+        self,
+        iterator: AsyncIterator[Any],
+        amount: int,
+        unit: str,
+        max_cost_for_model: int,
+        requested_model: str | None,
+        mint: str | None,
+        payment_token_hash: str | None,
+        request_id: str | None,
+    ) -> StreamingResponse:
+        """Buffer a litellm stream end-to-end, compute cost, then replay.
+
+        Note this is **not** true streaming — the full event sequence is
+        accumulated into memory before a single byte is sent to the
+        client. The constraint is the ``X-Cashu`` refund token, which must
+        be set as a response *header* and therefore has to be known before
+        the response begins. The bearer-key path
+        (:meth:`_stream_litellm_messages`) avoids this by emitting cost as
+        a trailing ``event: cost`` SSE message; switching x-cashu to the
+        same trailing-event contract would let this path stream live, at
+        the cost of a wire-format change for clients that read ``X-Cashu``
+        from headers today.
+        """
+        buffered: list[bytes] = []
+        last_model_seen: str | None = None
+        input_tokens = 0
+        output_tokens = 0
+        cache_read_input_tokens = 0
+        cache_creation_input_tokens = 0
+        total_cost = 0.0
+        input_cost = 0.0
+        output_cost = 0.0
+
+        async for annotated in messages_dispatch.stream_annotated_events(
+            iterator, requested_model
+        ):
+            if annotated.model:
+                last_model_seen = annotated.model
+            # See _stream_litellm_messages for why this is max() not +=.
+            input_tokens = max(input_tokens, annotated.input_tokens)
+            output_tokens = max(output_tokens, annotated.output_tokens)
+            cache_read_input_tokens = max(
+                cache_read_input_tokens, annotated.cache_read_input_tokens
+            )
+            cache_creation_input_tokens = max(
+                cache_creation_input_tokens,
+                annotated.cache_creation_input_tokens,
+            )
+            total_cost = max(total_cost, annotated.total_cost)
+            input_cost = max(input_cost, annotated.input_cost)
+            output_cost = max(output_cost, annotated.output_cost)
+            buffered.append(annotated.sse_bytes)
+
+        response_headers: dict[str, str] = {
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+        }
+
+        if (
+            input_tokens == 0
+            and output_tokens == 0
+            and cache_read_input_tokens == 0
+            and cache_creation_input_tokens == 0
+            and total_cost == 0
+        ):
+            logger.warning(
+                "x-cashu /v1/messages stream finished with no usage data "
+                "— refund cannot be computed and the client effectively "
+                "pays the full cashu amount. Likely cause: upstream "
+                "omitted `usage` from the SSE stream.",
+                extra={
+                    "model": last_model_seen or "unknown",
+                    "provider": self.provider_type or self.base_url,
+                    "amount": amount,
+                    "unit": unit,
+                },
+            )
+
+        if (
+            input_tokens > 0
+            or output_tokens > 0
+            or cache_read_input_tokens > 0
+            or cache_creation_input_tokens > 0
+            or total_cost > 0
+        ):
+            rebuilt_usage: dict = {
+                "input_tokens": input_tokens,
+                "output_tokens": output_tokens,
+                "cache_read_input_tokens": cache_read_input_tokens,
+                "cache_creation_input_tokens": cache_creation_input_tokens,
+            }
+            messages_dispatch.embed_usd_costs(
+                rebuilt_usage, total_cost, input_cost, output_cost
+            )
+            response_data: dict = {
+                "model": last_model_seen or "unknown",
+                "usage": rebuilt_usage,
+            }
+            try:
+                cost_data = await self.get_x_cashu_cost(
+                    response_data, max_cost_for_model
+                )
+                if cost_data:
+                    refund_amount = messages_dispatch.compute_refund(
+                        amount, unit, cost_data.total_msats
+                    )
+                    if refund_amount > 0:
+                        refund_token = await self.send_refund(
+                            refund_amount,
+                            unit,
+                            mint,
+                            payment_token_hash,
+                            request_id=request_id,
+                        )
+                        response_headers["X-Cashu"] = refund_token
+                        logger.info(
+                            "Refund processed for streaming /v1/messages "
+                            "via litellm",
+                            extra={
+                                "refund_amount": refund_amount,
+                                "unit": unit,
+                                "model": last_model_seen,
+                            },
+                        )
+            except Exception as exc:
+                logger.error(
+                    "Error calculating cost for streaming /v1/messages",
+                    extra={
+                        "error": str(exc),
+                        "error_type": type(exc).__name__,
+                        "amount": amount,
+                        "unit": unit,
+                    },
+                )
+
+        async def replay() -> AsyncGenerator[bytes, None]:
+            for chunk in buffered:
+                yield chunk
+
+        return StreamingResponse(
+            replay(),
+            media_type="text/event-stream",
+            headers=response_headers,
+        )
+
     async def forward_request(
         self,
         request: Request,
@@ -1496,6 +2158,20 @@ class BaseUpstreamProvider:
             Response or StreamingResponse from upstream with cost tracking
         """
         path = self.normalize_request_path(path, model_obj)
+
+        if (
+            path.endswith("messages")
+            and not path.endswith("count_tokens")
+            and not self.supports_anthropic_messages
+        ):
+            return await self._forward_messages_via_litellm(
+                request_body=request_body,
+                key=key,
+                session=session,
+                max_cost_for_model=max_cost_for_model,
+                model_obj=model_obj,
+            )
+
         url = self.build_request_url(path, model_obj)
 
         original_model_id = (
@@ -2679,9 +3355,26 @@ class BaseUpstreamProvider:
         if path.startswith("v1/"):
             path = path.replace("v1/", "")
 
+        request_body = await request.body()
+
+        if (
+            path.endswith("messages")
+            and not path.endswith("count_tokens")
+            and not self.supports_anthropic_messages
+        ):
+            return await self._forward_x_cashu_messages_via_litellm(
+                request_body=request_body,
+                amount=amount,
+                unit=unit,
+                max_cost_for_model=max_cost_for_model,
+                model_obj=model_obj,
+                mint=mint,
+                payment_token_hash=payment_token_hash,
+                request_id=getattr(request.state, "request_id", None),
+            )
+
         url = f"{self.base_url}/{path}"
 
-        request_body = await request.body()
         transformed_body = self.prepare_request_body(request_body, model_obj)
 
         logger.debug(
@@ -3831,7 +4524,7 @@ class BaseUpstreamProvider:
                 except Exception:
                     self._models_cache = models_with_fees
 
-                self._models_by_id = {m.id: m for m in self._models_cache}
+                self._models_by_id = {m.forwarded_model_id or m.id: m for m in self._models_cache}
 
         except Exception as e:
             logger.error(

routstr/upstream/clients/gemini.py

@@ -26,14 +26,14 @@ class GeminiClient(BaseAPIClient):
         max_tokens: int | None = None,
         **kwargs: Any,
     ) -> dict[str, Any]:
-        from openai import NOT_GIVEN
+        from openai import omit
 
         response = await self.client.chat.completions.create(
             model=model,
             messages=messages,  # type: ignore
-            temperature=temperature if temperature is not None else NOT_GIVEN,
-            max_tokens=max_tokens if max_tokens is not None else NOT_GIVEN,
-            top_p=kwargs.get("top_p", NOT_GIVEN),
+            temperature=temperature if temperature is not None else omit,
+            max_tokens=max_tokens if max_tokens is not None else omit,
+            top_p=kwargs.get("top_p", omit),
         )
         return response.model_dump()
 
@@ -45,7 +45,7 @@ class GeminiClient(BaseAPIClient):
         max_tokens: int | None = None,
         **kwargs: Any,
     ) -> AsyncGenerator[dict[str, Any], None]:
-        from openai import NOT_GIVEN
+        from openai import omit
 
         usage_callback = kwargs.get("usage_callback")
         completion_callback = kwargs.get("completion_callback")
@@ -55,9 +55,9 @@ class GeminiClient(BaseAPIClient):
             messages=messages,  # type: ignore
             stream=True,
             stream_options={"include_usage": True},
-            temperature=temperature if temperature is not None else NOT_GIVEN,
-            max_tokens=max_tokens if max_tokens is not None else NOT_GIVEN,
-            top_p=kwargs.get("top_p", NOT_GIVEN),
+            temperature=temperature if temperature is not None else omit,
+            max_tokens=max_tokens if max_tokens is not None else omit,
+            top_p=kwargs.get("top_p", omit),
         )
 
         final_usage = None

routstr/upstream/fireworks.py

@@ -12,6 +12,7 @@ class FireworksUpstreamProvider(BaseUpstreamProvider):
     provider_type = "fireworks"
     default_base_url = "https://api.fireworks.ai/inference/v1"
     platform_url = "https://app.fireworks.ai/settings/users/api-keys"
+    litellm_provider_prefix = "fireworks_ai/"
 
     def __init__(self, api_key: str, provider_fee: float = 1.01):
         super().__init__(

routstr/upstream/gemini.py

@@ -1,17 +1,13 @@
 from __future__ import annotations
 
-import json
-from collections.abc import AsyncGenerator
 from typing import TYPE_CHECKING, Any
 
-from fastapi import Request
-from fastapi.responses import Response, StreamingResponse
-
+from . import gemini_messages
 from .base import BaseUpstreamProvider
 from .clients.gemini import GeminiClient
 
 if TYPE_CHECKING:
-    from ..core.db import ApiKey, AsyncSession, UpstreamProviderRow
+    from ..core.db import UpstreamProviderRow
     from ..payment.models import Model
 
 from ..core.logging import get_logger
@@ -20,9 +16,19 @@ logger = get_logger(__name__)
 
 
 class GeminiUpstreamProvider(BaseUpstreamProvider):
+    """Gemini provider — proxies through Gemini's OpenAI-compat surface.
+
+    The chat-completions, embeddings, and models paths all flow through
+    :meth:`BaseUpstreamProvider.forward_request`; we only override
+    ``get_request_base_url`` to redirect to ``{base}/openai/...`` and
+    ``_dispatch_anthropic_messages`` to inject thought-signatures on the
+    /v1/messages path (see :mod:`gemini_messages` for that rationale).
+    """
+
     provider_type = "gemini"
     default_base_url = "https://generativelanguage.googleapis.com/v1beta"
     platform_url = "https://aistudio.google.com/app/apikey"
+    litellm_provider_prefix = "gemini/"
 
     def __init__(
         self,
@@ -39,7 +45,7 @@ class GeminiUpstreamProvider(BaseUpstreamProvider):
 
     @property
     def client(self) -> GeminiClient:
-        """Get or create the Gemini API client."""
+        """Get or create the Gemini API client (used for the models listing)."""
         if self._client is None:
             self._client = GeminiClient(api_key=self.api_key)
         return self._client
@@ -65,248 +71,66 @@ class GeminiUpstreamProvider(BaseUpstreamProvider):
         }
 
     def transform_model_name(self, model_id: str) -> str:
-        return model_id.removeprefix("gemini/")
+        """Reduce a routstr model id to the bare upstream Gemini name.
 
-    async def forward_request(
+        Gemini's OpenAI-compat surface expects the literal model id
+        (e.g. ``gemini-3.1-flash-lite-preview``) — no ``gemini/`` provider
+        prefix and no ``google/`` vendor sub-prefix. Take the last path
+        segment so we tolerate any of:
+
+            ``gemini-2.0-flash``
+            ``gemini/gemini-2.0-flash``
+            ``gemini/google/gemini-3.1-flash-lite-preview``
+        """
+        return model_id.rsplit("/", 1)[-1]
+
+    @property
+    def compat_base_url(self) -> str:
+        """Gemini's OpenAI-compat surface, regardless of what's stored.
+
+        Stored ``base_url`` may be ``.../v1beta`` (the native Gemini API
+        root) or ``.../v1beta/openai`` (already pointed at the compat
+        surface). Normalize to the latter.
+        """
+        return self.base_url.rstrip("/").removesuffix("/openai") + "/openai"
+
+    def get_request_base_url(
+        self, path: str, model_obj: "Model | None" = None
+    ) -> str:
+        """Route every proxied request to the OpenAI-compat surface.
+
+        Required because the stored ``base_url`` typically points at the
+        native Gemini API (``/v1beta``), but :meth:`forward_request`
+        forwards OpenAI-shaped paths (``/chat/completions``,
+        ``/embeddings``, ``/models``) which only exist under the
+        ``/openai`` subtree.
+        """
+        return self.compat_base_url
+
+    async def _dispatch_anthropic_messages(
         self,
-        request: Request,
-        path: str,
-        headers: dict,
         request_body: bytes | None,
-        key: ApiKey,
-        max_cost_for_model: int,
-        session: AsyncSession,
-        model_obj: Model,
-    ) -> Response | StreamingResponse:
-        # Remove provider prefix from model ID for Gemini API
-        if "/" in model_obj.id:
-            model_obj.id = model_obj.id.split("/", 1)[1]
+        model_obj: "Model",
+        *,
+        log_extra: dict[str, Any] | None = None,
+    ) -> tuple[bool, Any, str | None]:
+        """Dispatch /v1/messages via the gemini-specific httpx path.
 
