Remove require_parameters injection from OpenRouter provider

The prepare_request_body override in OpenRouterUpstreamProvider injected
provider.require_parameters=true on tool-use requests. This is no longer
needed. Removed the override, the now-unused json import, and the
corresponding test suite.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "routstr"
-version = "0.4.3"
+version = "0.4.4"
 description = "Payment proxy for your LLM endpoint using cashu and nostr."
 readme = "README.md"
 requires-python = ">=3.11"

routstr/balance.py

@@ -269,7 +269,20 @@ async def refund_wallet_endpoint(
         )
         out_tx = out_tx_result.first()
         if out_tx is None:
-            raise HTTPException(status_code=404, detail="Refund not found")
+            # The "in" row exists with a request_id, but the "out" (refund)
+            # row hasn't been written yet — the upstream request is still in
+            # flight and the refund will be minted once it completes. Tell the
+            # client to retry instead of 404ing permanently (race condition
+            # where /v1/wallet/refund is polled before the refund exists).
+            logger.debug(
+                "refund_wallet_endpoint: refund pending (in row exists, out row not yet created)",
+                extra={"request_id": in_tx.request_id},
+            )
+            raise HTTPException(
+                status_code=425,
+                detail="Refund is pending; retry shortly.",
+                headers={"Retry-After": "2"},
+            )
         if out_tx.swept:
             raise HTTPException(status_code=410, detail="Refund has been swept")
 

routstr/core/version.py

@@ -20,7 +20,7 @@ import subprocess
 from functools import lru_cache
 from pathlib import Path
 
-BASE_VERSION = "0.4.3"
+BASE_VERSION = "0.4.4"
 
 _REPO_ROOT = Path(__file__).resolve().parents[2]
 _GIT_TIMEOUT_SECONDS = 2.0

routstr/payment/cost_calculation.py

@@ -41,6 +41,28 @@ class CostDataError(BaseModel):
     code: str
 
 
+def _empty_cost(cls: type[CostData] = CostData) -> CostData:
+    """Build an all-zero cost object — a full refund for an empty response.
+
+    Shared by the two paths that must not bill: an upstream response with no
+    usage data at all, and one that reports a USD cost but carries zero tokens
+    in every bucket.
+    """
+    return cls(
+        base_msats=0,
+        input_msats=0,
+        output_msats=0,
+        total_msats=0,
+        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,
+    )
+
+
 async def calculate_cost(
     response_data: dict,
     max_cost: int,
@@ -83,19 +105,7 @@ async def calculate_cost(
                 else None,
             },
         )
-        return MaxCostData(
-            base_msats=0,
-            input_msats=0,
-            output_msats=0,
-            total_msats=0,
-            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,
-        )
+        return _empty_cost(MaxCostData)
 
     usage_data = response_data.get("usage") or {}
     if not isinstance(usage_data, dict):
@@ -109,6 +119,27 @@ async def calculate_cost(
     # Try USD cost first
     usd_cost = _resolve_usd_cost(usage_data, response_data)
     if usd_cost > 0:
+        truly_empty = (
+            input_tokens == 0
+            and output_tokens == 0
+            and cache_read_tokens == 0
+            and cache_creation_tokens == 0
+        )
+        if truly_empty:
+            logger.warning(
+                "Upstream reported a USD cost but the response carries no "
+                "tokens at all (input, output, cache-read and cache-creation "
+                "are all zero) — refunding in full rather than billing the "
+                "USD-derived cost for an empty response.",
+                extra={
+                    "model": response_data.get("model", "unknown"),
+                    "usd_cost": usd_cost,
+                    "usage_keys": sorted(usage_data.keys())
+                    if isinstance(usage_data, dict)
+                    else None,
+                },
+            )
+            return _empty_cost()
         if input_tokens == 0 and output_tokens == 0:
             logger.warning(
                 "Upstream reported a USD cost but no token counts — "

routstr/payment/models.py

@@ -85,6 +85,30 @@ class Model(BaseModel):
         return hash(self.id)
 