-        if not path.startswith("chat/completions"):
-            return await super().forward_request(
-                request,
-                path,
-                headers,
-                request_body,
-                key,
-                max_cost_for_model,
-                session,
-                model_obj,
-            )
-
-        if not request_body:
-            return await super().forward_request(
-                request,
-                path,
-                headers,
-                request_body,
-                key,
-                max_cost_for_model,
-                session,
-                model_obj,
-            )
-
-        try:
-            openai_data = json.loads(request_body)
-            messages = openai_data.get("messages", [])
-            temperature = openai_data.get("temperature")
-            max_tokens = openai_data.get("max_tokens")
-            top_p = openai_data.get("top_p")
-            is_streaming = openai_data.get("stream", False)
-
-            logger.info(
-                "Processing Gemini request with client abstraction",
-                extra={
-                    "model": model_obj.id,
-                    "is_streaming": is_streaming,
-                    "message_count": len(messages),
-                    "key_hash": key.hashed_key[:8] + "...",
-                },
-            )
-
-            if is_streaming:
-                final_usage_data: dict | None = None
-
-                def usage_callback(usage_data: dict[str, Any]) -> None:
-                    """Callback to capture usage data during streaming"""
-                    nonlocal final_usage_data
-                    final_usage_data = usage_data
-
-                async def completion_callback(
-                    model: str, usage_data: dict[str, Any] | None
-                ) -> None:
-                    """Callback to handle payment when streaming completes"""
-                    nonlocal final_usage_data
-                    if usage_data:
-                        final_usage_data = usage_data
-
-                    payment_data = {
-                        "model": model,
-                        "usage": final_usage_data,
-                    }
-
-                    from ..auth import adjust_payment_for_tokens
-                    from ..core.db import create_session
-
-                    async with create_session() as new_session:
-                        fresh_key = await new_session.get(key.__class__, key.hashed_key)
-                        if fresh_key:
-                            try:
-                                cost_data = await adjust_payment_for_tokens(
-                                    fresh_key,
-                                    payment_data,
-                                    new_session,
-                                    max_cost_for_model,
-                                )
-
-                                logger.info(
-                                    "Gemini streaming payment finalized",
-                                    extra={
-                                        "cost_data": cost_data,
-                                        "usage_data": final_usage_data,
-                                        "key_hash": key.hashed_key[:8] + "...",
-                                    },
-                                )
-                            except Exception as cost_error:
-                                logger.error(
-                                    "Error finalizing Gemini streaming payment",
-                                    extra={
-                                        "error": str(cost_error),
-                                        "key_hash": key.hashed_key[:8] + "...",
-                                    },
-                                )
-
-                response_generator = self.client.generate_content_stream(
-                    model=model_obj.id,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                    top_p=top_p,
-                    usage_callback=usage_callback,
-                    completion_callback=completion_callback,
-                )
-
-                async def stream_with_cost() -> AsyncGenerator[bytes, None]:
-                    payment_finalized = False
-
-                    async def finalize_payment() -> None:
-                        nonlocal payment_finalized
-                        if payment_finalized:
-                            return
-                        from ..auth import adjust_payment_for_tokens
-                        from ..core.db import create_session
-
-                        async with create_session() as new_session:
-                            fresh_key = await new_session.get(
-                                key.__class__, key.hashed_key
-                            )
-                            if fresh_key:
-                                try:
-                                    await adjust_payment_for_tokens(
-                                        fresh_key,
-                                        {
-                                            "model": model_obj.id,
-                                            "usage": final_usage_data,
-                                        },
-                                        new_session,
-                                        max_cost_for_model,
-                                    )
-                                    payment_finalized = True
-                                except Exception as cost_error:
-                                    logger.error(
-                                        "Error finalizing Gemini streaming payment in fallback",
-                                        extra={
-                                            "error": str(cost_error),
-                                            "key_hash": key.hashed_key[:8] + "...",
-                                        },
-                                    )
-
-                    try:
-                        async for chunk in response_generator:
-                            sse_data = f"data: {json.dumps(chunk)}\n\n"
-                            yield sse_data.encode()
-                    except Exception as e:
-                        logger.error(
-                            "Error in Gemini streaming response",
-                            extra={
-                                "error": str(e),
-                                "error_type": type(e).__name__,
-                                "key_hash": key.hashed_key[:8] + "...",
-                            },
-                        )
-                        raise
-                    finally:
-                        if not payment_finalized:
-                            await finalize_payment()
-
-                return StreamingResponse(
-                    stream_with_cost(),
-                    media_type="text/event-stream",
-                    headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
-                )
-
-            else:
-                openai_format_response = await self.client.generate_content(
-                    model=model_obj.id,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                    top_p=top_p,
-                )
-
-                from ..auth import adjust_payment_for_tokens
-
-                cost_data = await adjust_payment_for_tokens(
-                    key, openai_format_response, session, max_cost_for_model
-                )
-                await session.refresh(key)
-                remaining_balance_msats = key.balance
-                openai_format_response["cost"] = cost_data
-                openai_format_response["cost"]["sats_cost"] = (
-                    cost_data.get("total_msats", 0) // 1000
-                )
-                openai_format_response["cost"]["remaining_balance_msats"] = (
-                    remaining_balance_msats
-                )
-
-                logger.info(
-                    "Gemini non-streaming payment completed",
-                    extra={
-                        "cost_data": cost_data,
-                        "model": model_obj.id,
-                        "key_hash": key.hashed_key[:8] + "...",
-                    },
-                )
-
-                return Response(
-                    content=json.dumps(openai_format_response),
-                    media_type="application/json",
-                    headers={"Cache-Control": "no-cache"},
-                )
-
-        except Exception as e:
-            logger.error(
-                "Error in Gemini forward_request",
-                extra={
-                    "error": str(e),
-                    "error_type": type(e).__name__,
-                    "path": path,
-                    "key_hash": key.hashed_key[:8] + "...",
-                },
-            )
-            return await super().forward_request(
-                request,
-                path,
-                headers,
-                request_body,
-                key,
-                max_cost_for_model,
-                session,
-                model_obj,
-            )
+        See :mod:`routstr.upstream.gemini_messages` for the full rationale
+        (thought-signature injection, why litellm + the openai SDK can't
+        carry the required ``extra_content`` field).
+        """
+        return await gemini_messages.dispatch_gemini_messages(
+            request_body=request_body,
+            model_obj=model_obj,
+            base_url=self.compat_base_url,
+            api_key=self.api_key,
+            transform_model_name=self.transform_model_name,
+            log_extra=log_extra,
+        )
 
     async def _fetch_provider_models(self) -> dict:
-        """Fetch models from Gemini API."""
+        """Fetch models from Gemini API via the OpenAI-compat client."""
         try:
             models_data = await self.client.list_models()
 