 
+def litellm_cost_entry(model_id: str) -> dict | None:
+    """Look up ``model_id`` in litellm's bundled cost map.
+
+    litellm ships per-model USD rates keyed by the exact OpenRouter id
+    (``deepseek/deepseek-chat``) or the bare model name (``gpt-4o``,
+    ``claude-sonnet-4-5``), so both spellings are tried. Keys are lowercase, so
+    a mixed-case upstream id (``deepseek-ai/DeepSeek-V4-Flash``) is retried via
+    a case-insensitive scan. Returns the matched cost dict, or ``None``.
+    """
+    import litellm
+
+    candidates = (model_id, model_id.split("/", 1)[-1])
+    for key in candidates:
+        info = litellm.model_cost.get(key)
+        if isinstance(info, dict):
+            return info
+
+    lowered = {c.lower() for c in candidates}
+    for key, info in litellm.model_cost.items():
+        if isinstance(key, str) and key.lower() in lowered and isinstance(info, dict):
+            return info
+    return None
+
+
 def backfill_cache_pricing(model_id: str, pricing: Pricing) -> Pricing:
     """Fill missing cache rates from litellm's bundled cost map.
 
@@ -92,12 +116,8 @@ def backfill_cache_pricing(model_id: str, pricing: Pricing) -> Pricing:
     for many models (most DeepSeek entries, openai/gpt-4o, ...). Without a
     cache rate, billing falls back to the full input rate, which overcharges
     cache reads (DeepSeek hits are 10x cheaper) and undercharges Anthropic
-    cache writes (1.25x). litellm ships per-model USD rates keyed by the exact
-    OpenRouter id (deepseek/deepseek-chat) or by the bare model name
-    (gpt-4o, claude-sonnet-4-5), so both spellings are tried. litellm keys are
-    lowercase, but a generic upstream may report a mixed-case id
-    (``deepseek-ai/DeepSeek-V4-Flash``); an exact match is attempted first, then
-    a case-insensitive fallback so such ids still resolve.
+    cache writes (1.25x). The lookup (see ``litellm_cost_entry``) tries both
+    id spellings and a case-insensitive fallback.
 
     Rates already present (e.g. provided by OpenRouter) are authoritative and
     never overwritten. Unknown models are returned unchanged.
@@ -107,28 +127,7 @@ def backfill_cache_pricing(model_id: str, pricing: Pricing) -> Pricing:
     if not (needs_read or needs_write):
         return pricing
 
-    import litellm
-
-    candidates = (model_id, model_id.split("/", 1)[-1])
-    info: dict | None = None
-    for key in candidates:
-        candidate = litellm.model_cost.get(key)
-        if isinstance(candidate, dict):
-            info = candidate
-            break
-    if info is None:
-        # Case-insensitive fallback: a mixed-case upstream id (e.g.
-        # ``deepseek-ai/DeepSeek-V4-Flash``) won't match litellm's lowercase
-        # keys exactly. Build a lowercased index once and retry.
-        lowered = {c.lower() for c in candidates}
-        for key, candidate in litellm.model_cost.items():
-            if (
-                isinstance(key, str)
-                and key.lower() in lowered
-                and isinstance(candidate, dict)
-            ):
-                info = candidate
-                break
+    info = litellm_cost_entry(model_id)
     if info is None:
         return pricing
 

routstr/upstream/generic.py

@@ -5,6 +5,12 @@ from typing import TYPE_CHECKING
 import httpx
 
 from .base import BaseUpstreamProvider
+from .pricing_resolver import (
+    FallbackPricingResolver,
+    ResolvedPricing,
+    _as_float,
+    estimate_context_length,
+)
 
 if TYPE_CHECKING:
     from ..core.db import UpstreamProviderRow
@@ -64,6 +70,40 @@ class GenericUpstreamProvider(BaseUpstreamProvider):
             "platform_url": cls.platform_url,
         }
 
+    def _native_pricing(
+        self, model_id: str, model_spec: dict
+    ) -> ResolvedPricing | None:
+        """Read pricing/metadata from Venice's bespoke ``model_spec`` schema.
+
+        Returns ``None`` when the upstream reported no *usable* native price —
+        absent, non-numeric, negative, or both-zero — so the caller falls
+        through to the shared resolution chain instead of fabricating a number
+        or trusting a bogus one. This mirrors the money-safety guards the
+        litellm and OpenRouter rungs already apply: a both-zero price would
+        serve the model free, a negative one would credit the caller, and a
+        non-numeric string would otherwise throw and drop the whole catalog.
+        """
+        pricing_info = model_spec.get("pricing", {})
+        input_usd = _as_float(pricing_info.get("input", {}).get("usd"))
+        output_usd = _as_float(pricing_info.get("output", {}).get("usd"))
+        if input_usd is None or output_usd is None:
+            return None
+        if input_usd < 0 or output_usd < 0 or (input_usd == 0 and output_usd == 0):
+            return None
+
+        capabilities = model_spec.get("capabilities", {})
+        input_modalities = ["text"]
+        if capabilities.get("supportsVision", False):
+            input_modalities.append("image")
+
+        return ResolvedPricing(
+            prompt=input_usd / 1_000_000,
+            completion=output_usd / 1_000_000,
+            context_length=model_spec.get("availableContextTokens"),
+            source="native",
+            input_modalities=input_modalities,
+        )
+
     async def fetch_models(self) -> list[Model]:
         """Fetch models from upstream API using /models endpoint."""
         from ..payment.models import Architecture, Model, Pricing, TopProvider
@@ -78,6 +118,7 @@ class GenericUpstreamProvider(BaseUpstreamProvider):
                 response.raise_for_status()
                 data = response.json()
 
+                resolver = FallbackPricingResolver()
                 models_list = []
                 for model_data in data.get("data", []):
                     model_id = model_data.get("id", "")
@@ -89,41 +130,44 @@ class GenericUpstreamProvider(BaseUpstreamProvider):
                     owned_by = model_data.get("owned_by", "unknown")
                     model_spec = model_data.get("model_spec", {})
 
-                    context_length = 4096
-                    if model_spec.get("availableContextTokens"):
-                        context_length = model_spec["availableContextTokens"]
-                    elif any(
-                        pattern in model_id.lower() for pattern in ["32k", "32000"]
-                    ):
-                        context_length = 32768
-                    elif any(
-                        pattern in model_id.lower() for pattern in ["16k", "16000"]
-                    ):
-                        context_length = 16384
-                    elif any(pattern in model_id.lower() for pattern in ["8k", "8000"]):
-                        context_length = 8192
-                    elif "gpt-4" in model_id.lower():
-                        context_length = 8192
-                    elif "claude" in model_id.lower():
-                        context_length = 200000
+                    resolved = self._native_pricing(model_id, model_spec)
+                    if resolved is None:
+                        resolved = await resolver.resolve(model_id)
 
-                    pricing_info = model_spec.get("pricing", {})
-                    input_pricing = pricing_info.get("input", {})
-                    output_pricing = pricing_info.get("output", {})
+                    if resolved is None:
+                        # Fail closed: never invent a price. Import the model
+                        # disabled with a warning so the operator can price it
+                        # (the admin UI surfaces disabled remote models).
+                        logger.warning(
+                            f"No pricing source resolved for '{model_id}' from "
+                            f"{self.upstream_name}; importing it disabled",
+                            extra={"model_id": model_id, "base_url": self.base_url},
+                        )
+                        resolved = ResolvedPricing(
+                            prompt=0.0,
+                            completion=0.0,
+                            context_length=None,
+                            source="unresolved",
+                        )
+                        enabled = False
+                    else:
+                        enabled = True
 
-                    prompt_price = input_pricing.get("usd", 0.001) / 1000000
-                    completion_price = output_pricing.get("usd", 0.001) / 1000000
+                    # Prefer the source's own modality string (OpenRouter ships
+                    # one, e.g. "text+image->text"); otherwise derive it from the
+                    # captured input/output modalities in the same "in->out" shape
+                    # rather than flattening vision models to "text->text".
+                    modality = resolved.modality or (
+                        f"{'+'.join(resolved.input_modalities)}"
+                        f"->{'+'.join(resolved.output_modalities)}"
+                    )
 
-                    capabilities = model_spec.get("capabilities", {})
-                    input_modalities = ["text"]
-                    output_modalities = ["text"]
-
-                    if capabilities.get("supportsVision", False):
-                        input_modalities.append("image")
-
-                    modality = "text"
-                    if capabilities.get("supportsVision", False):
-                        modality = "text->text"
+                    # A source can carry a price but no context (e.g. a litellm
+                    # entry missing max_input_tokens); fall back to an id-based
+                    # estimate so we never persist a zero-length window.
+                    context_length = resolved.context_length or estimate_context_length(
+                        model_id
+                    )
 