routstr/upstream/gemini_messages.py

@@ -0,0 +1,480 @@
+"""Custom /v1/messages dispatcher for Gemini's OpenAI-compat endpoint.
+
+Why this exists
+---------------
+
+Gemini 2.5 / 3 thinking models reject inbound ``functionCall`` parts that
+lack a ``thought_signature`` field once any prior turn in the conversation
+contains a function call. Anthropic-Messages clients (Claude Code etc.)
+have no concept of thought signatures, so multi-turn tool conversations
+fail with::
+
+    Function call is missing a thought_signature in functionCall parts.
+
+Google's published escape hatch (https://ai.google.dev/gemini-api/docs/
+thought-signatures, FAQ #1) is the dummy signature
+``"skip_thought_signature_validator"`` placed at
+``tool_calls[i].extra_content.google.thought_signature`` for every tool
+call in the request. The hatch is documented specifically for
+"transferring a trace from a different model that does not include thought
+signatures" — exactly our case.
+
+Why we can't reach the wire via litellm
+---------------------------------------
+
+``litellm.anthropic.messages.acreate`` flows through the openai SDK, whose
+pydantic ``ChatCompletionMessageToolCall`` model silently drops unknown
+fields like ``extra_content``. Litellm has no openai-compat translator
+that emits ``extra_content.google.thought_signature``. So we bypass both
+litellm and the openai SDK at the transport layer.
+
+Pipeline
+--------
+
+1. Translate Anthropic body → OpenAI body via litellm's
+   ``AnthropicAdapter`` (the same translator
+   ``litellm.anthropic.messages.acreate`` uses internally).
+2. Inject ``extra_content.google.thought_signature`` on every
+   ``tool_calls[]`` entry.
+3. Set ``reasoning_effort="none"`` to disable Gemini's thinking pass.
+4. POST directly to ``{base_url}/chat/completions`` with ``stream=true``
+   via ``httpx`` (preserves arbitrary fields verbatim).
+5. Translate OpenAI streaming chunks → Anthropic SSE events.
+"""
+
+from __future__ import annotations
+
+import json
+import uuid
+from collections.abc import AsyncGenerator, AsyncIterator
+from typing import Any, Callable
+
+import httpx
+
+from ..core import get_logger
+from ..core.exceptions import UpstreamError
+from ..payment.models import Model
+from .messages_dispatch import (
+    ANTHROPIC_ONLY_FIELDS,
+    aggregate_anthropic_events_to_message,
+)
+
+logger = get_logger(__name__)
+
+DUMMY_THOUGHT_SIGNATURE = "skip_thought_signature_validator"
+
+# Mapping: OpenAI finish_reason → Anthropic stop_reason
+_FINISH_TO_STOP = {
+    "stop": "end_turn",
+    "length": "max_tokens",
+    "tool_calls": "tool_use",
+    "function_call": "tool_use",
+    "content_filter": "refusal",
+}
+
+
+def inject_thought_signatures(messages: list[dict]) -> None:
+    """Add ``extra_content.google.thought_signature`` to every tool_call.
+
+    Mutates ``messages`` in place. Idempotent: existing signatures are not
+    overwritten.
+    """
+    for msg in messages:
+        tool_calls = msg.get("tool_calls")
+        if not isinstance(tool_calls, list):
+            continue
+        for tc in tool_calls:
+            if not isinstance(tc, dict):
+                continue
+            extra = tc.get("extra_content")
+            if not isinstance(extra, dict):
+                extra = tc["extra_content"] = {}
+            google_cfg = extra.get("google")
+            if not isinstance(google_cfg, dict):
+                google_cfg = extra["google"] = {}
+            google_cfg.setdefault("thought_signature", DUMMY_THOUGHT_SIGNATURE)
+
+
+def _translate_anthropic_to_openai(body: dict, model: str) -> dict:
+    """Use litellm's translator to convert an Anthropic /messages body to
+    OpenAI /chat/completions kwargs.
+
+    Imported lazily because the litellm internal path is heavy and not
+    needed for any other code path in routstr.
+    """
+    from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (  # noqa: E501
+        AnthropicAdapter,
+    )
+
+    kwargs = {"model": model, **body}
+    translated = AnthropicAdapter().translate_completion_input_params(kwargs)
+    if translated is None:
+        raise UpstreamError(
+            "Failed to translate Anthropic body to OpenAI format",
+            status_code=500,
+        )
+    return dict(translated)
+
+
+def _sse_event(event_type: str, payload: dict) -> bytes:
+    return f"event: {event_type}\ndata: {json.dumps(payload)}\n\n".encode()
+
+
+async def _openai_chunks_to_anthropic_events(
+    line_iter: AsyncIterator[str], requested_model: str | None
+) -> AsyncGenerator[bytes, None]:
+    """Translate an OpenAI chat-completions SSE byte stream into the
+    Anthropic-Messages SSE event sequence.
+
+    Maintains per-chunk state across:
+
+    * one optional text content block (lazy-opened on first text delta)
+    * any number of tool_use blocks indexed by openai's ``delta.tool_calls[].index``
+    * final ``stop_reason`` / ``usage`` carried out via ``message_delta`` /
+      ``message_stop``
+    """
+    msg_id = f"msg_{uuid.uuid4().hex[:24]}"
+    started = False
+    text_block_idx: int | None = None
+    tool_block_indices: dict[int, int] = {}
+    next_block_idx = 0
+    final_finish_reason: str | None = None
+    final_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
+
+    def open_text_block() -> bytes:
+        nonlocal text_block_idx, next_block_idx
+        text_block_idx = next_block_idx
+        next_block_idx += 1
+        return _sse_event(
+            "content_block_start",
+            {
+                "type": "content_block_start",
+                "index": text_block_idx,
+                "content_block": {"type": "text", "text": ""},
+            },
+        )
+
+    def open_tool_block(delta_idx: int, tc: dict) -> bytes:
+        nonlocal next_block_idx
+        block_idx = next_block_idx
+        next_block_idx += 1
+        tool_block_indices[delta_idx] = block_idx
+        fn = tc.get("function") or {}
+        return _sse_event(
+            "content_block_start",
+            {
+                "type": "content_block_start",
+                "index": block_idx,
+                "content_block": {
+                    "type": "tool_use",
+                    "id": tc.get("id") or f"toolu_{uuid.uuid4().hex[:24]}",
+                    "name": fn.get("name") or "",
+                    "input": {},
+                },
+            },
+        )
+
+    def close_block(idx: int) -> bytes:
+        return _sse_event(
+            "content_block_stop",
+            {"type": "content_block_stop", "index": idx},
+        )
+
+    async for raw_line in line_iter:
+        line = raw_line.strip()
+        if not line:
+            continue
+        if not line.startswith("data:"):
+            continue
+        payload = line[5:].lstrip()
+        if not payload or payload == "[DONE]":
+            continue
+        try:
+            chunk = json.loads(payload)
+        except json.JSONDecodeError:
+            continue
+        if not isinstance(chunk, dict):
+            continue
+
+        if not started:
+            started = True
+            yield _sse_event(
+                "message_start",
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": chunk.get("id") or msg_id,
+                        "type": "message",
+                        "role": "assistant",
+                        "model": requested_model or chunk.get("model") or "",
+                        "content": [],
+                        "stop_reason": None,
+                        "stop_sequence": None,
+                        "usage": {
+                            "input_tokens": 0,
+                            "output_tokens": 0,
+                        },
+                    },
+                },
+            )
+
+        usage = chunk.get("usage")
+        if isinstance(usage, dict):
+            in_tok = usage.get("prompt_tokens") or usage.get("input_tokens") or 0
+            out_tok = usage.get("completion_tokens") or usage.get("output_tokens") or 0
+            if in_tok:
+                final_usage["input_tokens"] = int(in_tok)
+            if out_tok:
+                final_usage["output_tokens"] = int(out_tok)
+
+        choices = chunk.get("choices") or []
+        if not choices:
+            continue
+        choice = choices[0] if isinstance(choices[0], dict) else {}
+        delta = choice.get("delta") or {}
+        if not isinstance(delta, dict):
+            delta = {}
+
+        # Text delta
+        text = delta.get("content")
+        if isinstance(text, str) and text:
+            if text_block_idx is None:
+                yield open_text_block()
+            yield _sse_event(
+                "content_block_delta",
+                {
+                    "type": "content_block_delta",
+                    "index": text_block_idx,
+                    "delta": {"type": "text_delta", "text": text},
+                },
+            )
+
+        # Tool call deltas
+        tool_calls_delta = delta.get("tool_calls")
+        if isinstance(tool_calls_delta, list):
+            for tc in tool_calls_delta:
+                if not isinstance(tc, dict):
+                    continue
+                d_idx = int(tc.get("index") or 0)
+                if d_idx not in tool_block_indices:
+                    yield open_tool_block(d_idx, tc)
+                block_idx = tool_block_indices[d_idx]
+                fn = tc.get("function") or {}
+                args = fn.get("arguments")
+                if isinstance(args, str) and args:
+                    yield _sse_event(
+                        "content_block_delta",
+                        {
+                            "type": "content_block_delta",
+                            "index": block_idx,
+                            "delta": {
+                                "type": "input_json_delta",
+                                "partial_json": args,
+                            },
+                        },
+                    )
+
+        finish = choice.get("finish_reason")
+        if finish:
+            final_finish_reason = finish
+
+    # Close any open content blocks
+    if text_block_idx is not None:
+        yield close_block(text_block_idx)
+    for block_idx in tool_block_indices.values():
+        yield close_block(block_idx)
+
+    # message_delta with stop_reason and usage
+    stop_reason = _FINISH_TO_STOP.get(final_finish_reason or "", "end_turn")
+    yield _sse_event(
+        "message_delta",
+        {
+            "type": "message_delta",
+            "delta": {"stop_reason": stop_reason, "stop_sequence": None},
+            "usage": final_usage,
+        },
+    )
+    yield _sse_event("message_stop", {"type": "message_stop"})
+
+
+async def _post_and_stream(
+    base_url: str,
+    api_key: str,
+    payload: dict,
+    log_extra: dict[str, Any] | None,
+) -> tuple[httpx.AsyncClient, httpx.Response]:
+    """POST to upstream chat-completions and return (client, response) for
+    streaming. Caller is responsible for closing both."""
+    url = f"{base_url.rstrip('/')}/chat/completions"
+    client = httpx.AsyncClient(timeout=httpx.Timeout(120.0, read=120.0))
+    try:
+        request = client.build_request(
+            "POST",
+            url,
+            json=payload,
+            headers={
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json",
+                "Accept": "text/event-stream",
+            },
+        )
+        response = await client.send(request, stream=True)
+    except Exception as exc:
+        await client.aclose()
+        logger.error(
+            "Gemini messages dispatch HTTP error",
+            extra={"error": str(exc), "url": url, **(log_extra or {})},
+        )
+        raise UpstreamError(
+            f"Failed to reach Gemini upstream: {exc}", status_code=502
+        ) from exc
+
+    if response.status_code >= 400:
+        try:
+            body_bytes = await response.aread()
+        finally:
+            await response.aclose()
+            await client.aclose()
+        body_text = body_bytes.decode("utf-8", errors="replace")
+        logger.error(
+            "Gemini messages dispatch upstream error",
+            extra={
+                "status_code": response.status_code,
+                "body": body_text[:1000],
+                "url": url,
+                **(log_extra or {}),
+            },
+        )
+        raise UpstreamError(
+            f"Upstream error via gemini compat: {body_text}",
+            status_code=response.status_code,
+        )
+
+    return client, response
+
+
+async def dispatch_gemini_messages(
+    *,
+    request_body: bytes | None,
+    model_obj: Model,
+    base_url: str,
+    api_key: str,
+    transform_model_name: Callable[[str], str],
+    log_extra: dict[str, Any] | None = None,
+) -> tuple[bool, Any, str | None]:
+    """Dispatch a /v1/messages request to Gemini's OpenAI-compat endpoint
+    with thought-signature injection.
+
+    Returns ``(client_stream, result, requested_model)`` where ``result``
+    is either an ``AsyncIterator[bytes]`` of Anthropic-format SSE events
+    (for streaming clients) or an Anthropic Message dict (after the caller
+    aggregates).
+    """
+    if not request_body:
+        raise UpstreamError(
+            "Missing request body for /v1/messages", status_code=400
+        )
+
+    try:
+        body: dict = json.loads(request_body)
+    except json.JSONDecodeError as exc:
+        raise UpstreamError(
+            f"Invalid JSON in /v1/messages body: {exc}", status_code=400
+        ) from exc
+
+    body.pop("model", None)
+    client_stream = bool(body.pop("stream", False))
+
+    # Anthropic-Messages-only fields that don't translate to OpenAI
+    # Chat Completions. litellm's translator passes through unknown
+    # top-level fields verbatim and Gemini's compat surface 400s on
+    # unknown names like ``context_management`` / ``output_config``.
+    dropped: dict[str, Any] = {}
+    for field in ANTHROPIC_ONLY_FIELDS:
+        if field in body:
+            dropped[field] = body.pop(field)
+    if dropped:
+        logger.debug(
+            "Dropped anthropic-only fields before gemini compat dispatch",
+            extra={"dropped_keys": sorted(dropped.keys())},
+        )
+
+    requested_model = (
+        (model_obj.forwarded_model_id or model_obj.id) if model_obj else None
+    )
+    upstream_model = transform_model_name(model_obj.id)
+
+    openai_kwargs = _translate_anthropic_to_openai(body, upstream_model)
+
+    messages = openai_kwargs.get("messages") or []
+    if isinstance(messages, list):
+        inject_thought_signatures(messages)
+
+    # Disable Gemini's thinking pass; the dummy signature already lifts
+    # validation, but skipping thinking entirely avoids degraded model
+    # output and keeps tool-calling deterministic.
+    openai_kwargs.setdefault("reasoning_effort", "none")
+    openai_kwargs["stream"] = True
+    openai_kwargs["model"] = upstream_model
+    # OpenAI-compat backends (including Gemini's) only emit a final
+    # ``usage`` chunk when the request opts in via this flag. Without it
+    # the cost-calculation pipeline can't read real token counts and
+    # falls back to MaxCostData billing.
+    existing_stream_options = openai_kwargs.get("stream_options")
+    merged_stream_options = (
+        dict(existing_stream_options)
+        if isinstance(existing_stream_options, dict)
+        else {}
+    )
+    merged_stream_options.setdefault("include_usage", True)
+    openai_kwargs["stream_options"] = merged_stream_options
+
+    logger.info(
+        "Dispatching /v1/messages via gemini compat (httpx)",
+        extra={
+            "model": upstream_model,
+            "client_stream": client_stream,
+            "messages_with_tool_calls": sum(
+                1 for m in messages if isinstance(m, dict) and m.get("tool_calls")
+            ),
+            **(log_extra or {}),
+        },
+    )
+
+    http_client, response = await _post_and_stream(
+        base_url, api_key, openai_kwargs, log_extra
+    )
+
+    async def line_iter() -> AsyncGenerator[str, None]:
+        try:
+            async for line in response.aiter_lines():
+                yield line
+        finally:
+            await response.aclose()
+            await http_client.aclose()
+
+    anthropic_event_iter = _openai_chunks_to_anthropic_events(
+        line_iter(), requested_model
+    )
+
+    if not client_stream:
+        # Aggregate the Anthropic SSE byte stream into a single Message dict
+        # so the rest of the pipeline (cost calc, metadata injection,
+        # response building) can treat it identically to a non-streaming
+        # litellm response.
+        try:
+            aggregated = await aggregate_anthropic_events_to_message(
+                anthropic_event_iter
+            )
+        except Exception as exc:
+            logger.error(
+                "Failed to aggregate Gemini compat events into message",
+                extra={"error": str(exc), **(log_extra or {})},
+            )
+            raise UpstreamError(
+                f"Failed to aggregate upstream stream: {exc}",
+                status_code=502,
+            ) from exc
+        return client_stream, aggregated, requested_model
+
+    return client_stream, anthropic_event_iter, requested_model

routstr/upstream/groq.py

@@ -12,6 +12,7 @@ class GroqUpstreamProvider(BaseUpstreamProvider):
     provider_type = "groq"
     default_base_url = "https://api.groq.com/openai/v1"
     platform_url = "https://console.groq.com/keys"
+    litellm_provider_prefix = "groq/"
 
     def __init__(self, api_key: str, provider_fee: float = 1.01):
         super().__init__(

routstr/upstream/helpers.py

@@ -197,13 +197,14 @@ async def init_upstreams() -> list[BaseUpstreamProvider]:
         existing_providers = result.all()
 
         if not existing_providers:
-            logger.info(
-                "No upstream providers found in database, seeding from settings"
-            )
             await _seed_providers_from_settings(session, settings)
             await session.commit()
             result = await session.exec(select(UpstreamProviderRow))
             existing_providers = result.all()
+            if existing_providers:
+                logger.info(
+                    f"Seeded {len(existing_providers)} upstream providers from settings"
+                )
 