                     spec_name = model_spec.get("name", model_name)
                     description = f"{spec_name}"
@@ -139,30 +183,33 @@ class GenericUpstreamProvider(BaseUpstreamProvider):
                             context_length=context_length,
                             architecture=Architecture(
                                 modality=modality,
-                                input_modalities=input_modalities,
-                                output_modalities=output_modalities,
-                                tokenizer="unknown",
-                                instruct_type=None,
+                                input_modalities=resolved.input_modalities,
+                                output_modalities=resolved.output_modalities,
+                                tokenizer=resolved.tokenizer,
+                                instruct_type=resolved.instruct_type,
                             ),
                             pricing=Pricing(
-                                prompt=prompt_price,
-                                completion=completion_price,
+                                prompt=resolved.prompt,
+                                completion=resolved.completion,
                                 request=0.0,
                                 image=0.0,
                                 web_search=0.0,
                                 internal_reasoning=0.0,
-                                max_prompt_cost=0.001,
-                                max_completion_cost=0.001,
-                                max_cost=0.001,
+                                input_cache_read=resolved.input_cache_read,
+                                input_cache_write=resolved.input_cache_write,
                             ),
                             sats_pricing=None,
                             per_request_limits=None,
                             top_provider=TopProvider(
                                 context_length=context_length,
-                                max_completion_tokens=context_length // 2,
-                                is_moderated=False,
+                                max_completion_tokens=(
+                                    resolved.max_completion_tokens
+                                    if resolved.max_completion_tokens is not None
+                                    else context_length // 2
+                                ),
+                                is_moderated=bool(resolved.is_moderated),
                             ),
-                            enabled=True,
+                            enabled=enabled,
                             upstream_provider_id=None,
                             canonical_slug=None,
                         )

routstr/upstream/openrouter.py

@@ -1,4 +1,3 @@
-import json
 from typing import TYPE_CHECKING
 
 import httpx
@@ -19,38 +18,6 @@ class OpenRouterUpstreamProvider(BaseUpstreamProvider):
     supports_anthropic_messages = True
     litellm_provider_prefix = "openrouter/"
 
-    def prepare_request_body(
-        self, body: bytes | None, model_obj: Model
-    ) -> bytes | None:
-        """Set provider.require_parameters on tool-use requests.
-
-        Without it OpenRouter can route a tool call to an endpoint that doesn't
-        support function calling and 404 with "No endpoints found that support
-        tool use". We leave a client-supplied value untouched.
-        """
-        body = super().prepare_request_body(body, model_obj)
-        if not body:
-            return body
-
-        try:
-            data = json.loads(body)
-        except json.JSONDecodeError:
-            return body
-
-        if not isinstance(data, dict) or not data.get("tools"):
-            return body
-
-        provider = data.get("provider")
-        if not isinstance(provider, dict):
-            provider = {}
-
-        if "require_parameters" in provider:
-            return body
-
-        provider["require_parameters"] = True
-        data["provider"] = provider
-        return json.dumps(data).encode()
-
     def _apply_provider_field(self, response_json: object) -> None:
         """Stamp the ``provider`` field for OpenRouter responses.
 

routstr/upstream/pricing_resolver.py

@@ -0,0 +1,203 @@
+"""Shared price/metadata resolution chain for upstream model discovery.
+
+Most OpenAI-compatible ``/models`` responses carry no pricing. Rather than let
+a provider fabricate one, this module resolves a model through decreasingly
+trustworthy sources — litellm's bundled cost map (curated list prices, mirrors
+provider docs), then the OpenRouter feed (resale prices, broader coverage) —
+and returns ``None`` when none of them know the model, so the caller can fail
+closed instead of inventing a number.
+
+Provider-native pricing (a gateway's own ``/models`` schema, e.g. Venice's
+``model_spec``) is authoritative and handled by the provider before this chain
+is consulted; only the shared fallback lives here so a later refactor can hoist
+it into the base provider unchanged.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class ResolvedPricing:
+    """Per-token pricing plus whatever metadata the answering source carried.
+
+    Prices are USD per token. ``source`` records provenance
+    (``native``/``litellm``/``openrouter``/``unresolved``) so later work can
+    surface where each price came from.
+    """
+
+    prompt: float
+    completion: float
+    context_length: int | None
+    source: str
+    modality: str | None = None
+    max_completion_tokens: int | None = None
+    input_cache_read: float = 0.0
+    input_cache_write: float = 0.0
+    input_modalities: list[str] = field(default_factory=lambda: ["text"])
+    output_modalities: list[str] = field(default_factory=lambda: ["text"])
+    tokenizer: str = "unknown"
+    instruct_type: str | None = None
+    is_moderated: bool | None = None
+
+
+def estimate_context_length(model_id: str) -> int:
+    """Best-effort context window from a model id when no source reports one.
+
+    The last rung of the fallback chain, reached only for a model whose price
+    resolved but whose context did not (or that imported disabled). Context is
+    not a billing input, so a rough id-based guess is acceptable here where a
+    guessed *price* never would be.
+    """
+    lowered = model_id.lower()
+    if any(pattern in lowered for pattern in ["32k", "32000"]):
+        return 32768
+    if any(pattern in lowered for pattern in ["16k", "16000"]):
+        return 16384
+    if any(pattern in lowered for pattern in ["8k", "8000"]):
+        return 8192
+    if "gpt-4" in lowered:
+        return 8192
+    if "claude" in lowered:
+        return 200000
+    return 4096
+
+
+def _as_float(value: object) -> float | None:
+    """OpenRouter reports prices as strings; coerce, ``None`` if unparseable."""
+    try:
+        return float(value)  # type: ignore[arg-type]
+    except (TypeError, ValueError):
+        return None
+
+
+def _as_int(value: object) -> int | None:
+    """Coerce an already-numeric token count to ``int``, else ``None``."""
+    return int(value) if isinstance(value, (int, float)) else None
+
+
+def _from_litellm(model_id: str) -> ResolvedPricing | None:
+    # Lazy import so the resolver stays import-light and shares the exact
+    # lookup semantics used by cache-rate backfill.
+    from ..payment.models import litellm_cost_entry
+
+    info = litellm_cost_entry(model_id)
+    if info is None:
+        return None
+
+    prompt = info.get("input_cost_per_token")
+    completion = info.get("output_cost_per_token")
+    if not isinstance(prompt, (int, float)) or not isinstance(completion, (int, float)):
+        return None
+    # A both-zero entry is litellm listing a model without a real price (free
+    # moderation/rerank tiers do this) — treating 0/0 as resolved would serve
+    # the model for free. Reject it (and any negative) so the caller falls
+    # through, mirroring async_fetch_openrouter_models' _has_valid_pricing.
+    if prompt < 0 or completion < 0 or (prompt == 0 and completion == 0):
+        return None
+
+    input_modalities = ["text"]
+    if info.get("supports_vision"):
+        input_modalities.append("image")
+
+    return ResolvedPricing(
+        prompt=float(prompt),
+        completion=float(completion),
+        # max_input_tokens is the context window; max_tokens is litellm's
+        # completion cap (it tracks max_output_tokens for ~94% of models), so
+        # it is never a context source. A missing window falls to the id-based
+        # estimate downstream rather than borrowing the output cap.
+        context_length=_as_int(info.get("max_input_tokens")),
+        source="litellm",
+        max_completion_tokens=_as_int(info.get("max_output_tokens")),
+        input_cache_read=float(info.get("cache_read_input_token_cost") or 0.0),
+        input_cache_write=float(info.get("cache_creation_input_token_cost") or 0.0),
+        input_modalities=input_modalities,
+    )
+
+
+def _match_openrouter(model_id: str, feed: list[dict]) -> dict | None:
+    """Find ``model_id`` in the OpenRouter feed, exact id before bare tail.
+
+    Bare-tail matching (``deepseek-chat`` ↔ ``deepseek/deepseek-chat``) is a
+    looser, lower-trust match — OpenRouter fans a model out across resellers —
+    so an exact id match always wins first. When several entries share the bare
+    tail, the one with the highest *combined* (prompt + completion) per-token
+    cost wins: the choice must be deterministic (not feed-order-dependent) and
+    money-safe whichever way traffic leans, since undercharging is the hazard.
+    Ranking on prompt alone could pick an entry that is cheap on input but dear
+    on output. The live feed has no such collisions today; this only governs
+    the latent case.
+    """
+    bare = model_id.split("/", 1)[-1]
+    exact = next((m for m in feed if m.get("id") == model_id), None)
+    if exact is not None:
+        return exact
+    matches = [m for m in feed if m.get("id", "").split("/", 1)[-1] == bare]
+    if not matches:
+        return None
+
+    def _combined_cost(m: dict) -> float:
+        pricing = m.get("pricing", {})
+        return (_as_float(pricing.get("prompt")) or 0.0) + (
+            _as_float(pricing.get("completion")) or 0.0
+        )
+
+    return max(matches, key=_combined_cost)
+
+
+def _from_openrouter(model_id: str, feed: list[dict]) -> ResolvedPricing | None:
+    entry = _match_openrouter(model_id, feed)
+    if entry is None:
+        return None
+
+    pricing = entry.get("pricing", {})
+    prompt = _as_float(pricing.get("prompt"))
+    completion = _as_float(pricing.get("completion"))
+    if prompt is None or completion is None:
+        return None
+
+    architecture = entry.get("architecture", {})
+    top_provider = entry.get("top_provider", {})
+
+    return ResolvedPricing(
+        prompt=prompt,
+        completion=completion,
+        context_length=_as_int(entry.get("context_length")),
+        source="openrouter",
+        modality=architecture.get("modality"),
+        max_completion_tokens=_as_int(top_provider.get("max_completion_tokens")),
+        input_cache_read=_as_float(pricing.get("input_cache_read")) or 0.0,
+        input_cache_write=_as_float(pricing.get("input_cache_write")) or 0.0,
+        input_modalities=architecture.get("input_modalities") or ["text"],
+        output_modalities=architecture.get("output_modalities") or ["text"],
+        tokenizer=architecture.get("tokenizer") or "unknown",
+        instruct_type=architecture.get("instruct_type"),
+        is_moderated=top_provider.get("is_moderated"),
+    )
+
+
+class FallbackPricingResolver:
+    """Resolves models via litellm → OpenRouter for one discovery pass.
+
+    The OpenRouter catalog is fetched at most once and only when a model
+    actually misses litellm, so a provider full of litellm-known models never
+    touches the network. Instantiate one per ``fetch_models`` call.
+    """
+
+    def __init__(self) -> None:
+        self._openrouter_feed: list[dict] | None = None
+
+    async def resolve(self, model_id: str) -> ResolvedPricing | None:
+        """Resolve ``model_id``; ``None`` if no source knows it."""
+        resolved = _from_litellm(model_id)
+        if resolved is not None:
+            return resolved
+
+        if self._openrouter_feed is None:
+            # Lazy import so tests can patch the feed at its source.
+            from ..payment.models import async_fetch_openrouter_models
+
+            self._openrouter_feed = await async_fetch_openrouter_models()
+        return _from_openrouter(model_id, self._openrouter_feed)