         async def _init_single_provider(
             provider_row: UpstreamProviderRow,

routstr/upstream/litellm_routing.py

@@ -0,0 +1,162 @@
+"""Map an upstream `base_url` to the correct litellm provider prefix.
+
+Used by `BaseUpstreamProvider.get_litellm_provider_prefix` so that custom /
+generic provider rows (which inherit the base class default) get routed to
+the right litellm backend instead of falling back to `openai/`.
+
+The table is compiled from litellm 1.74's
+`litellm/litellm_core_utils/get_llm_provider_logic.py` (the
+`openai_compatible_endpoints` table) plus the providers documented at
+https://docs.litellm.ai/docs/providers. Substring match is used so that
+URLs with paths, ports, regional subdomains, etc. all resolve correctly.
+
+Order matters: more specific needles must appear before more generic ones
+(e.g. ``openai.azure.com`` before ``api.openai.com``).
+"""
+
+from __future__ import annotations
+
+import os
+from urllib.parse import urlsplit
+
+import litellm
+
+DEFAULT_PREFIX = "openai/"
+
+LITELLM_HOST_PREFIX_MAP: tuple[tuple[str, str], ...] = (
+    # Azure must win over api.openai.com because the host ends with
+    # `openai.azure.com` and we don't want it picked up as plain OpenAI.
+    ("openai.azure.com", "azure/"),
+    # Google
+    ("generativelanguage.googleapis.com", "gemini/"),
+    ("aiplatform.googleapis.com", "vertex_ai/"),
+    # First-class providers with native litellm prefixes
+    ("api.openai.com", "openai/"),
+    ("api.anthropic.com", "anthropic/"),
+    ("api.groq.com", "groq/"),
+    ("api.fireworks.ai", "fireworks_ai/"),
+    ("api.x.ai", "xai/"),
+    ("api.perplexity.ai", "perplexity/"),
+    ("openrouter.ai", "openrouter/"),
+    ("api.deepseek.com", "deepseek/"),
+    ("api.together.xyz", "together_ai/"),
+    ("codestral.mistral.ai", "codestral/"),
+    ("api.mistral.ai", "mistral/"),
+    ("api.cohere.com", "cohere_chat/"),
+    ("api.cohere.ai", "cohere_chat/"),
+    ("api.deepinfra.com", "deepinfra/"),
+    ("api.endpoints.anyscale.com", "anyscale/"),
+    ("api.cerebras.ai", "cerebras/"),
+    ("inference.baseten.co", "baseten/"),
+    ("api.sambanova.ai", "sambanova/"),
+    ("api.ai21.com", "ai21_chat/"),
+    ("api.friendli.ai", "friendliai/"),
+    ("api.galadriel.com", "galadriel/"),
+    ("api.llama.com", "meta_llama/"),
+    ("api.featherless.ai", "featherless_ai/"),
+    ("inference.api.nscale.com", "nscale/"),
+    ("dashscope-intl.aliyuncs.com", "dashscope/"),
+    ("api.moonshot.ai", "moonshot/"),
+    ("api.moonshot.cn", "moonshot/"),
+    ("api.minimax.io", "minimax/"),
+    ("api.minimaxi.com", "minimax/"),
+    ("platform.publicai.co", "publicai/"),
+    ("api.synthetic.new", "synthetic/"),
+    ("api.stima.tech", "apertis/"),
+    ("nano-gpt.com", "nano-gpt/"),
+    ("api.poe.com", "poe/"),
+    ("llm.chutes.ai", "chutes/"),
+    ("api.v0.dev", "v0/"),
+    ("api.lambda.ai", "lambda_ai/"),
+    ("api.hyperbolic.xyz", "hyperbolic/"),
+    ("ai-gateway.vercel.sh", "vercel_ai_gateway/"),
+    ("api.inference.wandb.ai", "wandb/"),
+    ("integrate.api.nvidia.com", "nvidia_nim/"),
+    ("api.studio.nebius.com", "nebius/"),
+    ("api.novita.ai", "novita/"),
+    ("ark.cn-beijing.volces.com", "volcengine/"),
+    ("api.voyageai.com", "voyage/"),
+    ("api.jina.ai", "jina_ai/"),
+    ("api.aimlapi.com", "aiml/"),
+    ("api.snowflakecomputing.com", "snowflake/"),
+    ("databricks.com", "databricks/"),
+    ("huggingface.co", "huggingface/"),
+)
+
+# Substrings that indicate an Ollama deployment regardless of port/scheme.
+OLLAMA_HOST_HINTS: tuple[str, ...] = (
+    "localhost:11434",
+    "127.0.0.1:11434",
+    "ollama",
+)
+
+
+def detect_litellm_prefix(
+    base_url: str | None, default: str = DEFAULT_PREFIX
+) -> str:
+    """Return the litellm provider prefix (`"<provider>/"`) for `base_url`.
+
+    Falls back to `default` when the host doesn't match any known provider.
+    The default is `openai/` because every unmatched OpenAI-compatible
+    server is, by definition, an OpenAI-compatible server.
+    """
+    if not base_url:
+        return default
+
+    parsed = urlsplit(base_url)
+    host = parsed.netloc.lower() or base_url.lower()
+
+    for needle, prefix in LITELLM_HOST_PREFIX_MAP:
+        if needle in host:
+            return prefix
+
+    if any(hint in host for hint in OLLAMA_HOST_HINTS):
+        return "ollama_chat/"
+
+    return default
+
+
+_configured = False
+
+
+def configure_litellm() -> None:
+    """Apply litellm global settings used by the messages-dispatch path.
+
+    Idempotent: safe to call from both app startup and module-level
+    initializers without side effects on the second invocation.
+
+    Settings applied:
+
+    * ``LITELLM_DEBUG=1`` enables litellm's verbose debug logger.
+    * Forces the Anthropic-messages adapter to call OpenAI Chat Completions
+      (POST ``/chat/completions``) instead of the Responses API (POST
+      ``/responses``) for ``openai/``-prefixed providers. OpenAI-compatible
+      upstreams like Google's generativelanguage compat endpoint expose
+      ``/chat/completions`` but not ``/responses``, which would 404. Set
+      ``LITELLM_USE_RESPONSES_API_FOR_ANTHROPIC_MESSAGES=1`` to opt out.
+    * Silently drops Anthropic-Messages-only parameters (``thinking``,
+      ``cache_control``, ``context_management``, ...) when translating to
+      providers that don't accept them, instead of raising
+      ``UnsupportedParamsError``. Set ``LITELLM_STRICT_PARAMS=1`` to opt
+      out.
+    """
+    global _configured
+    if _configured:
+        return
+
+    if os.getenv("LITELLM_DEBUG") == "1":
+        try:
+            litellm._turn_on_debug()  # type: ignore[no-untyped-call]
+        except Exception:
+            pass
+
+    if os.getenv("LITELLM_USE_RESPONSES_API_FOR_ANTHROPIC_MESSAGES") != "1":
+        try:
+            litellm.use_chat_completions_url_for_anthropic_messages = True
+        except Exception:
+            pass
+
+    if os.getenv("LITELLM_STRICT_PARAMS") != "1":
+        litellm.drop_params = True
+
+    _configured = True

routstr/upstream/messages_dispatch.py

@@ -0,0 +1,561 @@
+"""Pure helpers for translating ``/v1/messages`` to upstream chat completions
+via litellm.
+
+This module owns the litellm/Anthropic-Messages translation layer:
+
+* SSE parsing (``parse_sse_blocks``, ``events_from_chunk``)
+* Payload coercion (``coerce_litellm_payload``)
+* Stream aggregation (``aggregate_anthropic_events_to_message``) — drains
+  a streamed Anthropic event sequence into a single Message dict
+* Per-event annotation for streaming (``annotate_event``,
+  ``stream_annotated_events``) — handles the model-rewrite + token-tally
+  bookkeeping shared by the bearer-key and x-cashu streaming paths
+* The dispatch entry point (``dispatch_anthropic_messages``)
+* Refund math (``compute_refund``)
+
+Nothing in here touches ``BaseUpstreamProvider``; the thin instance methods
+on the provider class forward to these functions and only retain logic that
+genuinely needs ``self`` (cost adjustment, metadata injection, refund
+sending).
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import AsyncGenerator, AsyncIterator
+from typing import Any, Callable, NamedTuple, cast
+
+import litellm
+
+from ..core import get_logger
+from ..core.exceptions import UpstreamError
+from ..payment.models import Model
+
+logger = get_logger(__name__)
+
+# Anthropic-Messages-only fields that don't translate to OpenAI
+# Chat Completions. ``litellm.drop_params`` only filters *known*
+# unsupported params; these newer/extension fields get passed through
+# verbatim and the upstream rejects them with a 400. Pop them here so the
+# request reaches the upstream cleanly.
+ANTHROPIC_ONLY_FIELDS: tuple[str, ...] = (
+    "thinking",
+    "cache_control",
+    "context_management",
+    "output_config",
+    "mcp_servers",
+    "service_tier",
+    "anthropic_version",
+    "anthropic_beta",
+)
+
+
+def coerce_litellm_payload(payload: object) -> dict:
+    """Convert a litellm event into a plain dict.
+
+    Non-streaming responses come back as Anthropic-shaped pydantic models
+    or dicts. Streaming may yield raw bytes/str (SSE-encoded); those go
+    through ``events_from_chunk`` instead, not here.
+    """
+    if isinstance(payload, dict):
+        return dict(payload)
+    if hasattr(payload, "model_dump"):
+        return cast(dict, payload.model_dump())
+    raise TypeError(f"Cannot coerce {type(payload).__name__} to dict")
+
+
+def parse_sse_blocks(buffer: bytes) -> tuple[list[dict], bytes]:
+    """Parse complete SSE event blocks out of a byte buffer.
+
+    Returns (events, remaining_buffer). Events are JSON objects parsed from
+    one or more ``data:`` lines per block. Comments, blank lines, and
+    ``[DONE]`` sentinels are ignored. A trailing partial block is preserved
+    in remaining_buffer.
+    """
+    events: list[dict] = []
+    while True:
+        sep = buffer.find(b"\n\n")
+        if sep < 0:
+            sep_rn = buffer.find(b"\r\n\r\n")
+            if sep_rn < 0:
+                break
+            block = buffer[:sep_rn]
+            buffer = buffer[sep_rn + 4 :]
+        else:
+            block = buffer[:sep]
+            buffer = buffer[sep + 2 :]
+
+        data_lines: list[str] = []
+        for raw_line in block.replace(b"\r\n", b"\n").split(b"\n"):
+            line = raw_line.decode("utf-8", errors="replace")
+            if line.startswith(":"):
+                continue
+            if line.startswith("data:"):
+                data_lines.append(line[5:].lstrip())
+        if not data_lines:
+            continue
+        payload = "\n".join(data_lines).strip()
+        if not payload or payload == "[DONE]":
+            continue
+        try:
+            obj = json.loads(payload)
+        except json.JSONDecodeError:
+            continue
+        if isinstance(obj, dict):
+            events.append(obj)
+    return events, buffer
+
+
+def events_from_chunk(
+    chunk: object, sse_buffer: bytes
+) -> tuple[list[dict], bytes]:
+    """Normalize a stream chunk into one or more event dicts.
+
+    ``litellm.anthropic.messages.acreate(stream=True)`` yields raw SSE
+    bytes in practice; some adapters yield strings or typed events. Handle
+    all three.
+    """
+    if isinstance(chunk, (bytes, bytearray)):
+        sse_buffer += bytes(chunk)
+        events, sse_buffer = parse_sse_blocks(sse_buffer)
+        return events, sse_buffer
+    if isinstance(chunk, str):
+        sse_buffer += chunk.encode("utf-8")
+        events, sse_buffer = parse_sse_blocks(sse_buffer)
+        return events, sse_buffer
+    return [coerce_litellm_payload(chunk)], sse_buffer
+
+
+async def aggregate_anthropic_events_to_message(
+    iterator: AsyncIterator[Any],
+) -> dict:
+    """Drain an Anthropic-Messages event iterator into a single Message dict.
+
+    Produces the shape ``litellm.anthropic.messages.acreate(stream=False)``
+    would have returned. Used to transparently stream from upstream while
+    still returning a non-streaming response to the client. Lets us
+    sidestep upstream quirks (e.g. Fireworks rejects ``max_tokens > 4096``
+    unless ``stream=true``) without leaking that into client-visible
+    behavior.
+    """
+    sse_buffer = b""
+    message: dict = {}
+    blocks: list[dict] = []
+    partial_json: dict[int, str] = {}
+    final_stop_reason: str | None = None
+    final_stop_sequence: str | None = None
+    final_usage: dict[str, Any] = {}
+    final_model: str | None = None
+
+    async for chunk in iterator:
+        events, sse_buffer = events_from_chunk(chunk, sse_buffer)
+        for event in events:
+            etype = event.get("type")
+            if etype == "message_start":
+                raw = event.get("message") or {}
+                if isinstance(raw, dict):
+                    message = dict(raw)
+                    existing = message.get("content")
+                    blocks = list(existing) if isinstance(existing, list) else []
+                    usage = message.get("usage")
+                    if isinstance(usage, dict):
+                        final_usage = dict(usage)
+                    if isinstance(message.get("model"), str):
+                        final_model = message["model"]
+            elif etype == "content_block_start":
+                idx = int(event.get("index") or 0)
+                cb = event.get("content_block") or {}
+                cb_dict = dict(cb) if isinstance(cb, dict) else {}
+                while len(blocks) <= idx:
+                    blocks.append({})
+                blocks[idx] = cb_dict
+            elif etype == "content_block_delta":
+                idx = int(event.get("index") or 0)
+                if idx >= len(blocks):
+                    continue
+                delta = event.get("delta") or {}
+                if not isinstance(delta, dict):
+                    continue
+                dtype = delta.get("type")
+                block = blocks[idx]
+                if dtype == "text_delta":
+                    block["text"] = (block.get("text") or "") + (
+                        delta.get("text") or ""
+                    )
+                elif dtype == "input_json_delta":
+                    partial_json[idx] = partial_json.get(idx, "") + (
+                        delta.get("partial_json") or ""
+                    )
+                elif dtype == "thinking_delta":
+                    block["thinking"] = (block.get("thinking") or "") + (
+                        delta.get("thinking") or ""
+                    )
+                elif dtype == "signature_delta":
+                    block["signature"] = (block.get("signature") or "") + (
+                        delta.get("signature") or ""
+                    )
+            elif etype == "content_block_stop":
+                idx = int(event.get("index") or 0)
+                raw_json = partial_json.pop(idx, None)
+                if raw_json is not None and idx < len(blocks):
+                    try:
+                        blocks[idx]["input"] = (
+                            json.loads(raw_json) if raw_json else {}
+                        )
+                    except json.JSONDecodeError:
+                        blocks[idx]["input"] = raw_json
+            elif etype == "message_delta":
+                delta = event.get("delta") or {}
+                if isinstance(delta, dict):
+                    if "stop_reason" in delta:
+                        final_stop_reason = delta.get("stop_reason")
+                    if "stop_sequence" in delta:
+                        final_stop_sequence = delta.get("stop_sequence")
+                usage = event.get("usage")
+                if isinstance(usage, dict):
+                    final_usage.update(usage)
+            # message_stop: nothing to merge
+
+    if not message:
+        # Upstream returned no message_start; expose what we can so the
+        # client at least sees the assembled content.
+        message = {
+            "id": "",
+            "type": "message",
+            "role": "assistant",
+            "content": [],
+        }
+
+    message["content"] = blocks
+    if final_model and not message.get("model"):
+        message["model"] = final_model
+    if final_stop_reason is not None:
+        message["stop_reason"] = final_stop_reason
+    if final_stop_sequence is not None:
+        message["stop_sequence"] = final_stop_sequence
+    if final_usage:
+        existing_usage = message.get("usage")
+        merged = dict(existing_usage) if isinstance(existing_usage, dict) else {}
+        merged.update(final_usage)
+        message["usage"] = merged
+    return message
+
+
+class AnnotatedEvent(NamedTuple):
+    """One Anthropic SSE event after model-rewrite + token-tally bookkeeping.
+
+    ``sse_bytes`` is the wire-ready ``event:`` / ``data:`` block; the two
+    streaming paths in ``BaseUpstreamProvider`` consume ``sse_bytes`` plus
+    the tallies and only differ in whether they stream live or buffer
+    first.
+
+    ``cache_read_input_tokens`` and ``cache_creation_input_tokens`` are
+    surfaced separately so the cost path can price them against the cache
+    rate rather than fold them silently into the regular input bucket.
+
+    ``total_cost`` / ``input_cost`` / ``output_cost`` carry any
+    USD cost figures the upstream attached to this event (from
+    ``usage.cost``, ``usage.total_cost``, or ``usage.cost_details``) so the
+    streaming paths can re-embed them in the rebuilt ``usage`` dict and let
+    ``calculate_cost`` convert directly USD→sats instead of falling back to
+    token-based math.
+    """
+
+    event: dict
+    sse_bytes: bytes
+    input_tokens: int
+    output_tokens: int
+    cache_read_input_tokens: int
+    cache_creation_input_tokens: int
+    total_cost: float
+    input_cost: float
+    output_cost: float
+    model: str | None
+
+
+def annotate_event(event: dict, requested_model: str | None) -> AnnotatedEvent:
+    """Rewrite ``model`` fields and extract per-event token / model info.
+
+    Mutates ``event`` in place when ``requested_model`` is set so the
+    upstream's true model name doesn't leak to the client.
+    """
+    if requested_model:
+        msg = event.get("message")
+        if isinstance(msg, dict) and "model" in msg:
+            msg["model"] = requested_model
+        if "model" in event:
+            event["model"] = requested_model
+
+    in_tokens = 0
+    out_tokens = 0
+    cache_read_tokens = 0
+    cache_create_tokens = 0
+    total_cost = 0.0
+    input_cost = 0.0
+    output_cost = 0.0
+    model: str | None = None
+
+    def _coerce_float(value: object) -> float:
+        if value is None or isinstance(value, bool):
+            return 0.0
+        if not isinstance(value, (int, float, str)):
+            return 0.0
+        try:
+            return max(0.0, float(value))
+        except (TypeError, ValueError):
+            return 0.0
+
+    def _accumulate(usage: dict) -> None:
+        nonlocal in_tokens, out_tokens, cache_read_tokens, cache_create_tokens
+        nonlocal total_cost, input_cost, output_cost
+        in_tokens += int(usage.get("input_tokens") or 0)
+        out_tokens += int(usage.get("output_tokens") or 0)
+        cache_read_tokens += int(usage.get("cache_read_input_tokens") or 0)
+        cache_create_tokens += int(usage.get("cache_creation_input_tokens") or 0)
+        total_cost += _coerce_float(usage.get("total_cost"))
+        input_cost += _coerce_float(usage.get("input_cost"))
+        output_cost += _coerce_float(usage.get("output_cost"))
+
+    msg_for_meta = event.get("message")
+    if isinstance(msg_for_meta, dict):
+        if msg_for_meta.get("model"):
+            model = str(msg_for_meta["model"])
+        usage = msg_for_meta.get("usage")
+        if isinstance(usage, dict):
+            _accumulate(usage)
+
+    if isinstance(event.get("usage"), dict):
+        _accumulate(event["usage"])
+
+    # Some upstreams (notably OpenRouter-style proxies) attach cost fields
+    # directly at the event root rather than inside ``usage``.
+    for field in ("total_cost", "cost"):
+        total_cost = max(total_cost, _coerce_float(event.get(field)))
+    input_cost = max(input_cost, _coerce_float(event.get("input_cost")))
+    output_cost = max(output_cost, _coerce_float(event.get("output_cost")))
+    root_cost_details = event.get("cost_details")
+    if isinstance(root_cost_details, dict):
+        total_cost = max(
+            total_cost,
+            _coerce_float(root_cost_details.get("total_cost")),
+        )
+        input_cost = max(
+            input_cost,
+            _coerce_float(root_cost_details.get("input_cost")),
+        )
+        output_cost = max(
+            output_cost,
+            _coerce_float(root_cost_details.get("output_cost")),
+        )
+
+    event_type = str(event.get("type") or "")
+    payload = json.dumps(event)
+    if event_type:
+        sse_bytes = f"event: {event_type}\ndata: {payload}\n\n".encode()
+    else:
+        sse_bytes = f"data: {payload}\n\n".encode()
+
+    return AnnotatedEvent(
+        event,
+        sse_bytes,
+        in_tokens,
+        out_tokens,
+        cache_read_tokens,
+        cache_create_tokens,
+        total_cost,
+        input_cost,
+        output_cost,
+        model,
+    )
+
+
+async def stream_annotated_events(
+    iterator: AsyncIterator[Any],
+    requested_model: str | None,
+) -> AsyncGenerator[AnnotatedEvent, None]:
+    """Yield annotated, SSE-serialized events from a litellm stream.
+
+    Both streaming paths in ``BaseUpstreamProvider`` consume this; the only
+    divergence between them — yield-as-you-go vs buffer-then-replay — stays
+    in the caller.
+    """
+    sse_buffer = b""
+    async for chunk in iterator:
+        events, sse_buffer = events_from_chunk(chunk, sse_buffer)
+        for event in events:
+            yield annotate_event(event, requested_model)
+
+
+def embed_usd_costs(
+    usage: dict,
+    total_cost: float,
+    input_cost: float,
+    output_cost: float,
+) -> None:
+    """Mutate ``usage`` so ``calculate_cost`` will pick up the USD totals.
+
+    Mirrors the upstream shape: when any USD figure is present, attach
+    ``cost`` (used by the simple-fallback branch in ``calculate_cost``) and
+    a ``cost_details`` block (used by the preferred branch — also gives the
+    input/output USD split when we have one).
+    """
+    if total_cost <= 0 and input_cost <= 0 and output_cost <= 0:
+        return
+
+    cost_details: dict[str, float] = {}
+    effective_total = total_cost
+    if effective_total <= 0 and (input_cost > 0 or output_cost > 0):
+        effective_total = input_cost + output_cost
+
+    if effective_total > 0:
+        cost_details["total_cost"] = effective_total
+        usage["cost"] = effective_total
+    if input_cost > 0:
+        cost_details["input_cost"] = input_cost
+    if output_cost > 0:
+        cost_details["output_cost"] = output_cost
+    if cost_details:
+        usage["cost_details"] = cost_details
+
+
+def compute_refund(amount: int, unit: str, cost_msats: int) -> int:
+    if unit == "msat":
+        return amount - cost_msats
+    if unit == "sat":
+        return amount - (cost_msats + 999) // 1000
+    raise ValueError(f"Invalid unit: {unit}")
+
+
+async def dispatch_anthropic_messages(
+    *,
+    request_body: bytes | None,
+    model_obj: Model,
+    base_url: str,
+    api_key: str,
+    provider_prefix: str,
+    transform_model_name: Callable[[str], str],
+    log_extra: dict[str, Any] | None = None,
+) -> tuple[bool, Any, str | None]:
+    """Call ``litellm.anthropic.messages.acreate`` and return
+    ``(client_stream, result, requested_model)``.
+
+    Shared by the bearer-key and x-cashu paths. Raises :class:`UpstreamError`
+    on bad input or upstream failure.
+    """
+    if not request_body:
+        raise UpstreamError(
+            "Missing request body for /v1/messages", status_code=400
+        )
+
+    try:
+        body: dict = json.loads(request_body)
+    except json.JSONDecodeError as exc:
+        raise UpstreamError(
+            f"Invalid JSON in /v1/messages body: {exc}", status_code=400
+        ) from exc
+
+    body.pop("model", None)
+    # `stream` here is what the **client** asked for. Upstream is always
+    # streamed (see `upstream_stream` below); when the client asked for a
+    # non-streaming response we drain and aggregate the events into a
+    # single Anthropic Message dict before returning. This sidesteps
+    # provider-specific non-streaming caps (e.g. Fireworks rejects
+    # `max_tokens > 4096` unless `stream=true`).
+    client_stream = bool(body.pop("stream", False))
+    upstream_stream = True
+
+    dropped: dict[str, Any] = {}
+    for field in ANTHROPIC_ONLY_FIELDS:
+        if field in body:
+            dropped[field] = body.pop(field)
+    if dropped:
+        logger.debug(
+            "Dropped anthropic-only fields before litellm dispatch",
+            extra={"dropped_keys": sorted(dropped.keys())},
+        )
+
+    # Convention: `model.id` is the canonical upstream model name;
+    # `forwarded_model_id` is the public alias the internal API exposes
+    # and echoes back to the client.
+    requested_model = (
+        (model_obj.forwarded_model_id or model_obj.id) if model_obj else None
+    )
+    upstream_model = transform_model_name(model_obj.id)
+    litellm_model = f"{provider_prefix}{upstream_model}"
+
+    kwargs: dict = {
+        "model": litellm_model,
+        "api_base": base_url,
+        "api_key": api_key,
+        "stream": upstream_stream,
+        **body,
+    }
+
+    logger.info(
+        "Dispatching /v1/messages via litellm",
+        extra={
+            "model": litellm_model,
+            "resolved_provider": provider_prefix.rstrip("/"),
+            "client_stream": client_stream,
+            "upstream_stream": upstream_stream,
+            **(log_extra or {}),
+        },
+    )
+
+    try:
+        result = await litellm.anthropic.messages.acreate(**kwargs)
+    except Exception as exc:
+        exc_message = getattr(exc, "message", None) or str(exc) or repr(exc)
+        exc_status = getattr(exc, "status_code", None)
+        exc_response = getattr(exc, "response", None)
+        response_text = None
+        if exc_response is not None:
+            try:
+                response_text = getattr(exc_response, "text", str(exc_response))
+            except Exception:
+                response_text = "<unreadable>"
+        logger.error(
+            "litellm dispatch failed",
+            extra={
+                "error": exc_message,
+                "error_type": type(exc).__name__,
+                "status_code": exc_status,
+                "llm_provider": getattr(exc, "llm_provider", None),
+                "body": getattr(exc, "body", None),
+                "response_text": response_text,
+                "model": litellm_model,
+                "api_base": base_url,
+            },
+        )
+        raise UpstreamError(
+            f"Upstream error via litellm: {exc_message}",
+            status_code=exc_status if isinstance(exc_status, int) else 502,
+        ) from exc
+
+    if not client_stream and hasattr(result, "__aiter__"):
+        # Client asked for a non-streaming response but we always stream
+        # from upstream — drain the events into a single Anthropic Message
+        # dict so the rest of the pipeline can treat it as if upstream had
+        # returned non-streaming. Some litellm adapters return a
+        # non-streaming dict even when ``stream=True``; in that case,
+        # leave the result as-is.
+        try:
+            aggregated: Any = await aggregate_anthropic_events_to_message(
+                cast(AsyncIterator[Any], result)
+            )
+        except Exception as exc:
+            logger.error(
+                "Failed to aggregate streamed events into message",
+                extra={
+                    "error": str(exc),
+                    "error_type": type(exc).__name__,
+                    "model": litellm_model,
+                },
+            )
+            raise UpstreamError(
+                f"Failed to aggregate upstream stream: {exc}",
+                status_code=502,
+            ) from exc
+        return client_stream, aggregated, requested_model
+
+    return client_stream, result, requested_model

routstr/upstream/ollama.py

@@ -21,6 +21,7 @@ class OllamaUpstreamProvider(BaseUpstreamProvider):
     provider_type = "ollama"
     default_base_url = "http://localhost:11434"
     platform_url = None
+    litellm_provider_prefix = "ollama_chat/"
 
     def __init__(
         self,
@@ -184,7 +185,7 @@ class OllamaUpstreamProvider(BaseUpstreamProvider):
             except Exception:
                 self._models_cache = models_with_fees
 
-            self._models_by_id = {m.id: m for m in self._models_cache}
+            self._models_by_id = {m.forwarded_model_id or m.id: m for m in self._models_cache}
             logger.info(
                 f"Refreshed models cache for {self.base_url}",
                 extra={"model_count": len(models)},

routstr/upstream/openrouter.py

@@ -15,6 +15,8 @@ class OpenRouterUpstreamProvider(BaseUpstreamProvider):
     provider_type = "openrouter"
     default_base_url = "https://openrouter.ai/api/v1"
     platform_url = "https://openrouter.ai/settings/keys"
+    supports_anthropic_messages = True
+    litellm_provider_prefix = "openrouter/"
 
     def __init__(self, api_key: str, provider_fee: float = 1.06):
         """Initialize OpenRouter provider with API key.

routstr/upstream/perplexity.py

@@ -13,6 +13,7 @@ class PerplexityUpstreamProvider(BaseUpstreamProvider):
     provider_type = "perplexity"
     default_base_url = "https://api.perplexity.ai/"
     platform_url = "https://www.perplexity.ai/account/api/keys"
+    litellm_provider_prefix = "perplexity/"
 
     def __init__(self, api_key: str, provider_fee: float = 1.01):
         super().__init__(

routstr/upstream/ppqai.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Optional
 
 import httpx
-from pydantic import BaseModel, Field
+from pydantic.v1 import BaseModel, Field
 
 from ..core.logging import get_logger
 from ..payment.models import Architecture, Model, Pricing, async_fetch_openrouter_models

routstr/upstream/xai.py

@@ -13,6 +13,7 @@ class XAIUpstreamProvider(BaseUpstreamProvider):
     provider_type = "x-ai"
     default_base_url = "https://api.x.ai/v1"
     platform_url = "https://console.x.ai/"
+    litellm_provider_prefix = "xai/"
 
     def __init__(self, api_key: str, provider_fee: float = 1.01):
         super().__init__(

routstr/wallet.py

@@ -1,10 +1,13 @@
 import asyncio
 import time
+import typing
 from typing import TypedDict
 
 from cashu.core.base import Proof, Token
+from cashu.core.mint_info import MintInfo as _CashuMintInfo
 from cashu.wallet.helpers import deserialize_token_from_string
 from cashu.wallet.wallet import Wallet
+from pydantic_core import PydanticUndefined
 from sqlmodel import col, select, update
 
 from .core import db, get_logger
@@ -12,6 +15,18 @@ from .core.db import store_cashu_transaction
 from .core.settings import settings
 from .payment.lnurl import raw_send_to_lnurl
 
+# cashu still declares Optional[X] without explicit defaults on MintInfo.
+# Under pydantic v2 those are required, but real mints omit many of them.
+# Default Optional fields to None at import time so balance fetches don't 422.
+for _name, _field in _CashuMintInfo.model_fields.items():
+    _annot = _field.annotation
+    _is_optional = typing.get_origin(_annot) is typing.Union and type(
+        None
+    ) in typing.get_args(_annot)
+    if _is_optional and _field.default is PydanticUndefined:
+        _field.default = None
+_CashuMintInfo.model_rebuild(force=True)
+
 logger = get_logger(__name__)
 
 
@@ -326,7 +341,7 @@ async def credit_balance(
         except Exception:
             pass
 
-        logger.info(
+        logger.debug(
             "Cashu token successfully redeemed and stored",
             extra={"amount": amount, "unit": unit, "mint_url": mint_url},
         )
@@ -488,7 +503,7 @@ async def fetch_all_balances(
 
 async def periodic_payout() -> None:
     if not settings.receive_ln_address:
-        logger.error("RECEIVE_LN_ADDRESS is not set, skipping payout")
+        logger.warning("RECEIVE_LN_ADDRESS is not set, periodic payout disabled")
         return
     while True:
         await asyncio.sleep(60 * 15)

tests/unit/test_cost_calculation_caching.py

@@ -0,0 +1,345 @@
+"""Tests for cache token handling in cost calculation.
+
+Covers OpenAI vs Anthropic caching formats, edge cases, and billing accuracy.
+"""
+
+import os
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+os.environ.setdefault("UPSTREAM_BASE_URL", "http://test")
+os.environ.setdefault("UPSTREAM_API_KEY", "test")
+os.environ.setdefault("LIGHTNING_ADDRESS", "test@stm.to")
+
+from routstr.core.settings import settings
+from routstr.payment.cost_calculation import CostData, MaxCostData, calculate_cost
+
+
+@pytest.fixture
+def mock_session() -> AsyncMock:
+    """Mock AsyncSession for cost calculation tests."""
+    return AsyncMock()
+
+
+@pytest.fixture(autouse=True)
+def mock_fixed_pricing(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Mock settings and price to use fixed pricing."""
+    monkeypatch.setattr(settings, "fixed_pricing", True)
+    monkeypatch.setattr(settings, "fixed_per_1k_input_tokens", 0.001)
+    monkeypatch.setattr(settings, "fixed_per_1k_output_tokens", 0.001)
+
+
+@pytest.fixture(autouse=True)
+def patch_sats_usd_price() -> None:  # type: ignore[misc]
+    """Patch sats_usd_price to avoid initialization issues."""
+    with patch("routstr.payment.cost_calculation.sats_usd_price", return_value=5.0e-5):
+        yield
+
+
+# ============================================================================
+# Test 1: OpenAI Cache Format
+# ============================================================================
+@pytest.mark.asyncio
+async def test_openai_cache_subtraction(mock_session: AsyncMock) -> None:
+    """OpenAI includes cached_tokens in prompt_tokens, subtract them."""
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "prompt_tokens": 2000,  # ← Includes 1000 cached
+            "completion_tokens": 100,
+            "prompt_tokens_details": {
+                "cached_tokens": 1000  # ← Extracted separately
+            }
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 1000  # 2000 - 1000
+    assert result.cache_read_input_tokens == 1000
+    assert result.output_tokens == 100
+
+
+# ============================================================================
+# Test 2: Anthropic Cache Format
+# ============================================================================
+@pytest.mark.asyncio
+async def test_anthropic_cache_additive(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Anthropic cache tokens are separate (additive) from input_tokens."""
+    response = {
+        "model": "claude-3-5-sonnet",
+        "usage": {
+            "input_tokens": 500,  # ← Regular input only
+            "output_tokens": 100,
+            "cache_creation_input_tokens": 1500,  # ← Additive, not included above
+            "cache_read_input_tokens": 0,
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 500
+    assert result.cache_creation_input_tokens == 1500
+    assert result.cache_read_input_tokens == 0
+    assert result.output_tokens == 100
+
+
+# ============================================================================
+# Test 3: Invalid Cache (Edge Case)
+# ============================================================================
+@pytest.mark.asyncio
+async def test_cache_read_exceeds_prompt_tokens(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Handle buggy upstream reporting cached > prompt_tokens."""
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "prompt_tokens": 100,
+            "completion_tokens": 50,
+            "prompt_tokens_details": {
+                "cached_tokens": 150  # ← Invalid! Greater than prompt
+            }
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    # Should not go negative
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 0  # max(0, 100 - 150)
+    assert result.cache_read_input_tokens == 150
+    assert result.output_tokens == 50
+
+
+# ============================================================================
+# Test 4: Malformed Token Values
+# ============================================================================
+@pytest.mark.asyncio
+async def test_malformed_cache_tokens_coerce_to_zero(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Handle non-numeric cache token values."""
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "prompt_tokens": 100,
+            "completion_tokens": 50,
+            "cache_read_input_tokens": "-50",  # ← String, negative
+            "prompt_tokens_details": {
+                "cached_tokens": "invalid"  # ← Non-numeric string
+            }
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    # Both should coerce to 0
+    assert isinstance(result, CostData)
+    assert result.cache_read_input_tokens == 0
+    assert result.input_tokens == 100  # No subtraction if cache_read = 0
+
+
+# ============================================================================
+# Test 5: Anthropic Cache Not Subtracted
+# ============================================================================
+@pytest.mark.asyncio
+async def test_anthropic_cache_not_subtracted(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Anthropic cache fields should NOT be subtracted from input_tokens."""
+    response = {
+        "model": "claude-3-5-sonnet",
+        "usage": {
+            "input_tokens": 500,
+            "completion_tokens": 100,
+            "cache_read_input_tokens": 200,  # ← Additive, don't subtract
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    # Anthropic: input_tokens stays as-is
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 500  # NOT 300
+    assert result.cache_read_input_tokens == 200
+
+
+# ============================================================================
+# Test 6: Only Cache Read, No Regular Input
+# ============================================================================
+@pytest.mark.asyncio
+async def test_only_cache_read_tokens(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Handle response with only cache read tokens."""
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "prompt_tokens": 0,
+            "completion_tokens": 50,
+            "prompt_tokens_details": {
+                "cached_tokens": 1000
+            }
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 0  # max(0, 0 - 1000)
+    assert result.cache_read_input_tokens == 1000
+    assert result.output_tokens == 50
+
+
+# ============================================================================
+# Test 7: Only Cache Creation
+# ============================================================================
+@pytest.mark.asyncio
+async def test_only_cache_creation_tokens(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Handle response with only cache creation tokens (Anthropic)."""
+    response = {
+        "model": "claude-3-5-sonnet",
+        "usage": {
+            "input_tokens": 500,
+            "output_tokens": 100,
+            "cache_creation_input_tokens": 2000,
+            "cache_read_input_tokens": 0,
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 500
+    assert result.cache_creation_input_tokens == 2000
+    assert result.cache_read_input_tokens == 0
+    assert result.output_tokens == 100
+
+
+# ============================================================================
+# Test 8: Both Cache Read and Creation
+# ============================================================================
+@pytest.mark.asyncio
+async def test_both_cache_read_and_creation(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Handle response with both cache read and creation."""
+    response = {
+        "model": "claude-3-5-sonnet",
+        "usage": {
+            "input_tokens": 300,
+            "output_tokens": 100,
+            "cache_creation_input_tokens": 2000,
+            "cache_read_input_tokens": 500,
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 300
+    assert result.cache_creation_input_tokens == 2000
+    assert result.cache_read_input_tokens == 500
+    assert result.output_tokens == 100
+
+
+# ============================================================================
+# Test 9: Token Field Fallback
+# ============================================================================
+@pytest.mark.asyncio
+async def test_token_field_fallback_order(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Verify fallback order for token extraction."""
+    # When prompt_tokens is not present, fall back to input_tokens
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "input_tokens": 250,
+            "completion_tokens": 50,
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 250
+    assert result.output_tokens == 50
+
+
+# ============================================================================
+# Test 10: Float Token Values
+# ============================================================================
+@pytest.mark.asyncio
+async def test_float_token_values_coerced_to_int(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Handle float token values by converting to int."""
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "prompt_tokens": 100.7,  # Float
+            "completion_tokens": 50.3,  # Float
+            "cache_read_input_tokens": 25.9,  # Float
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.input_tokens == 100  # Floored
+    assert result.output_tokens == 50  # Floored
+    assert result.cache_read_input_tokens == 25  # Floored
+
+
+# ============================================================================
+# Test 11: Boolean Cache Tokens
+# ============================================================================
+@pytest.mark.asyncio
+async def test_boolean_cache_tokens_coerced_to_zero(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Handle boolean cache token values by coercing to zero."""
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "prompt_tokens": 100,
+            "completion_tokens": 50,
+            "cache_read_input_tokens": True,  # Boolean
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.cache_read_input_tokens == 0  # Boolean coerced to 0
+    assert result.input_tokens == 100  # No subtraction
+
+
+# ============================================================================
+# Test 12: Zero Cache Tokens
+# ============================================================================
+@pytest.mark.asyncio
+async def test_zero_cache_tokens(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """Handle explicit zero cache tokens."""
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "prompt_tokens": 100,
+            "completion_tokens": 50,
+            "prompt_tokens_details": {
+                "cached_tokens": 0
+            }
+        }
+    }
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, CostData)
+    assert result.cache_read_input_tokens == 0
+    assert result.input_tokens == 100
+
+
+# ============================================================================
+# Test 13: Missing Usage Block
+# ============================================================================
+@pytest.mark.asyncio
+async def test_missing_usage_block(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """When usage is missing, return MaxCostData with zero tokens."""
+    response = {"model": "gpt-4", "choices": [{"message": {"content": "test"}}]}
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, MaxCostData)
+    assert result.input_tokens == 0
+    assert result.cache_read_input_tokens == 0
+    assert result.output_tokens == 0
+
+
+# ============================================================================
+# Test 14: Null Usage Block
+# ============================================================================
+@pytest.mark.asyncio
+async def test_null_usage_block(mock_session: AsyncMock, mock_fixed_pricing: None) -> None:
+    """When usage is null, return MaxCostData with zero tokens."""
+    response = {"model": "gpt-4", "usage": None}
+    result = await calculate_cost(response, max_cost=100000, session=mock_session)
+
+    assert isinstance(result, MaxCostData)
+    assert result.input_tokens == 0
+    assert result.cache_read_input_tokens == 0

tests/unit/test_litellm_routing.py

@@ -0,0 +1,93 @@
+"""Tests for `routstr.upstream.litellm_routing.detect_litellm_prefix`."""
+
+from __future__ import annotations
+
+import pytest
+
+from routstr.upstream.litellm_routing import detect_litellm_prefix
+
+
+@pytest.mark.parametrize(
+    "base_url,expected",
+    [
+        # The bug case: custom row pointing at Fireworks must NOT route to openai/.
+        ("https://api.fireworks.ai/inference/v1", "fireworks_ai/"),
+        # Other OpenAI-compatible providers commonly plugged into the custom slot.
+        ("https://api.groq.com/openai/v1", "groq/"),
+        ("https://api.x.ai/v1", "xai/"),
+        ("https://api.deepseek.com/v1", "deepseek/"),
+        ("https://api.together.xyz/v1", "together_ai/"),
+        ("https://api.perplexity.ai", "perplexity/"),
+        ("https://openrouter.ai/api/v1", "openrouter/"),
+        ("https://api.mistral.ai/v1", "mistral/"),
+        ("https://codestral.mistral.ai/v1", "codestral/"),
+        ("https://api.cohere.com/v1", "cohere_chat/"),
+        ("https://api.cohere.ai/v1", "cohere_chat/"),
+        ("https://api.deepinfra.com/v1/openai", "deepinfra/"),
+        ("https://api.cerebras.ai/v1", "cerebras/"),
+        ("https://api.sambanova.ai/v1", "sambanova/"),
+        ("https://api.moonshot.cn/v1", "moonshot/"),
+        ("https://api.moonshot.ai/v1", "moonshot/"),
+        ("https://api.studio.nebius.com/v1", "nebius/"),
+        ("https://api.novita.ai/v3/openai", "novita/"),
+        ("https://api.lambda.ai/v1", "lambda_ai/"),
+        ("https://api.aimlapi.com/v1", "aiml/"),
+        ("https://api.featherless.ai/v1", "featherless_ai/"),
+        ("https://integrate.api.nvidia.com/v1", "nvidia_nim/"),
+        ("https://inference.baseten.co/v1", "baseten/"),
+        ("https://ai-gateway.vercel.sh/v1", "vercel_ai_gateway/"),
+        ("https://api.inference.wandb.ai/v1", "wandb/"),
+        ("https://api.poe.com/v1", "poe/"),
+        ("https://llm.chutes.ai/v1/", "chutes/"),
+        ("https://api.v0.dev/v1", "v0/"),
+        ("https://api.hyperbolic.xyz/v1", "hyperbolic/"),
+        ("https://api.synthetic.new/openai/v1", "synthetic/"),
+        ("https://api.stima.tech/v1", "apertis/"),
+        ("https://nano-gpt.com/api/v1", "nano-gpt/"),
+        ("https://api.friendli.ai/serverless/v1", "friendliai/"),
+        ("https://api.galadriel.com/v1", "galadriel/"),
+        ("https://api.llama.com/compat/v1", "meta_llama/"),
+        ("https://api.minimax.io/v1", "minimax/"),
+        ("https://api.minimaxi.com/v1", "minimax/"),
+        ("https://platform.publicai.co/v1", "publicai/"),
+        ("https://inference.api.nscale.com/v1", "nscale/"),
+        ("https://dashscope-intl.aliyuncs.com/compatible-mode/v1", "dashscope/"),
+        ("https://api.endpoints.anyscale.com/v1", "anyscale/"),
+        ("https://api.ai21.com/studio/v1", "ai21_chat/"),
+        ("https://ark.cn-beijing.volces.com/api/v3", "volcengine/"),
+        ("https://api.voyageai.com/v1", "voyage/"),
+        ("https://api.jina.ai/v1", "jina_ai/"),
+        ("https://api.snowflakecomputing.com", "snowflake/"),
+        ("https://my-workspace.databricks.com/serving-endpoints", "databricks/"),
+        ("https://huggingface.co/api/inference", "huggingface/"),
+        # First-class providers — URL detection still produces the right prefix
+        # so subclasses without an explicit override stay correct.
+        ("https://api.openai.com/v1", "openai/"),
+        ("https://api.anthropic.com/v1", "anthropic/"),
+        ("https://generativelanguage.googleapis.com/v1beta/openai", "gemini/"),
+        ("https://us-central1-aiplatform.googleapis.com/v1", "vertex_ai/"),
+        # Azure ordering: must beat api.openai.com.
+        ("https://my-resource.openai.azure.com/openai/deployments/foo", "azure/"),
+        # Ollama hints.
+        ("http://localhost:11434/v1", "ollama_chat/"),
+        ("http://127.0.0.1:11434/v1", "ollama_chat/"),
+        ("http://my-ollama-host:11434/v1", "ollama_chat/"),
+        # Casing and trailing slash normalisation.
+        ("HTTPS://API.FIREWORKS.AI/INFERENCE/V1/", "fireworks_ai/"),
+        # Unknown host falls back to openai/ (still OpenAI-compatible by convention).
+        ("https://example.com/v1", "openai/"),
+        ("", "openai/"),
+    ],
+)
+def test_detect_litellm_prefix(base_url: str, expected: str) -> None:
+    assert detect_litellm_prefix(base_url) == expected
+
+
+def test_detect_litellm_prefix_none_uses_default() -> None:
+    assert detect_litellm_prefix(None) == "openai/"
+
+
+def test_detect_litellm_prefix_custom_default() -> None:
+    assert detect_litellm_prefix("https://example.com", default="anthropic/") == (
+        "anthropic/"
+    )

tests/unit/test_messages_dispatch_cost_accumulation.py

@@ -0,0 +1,333 @@
+"""Tests for cost accumulation in streaming message dispatch.
+
+Verifies that costs are correctly summed across multiple streaming events,
+not taking only the maximum.
+"""
+
+import os
+
+import pytest
+
+os.environ.setdefault("UPSTREAM_BASE_URL", "http://test")
+os.environ.setdefault("UPSTREAM_API_KEY", "test")
+
+from routstr.upstream.messages_dispatch import annotate_event
+
+
+# ============================================================================
+# Test 1: Cost Accumulation via AnnotatedEvent
+# ============================================================================
+@pytest.mark.unit
+def test_annotate_event_extracts_costs() -> None:
+    """Each event should report its own costs."""
+    event = {
+        "type": "content_block_start",
+        "usage": {"total_cost": 0.010, "input_cost": 0.008, "output_cost": 0.002}
+    }
+    result = annotate_event(event, None)
+
+    assert result.total_cost == 0.010
+    assert result.input_cost == 0.008
+    assert result.output_cost == 0.002
+
+
+# ============================================================================
+# Test 2: Multiple Events with Incremental Costs
+# ============================================================================
+@pytest.mark.unit
+def test_multiple_events_sum_costs() -> None:
+    """When processing multiple events, costs should accumulate (not max)."""
+    # Event 1: initial costs
+    event1 = {
+        "type": "message_start",
+        "usage": {"input_tokens": 100, "total_cost": 0.010}
+    }
+    result1 = annotate_event(event1, None)
+
+    # Event 2: additional costs
+    event2 = {
+        "type": "content_block_start",
+        "usage": {"output_tokens": 50, "total_cost": 0.005}
+    }
+    result2 = annotate_event(event2, None)
+
+    # Event 3: more costs
+    event3 = {
+        "type": "message_delta",
+        "usage": {"output_tokens": 25, "total_cost": 0.008}
+    }
+    result3 = annotate_event(event3, None)
+
+    # Each event should report its own cost (before accumulation)
+    assert result1.total_cost == 0.010
+    assert result2.total_cost == 0.005
+    assert result3.total_cost == 0.008
+
+    # When summed for billing: 0.010 + 0.005 + 0.008 = 0.023
+    # This verifies the cost accumulation fix (using += instead of max())
+    total_from_events = result1.total_cost + result2.total_cost + result3.total_cost
+    assert total_from_events == 0.023
+
+
+# ============================================================================
+# Test 3: Token Accumulation Consistency
+# ============================================================================
+@pytest.mark.unit
+def test_tokens_and_costs_extracted_independently() -> None:
+    """Tokens and costs should be extracted independently per event."""
+    event = {
+        "type": "content_block_delta",
+        "usage": {
+            "input_tokens": 100,
+            "output_tokens": 50,
+            "cache_read_input_tokens": 30,
+            "total_cost": 0.007,
+            "input_cost": 0.005,
+            "output_cost": 0.002
+        }
+    }
+    result = annotate_event(event, None)
+
+    # All values should be extracted
+    assert result.input_tokens == 100
+    assert result.output_tokens == 50
+    assert result.cache_read_input_tokens == 30
+    assert result.total_cost == 0.007
+    assert result.input_cost == 0.005
+    assert result.output_cost == 0.002
+
+
+# ============================================================================
+# Test 4: Cost in Message vs Root
+# ============================================================================
+@pytest.mark.unit
+def test_cost_extracted_from_message_usage() -> None:
+    """Costs in message.usage should be extracted correctly."""
+    event = {
+        "type": "message_start",
+        "message": {
+            "usage": {
+                "input_tokens": 100,
+                "total_cost": 0.010,
+                "input_cost": 0.008,
+                "output_cost": 0.002
+            }
+        }
+    }
+    result = annotate_event(event, None)
+
+    assert result.input_tokens == 100
+    assert result.total_cost == 0.010
+    assert result.input_cost == 0.008
+    assert result.output_cost == 0.002
+
+
+# ============================================================================
+# Test 5: Cost at Event Root Level
+# ============================================================================
+@pytest.mark.unit
+def test_cost_extracted_from_event_root() -> None:
+    """Costs at event root should be extracted (OpenRouter style)."""
+    event = {
+        "type": "content_block_delta",
+        "usage": {"output_tokens": 25},
+        "total_cost": 0.005,  # ← At root level
+        "cost": 0.005
+    }
+    result = annotate_event(event, None)
+
+    assert result.output_tokens == 25
+    assert result.total_cost == 0.005
+
+
+# ============================================================================
+# Test 6: Cost Details in Event Root
+# ============================================================================
+@pytest.mark.unit
+def test_cost_details_extracted_from_event_root() -> None:
+    """cost_details at event root should be extracted correctly."""
+    event = {
+        "type": "message_delta",
+        "cost_details": {
+            "total_cost": 0.015,
+            "input_cost": 0.010,
+            "output_cost": 0.005
+        }
+    }
+    result = annotate_event(event, None)
+
+    assert result.total_cost == 0.015
+    assert result.input_cost == 0.010
+    assert result.output_cost == 0.005
+
+
+# ============================================================================
+# Test 7: No Duplicated Dict Lookups
+# ============================================================================
+@pytest.mark.unit
+def test_annotate_event_no_duplicate_lookups() -> None:
+    """Verify that dict lookups are not duplicated (fix for copy-paste error)."""
+    # This is tested implicitly through proper extraction
+    event = {
+        "type": "message_delta",
+        "cost_details": {
+            "total_cost": 0.020,
+            "input_cost": 0.015,
+            "output_cost": 0.005
+        }
+    }
+    result = annotate_event(event, None)
+
+    # Should extract each field exactly once, correctly
+    assert result.total_cost == 0.020
+    assert result.input_cost == 0.015
+    assert result.output_cost == 0.005
+
+
+# ============================================================================
+# Test 8: Model Name Extraction
+# ============================================================================
+@pytest.mark.unit
+def test_model_extracted_from_event() -> None:
+    """Model name should be extracted from event."""
+    event = {
+        "type": "message_start",
+        "message": {"model": "claude-3-5-sonnet"}
+    }
+    result = annotate_event(event, None)
+
+    assert result.model == "claude-3-5-sonnet"
+
+
+# ============================================================================
+# Test 9: Model Name Override
+# ============================================================================
+@pytest.mark.unit
+def test_model_name_override() -> None:
+    """Requested model should override actual model in event."""
+    event = {
+        "type": "message_start",
+        "message": {"model": "actual-model"}
+    }
+    # Override with requested_model
+    result = annotate_event(event, requested_model="alias-model")
+
+    # Event should be modified to use requested_model
+    assert event["message"]["model"] == "alias-model"  # type: ignore[index]
+    assert result.model == "alias-model"
+
+
+# ============================================================================
+# Test 10: SSE Encoding
+# ============================================================================
+@pytest.mark.unit
+def test_sse_bytes_encoded() -> None:
+    """Event should be encoded as SSE bytes."""
+    event = {
+        "type": "content_block_start",
+        "index": 0,
+        "content_block": {"type": "text", "text": ""}
+    }
+    result = annotate_event(event, None)
+
+    assert result.sse_bytes is not None
+    assert isinstance(result.sse_bytes, bytes)
+    assert b"event: content_block_start" in result.sse_bytes or b"data:" in result.sse_bytes
+
+
+# ============================================================================
+# Test 11: Missing Cost Fields Default to Zero
+# ============================================================================
+@pytest.mark.unit
+def test_missing_cost_fields_default_to_zero() -> None:
+    """When cost fields are missing, should default to 0.0."""
+    event = {
+        "type": "content_block_delta",
+        "usage": {"output_tokens": 25}
+        # No cost fields
+    }
+    result = annotate_event(event, None)
+
+    assert result.total_cost == 0.0
+    assert result.input_cost == 0.0
+    assert result.output_cost == 0.0
+
+
+# ============================================================================
+# Test 12: Cache Tokens in Events
+# ============================================================================
+@pytest.mark.unit
+def test_cache_tokens_extracted_from_event() -> None:
+    """Cache tokens should be extracted from event usage."""
+    event = {
+        "type": "message_delta",
+        "usage": {
+            "input_tokens": 100,
+            "output_tokens": 50,
+            "cache_read_input_tokens": 200,
+            "cache_creation_input_tokens": 0
+        }
+    }
+    result = annotate_event(event, None)
+
+    assert result.input_tokens == 100
+    assert result.output_tokens == 50
+    assert result.cache_read_input_tokens == 200
+    assert result.cache_creation_input_tokens == 0
+
+
+# ============================================================================
+# Test 13: Malformed Cost Values
+# ============================================================================
+@pytest.mark.unit
+def test_malformed_cost_values_coerced() -> None:
+    """Malformed cost values should be coerced to 0.0."""
+    event = {
+        "type": "message_delta",
+        "usage": {
+            "input_tokens": 100,
+            "output_tokens": 50,
+            "total_cost": "invalid",  # ← Non-numeric
+        }
+    }
+    result = annotate_event(event, None)
+
+    # Invalid cost should default to 0.0
+    assert result.total_cost == 0.0
+
+
+# ============================================================================
+# Test 14: Negative Cost Values
+# ============================================================================
+@pytest.mark.unit
+def test_negative_cost_values_clamped() -> None:
+    """Negative cost values should be clamped to 0.0."""
+    event = {
+        "type": "message_delta",
+        "usage": {
+            "input_tokens": 100,
+            "output_tokens": 50,
+            "total_cost": -0.05  # ← Negative
+        }
+    }
+    result = annotate_event(event, None)
+
+    # Negative cost should be clamped to 0.0
+    assert result.total_cost == 0.0
+
+
+# ============================================================================
+# Test 15: Streaming Event Type Preserved
+# ============================================================================
+@pytest.mark.unit
+def test_event_type_preserved_in_sse() -> None:
+    """Event type should be preserved in SSE encoding."""
+    event_types = ["message_start", "content_block_start", "content_block_delta", "message_delta"]
+    for event_type in event_types:
+        event = {"type": event_type}
+        result = annotate_event(event, None)
+
+        assert result.event["type"] == event_type  # type: ignore[index]
+        # SSE should include the event type line if present
+        if event_type:
+            assert f"event: {event_type}".encode() in result.sse_bytes

tests/unit/test_upstream_gemini.py

@@ -0,0 +1,313 @@
+"""Unit tests for the Gemini /v1/messages dispatch path.
+
+The Gemini upstream needs special handling because its OpenAI-compat
+surface rejects inbound ``functionCall`` parts that lack a
+``thought_signature``. We bypass litellm + openai SDK at the wire layer
+(see ``routstr/upstream/gemini_messages.py``) so we can inject Google's
+documented dummy signature (``"skip_thought_signature_validator"``).
+
+These tests cover the two pure helpers that drive the dispatcher:
+
+* ``inject_thought_signatures`` — request-side injection
+* ``_openai_chunks_to_anthropic_events`` — response-side translator
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import AsyncGenerator
+from typing import Any
+
+import pytest
+
+from routstr.upstream.gemini_messages import (
+    DUMMY_THOUGHT_SIGNATURE,
+    _openai_chunks_to_anthropic_events,
+    inject_thought_signatures,
+)
+
+# ---------------------------------------------------------------------------
+# inject_thought_signatures
+# ---------------------------------------------------------------------------
+
+
+def test_inject_thought_signatures_adds_dummy_to_each_tool_call() -> None:
+    messages: list[dict[str, Any]] = [
+        {"role": "user", "content": "do thing"},
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": "toolu_1",
+                    "type": "function",
+                    "function": {"name": "Bash", "arguments": "{}"},
+                },
+                {
+                    "id": "toolu_2",
+                    "type": "function",
+                    "function": {"name": "Read", "arguments": "{}"},
+                },
+            ],
+        },
+    ]
+
+    inject_thought_signatures(messages)
+
+    for tc in messages[1]["tool_calls"]:
+        assert (
+            tc["extra_content"]["google"]["thought_signature"]
+            == DUMMY_THOUGHT_SIGNATURE
+        )
+
+
+def test_inject_thought_signatures_preserves_existing_signature() -> None:
+    """Don't clobber a real signature that came back from a prior turn."""
+    messages: list[dict[str, Any]] = [
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": "tc1",
+                    "type": "function",
+                    "function": {"name": "fn", "arguments": "{}"},
+                    "extra_content": {
+                        "google": {"thought_signature": "real-signature"}
+                    },
+                }
+            ],
+        }
+    ]
+
+    inject_thought_signatures(messages)
+
+    assert (
+        messages[0]["tool_calls"][0]["extra_content"]["google"][
+            "thought_signature"
+        ]
+        == "real-signature"
+    )
+
+
+def test_inject_thought_signatures_skips_messages_without_tool_calls() -> None:
+    messages: list[dict[str, Any]] = [
+        {"role": "user", "content": "hi"},
+        {"role": "assistant", "content": "hello"},
+    ]
+
+    inject_thought_signatures(messages)
+
+    for m in messages:
+        assert "extra_content" not in m
+
+
+def test_inject_thought_signatures_handles_malformed_extra_content() -> None:
+    """If a caller already set ``extra_content`` to a non-dict (defensive),
+    we replace it instead of crashing."""
+    messages: list[dict[str, Any]] = [
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": "tc",
+                    "type": "function",
+                    "function": {"name": "fn", "arguments": "{}"},
+                    "extra_content": "garbage",
+                }
+            ],
+        }
+    ]
+
+    inject_thought_signatures(messages)
+
+    extra = messages[0]["tool_calls"][0]["extra_content"]
+    assert isinstance(extra, dict)
+    assert extra["google"]["thought_signature"] == DUMMY_THOUGHT_SIGNATURE
+
+
+# ---------------------------------------------------------------------------
+# _openai_chunks_to_anthropic_events
+# ---------------------------------------------------------------------------
+
+
+async def _lines(*chunks: dict | str) -> AsyncGenerator[str, None]:
+    """Helper to wrap chunk dicts as SSE-style ``data:`` lines."""
+    for c in chunks:
+        if isinstance(c, dict):
+            yield f"data: {json.dumps(c)}"
+        else:
+            yield c
+
+
+def _parse_anthropic_sse(blocks: list[bytes]) -> list[dict]:
+    """Flatten a list of Anthropic SSE byte chunks into event dicts."""
+    events: list[dict] = []
+    for blob in blocks:
+        text = blob.decode()
+        for entry in text.split("\n\n"):
+            for line in entry.splitlines():
+                if line.startswith("data:"):
+                    events.append(json.loads(line[5:].lstrip()))
+    return events
+
+
+@pytest.mark.asyncio
+async def test_translator_emits_text_only_response() -> None:
+    """Plain text response: message_start → content_block_* (text) →
+    message_delta(end_turn) → message_stop."""
+    chunks: list[dict] = [
+        {
+            "id": "chatcmpl-1",
+            "model": "gemini-2.5-flash",
+            "choices": [{"index": 0, "delta": {"role": "assistant"}}],
+        },
+        {"choices": [{"delta": {"content": "Hello"}}]},
+        {"choices": [{"delta": {"content": ", world"}}]},
+        {
+            "choices": [{"delta": {}, "finish_reason": "stop"}],
+            "usage": {"prompt_tokens": 5, "completion_tokens": 7},
+        },
+    ]
+
+    out = []
+    async for event_bytes in _openai_chunks_to_anthropic_events(
+        _lines(*chunks), requested_model="gemini-2.5-flash"
+    ):
+        out.append(event_bytes)
+
+    events = _parse_anthropic_sse(out)
+    types = [e["type"] for e in events]
+    assert types == [
+        "message_start",
+        "content_block_start",
+        "content_block_delta",
+        "content_block_delta",
+        "content_block_stop",
+        "message_delta",
+        "message_stop",
+    ]
+    # Text deltas concatenate to "Hello, world".
+    text_deltas = [
+        e["delta"]["text"]
+        for e in events
+        if e["type"] == "content_block_delta"
+    ]
+    assert "".join(text_deltas) == "Hello, world"
+    # Stop reason was mapped from openai's "stop".
+    msg_delta = next(e for e in events if e["type"] == "message_delta")
+    assert msg_delta["delta"]["stop_reason"] == "end_turn"
+    assert msg_delta["usage"]["input_tokens"] == 5
+    assert msg_delta["usage"]["output_tokens"] == 7
+
+
+@pytest.mark.asyncio
+async def test_translator_emits_tool_use_block() -> None:
+    """tool_calls split across deltas → tool_use content block with
+    accumulated input_json_delta and stop_reason='tool_use'."""
+    chunks: list[dict] = [
+        {
+            "id": "chatcmpl-2",
+            "model": "gemini-2.5-flash",
+            "choices": [{"delta": {"role": "assistant"}}],
+        },
+        {
+            "choices": [
+                {
+                    "delta": {
+                        "tool_calls": [
+                            {
+                                "index": 0,
+                                "id": "call-abc",
+                                "type": "function",
+                                "function": {
+                                    "name": "Bash",
+                                    "arguments": '{"cmd":',
+                                },
+                            }
+                        ]
+                    }
+                }
+            ]
+        },
+        {
+            "choices": [
+                {
+                    "delta": {
+                        "tool_calls": [
+                            {
+                                "index": 0,
+                                "function": {"arguments": ' "ls"}'},
+                            }
+                        ]
+                    }
+                }
+            ]
+        },
+        {"choices": [{"delta": {}, "finish_reason": "tool_calls"}]},
+    ]
+
+    out = []
+    async for event_bytes in _openai_chunks_to_anthropic_events(
+        _lines(*chunks), requested_model="gemini-2.5-flash"
+    ):
+        out.append(event_bytes)
+
+    events = _parse_anthropic_sse(out)
+    types = [e["type"] for e in events]
+    assert types == [
+        "message_start",
+        "content_block_start",
+        "content_block_delta",
+        "content_block_delta",
+        "content_block_stop",
+        "message_delta",
+        "message_stop",
+    ]
+    # Tool use block was opened with the right name.
+    cb_start = next(e for e in events if e["type"] == "content_block_start")
+    assert cb_start["content_block"]["type"] == "tool_use"
+    assert cb_start["content_block"]["name"] == "Bash"
+    assert cb_start["content_block"]["id"] == "call-abc"
+    # Argument deltas were forwarded as input_json_delta partials.
+    deltas = [e for e in events if e["type"] == "content_block_delta"]
+    assert all(d["delta"]["type"] == "input_json_delta" for d in deltas)
+    assert "".join(d["delta"]["partial_json"] for d in deltas) == (
+        '{"cmd": "ls"}'
+    )
+    # tool_calls finish_reason → tool_use stop_reason.
+    msg_delta = next(e for e in events if e["type"] == "message_delta")
+    assert msg_delta["delta"]["stop_reason"] == "tool_use"
+
+
+@pytest.mark.asyncio
+async def test_translator_handles_done_sentinel_and_blank_lines() -> None:
+    """Spec edge cases from openai SSE: ``data: [DONE]``, blank lines,
+    invalid JSON. Translator should skip them gracefully."""
+    chunks: list[dict | str] = [
+        {
+            "id": "x",
+            "model": "m",
+            "choices": [{"delta": {"role": "assistant"}}],
+        },
+        {"choices": [{"delta": {"content": "ok"}}]},
+        "",
+        ": comment",
+        "data: not-json",
+        "data: [DONE]",
+        {"choices": [{"delta": {}, "finish_reason": "stop"}]},
+    ]
+
+    out = []
+    async for event_bytes in _openai_chunks_to_anthropic_events(
+        _lines(*chunks), requested_model=None
+    ):
+        out.append(event_bytes)
+
+    events = _parse_anthropic_sse(out)
+    assert events[0]["type"] == "message_start"
+    assert events[-1]["type"] == "message_stop"
+    text = "".join(
+        e["delta"]["text"]
+        for e in events
+        if e["type"] == "content_block_delta"
+    )
+    assert text == "ok"

uv.lock

@@ -1,6 +1,10 @@
 version = 1
 revision = 3
 requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version < '3.14'",
+]
 
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+]
+
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -302,7 +315,7 @@ wheels = [
 
 [[package]]
 name = "cashu"
-version = "0.17.0"
+version = "0.20.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiosqlite" },
@@ -319,6 +332,7 @@ dependencies = [
     { name = "environs" },
     { name = "fastapi" },
     { name = "googleapis-common-protos" },
+    { name = "greenlet" },
     { name = "grpcio" },
     { name = "grpcio-tools" },
     { name = "h11" },
@@ -330,9 +344,9 @@ dependencies = [
     { name = "mypy-protobuf" },
     { name = "pycryptodomex" },
     { name = "pydantic" },
+    { name = "pydantic-settings" },
     { name = "pyjwt" },
     { name = "redis" },
-    { name = "secp256k1" },
     { name = "setuptools" },
     { name = "slowapi" },
     { name = "sqlalchemy", extra = ["asyncio"] },
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+    { url = "https://files.pythonhosted.org/packages/26/0c/3f736d3a6f2f88631d6f1cc42976e5ad7af3b97b7ff29b142f6d2829ae56/cashu-0.20.0-py3-none-any.whl", hash = "sha256:7148b13fcb8da63502775c5c627b56f74480dcaff2321cab65c1cfd2cd387dc2", size = 7087085, upload-time = "2026-03-31T16:44:30.544Z" },
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