tests/integration/test_admin_model_cache_pricing.py

@@ -0,0 +1,229 @@
+import json
+from datetime import datetime, timedelta, timezone
+from unittest.mock import patch
+
+import pytest
+from httpx import AsyncClient
+from sqlmodel.ext.asyncio.session import AsyncSession
+
+from routstr.core.admin import admin_sessions
+from routstr.core.db import ModelRow, UpstreamProviderRow
+from routstr.payment.cost_calculation import CostData, calculate_cost
+from routstr.proxy import get_model_instance, reinitialize_upstreams
+
+
+def _admin_headers() -> dict[str, str]:
+    token = "test-admin-cache-pricing-token"
+    admin_sessions[token] = int(
+        (datetime.now(timezone.utc) + timedelta(minutes=5)).timestamp()
+    )
+    return {"Authorization": f"Bearer {token}"}
+
+
+def _model_payload(
+    provider_id: int,
+    *,
+    cache_read: float,
+    cache_write: float,
+) -> dict[str, object]:
+    return {
+        "id": "custom-cache-model",
+        "name": "Custom Cache Model",
+        "description": "custom model with explicit cache pricing",
+        "created": 0,
+        "context_length": 128000,
+        "architecture": {
+            "modality": "text",
+            "input_modalities": ["text"],
+            "output_modalities": ["text"],
+            "tokenizer": "unknown",
+            "instruct_type": None,
+        },
+        "pricing": {
+            "prompt": 1.4e-7,
+            "completion": 2.8e-7,
+            "input_cache_read": cache_read,
+            "input_cache_write": cache_write,
+            "request": 0.0,
+            "image": 0.0,
+            "web_search": 0.0,
+            "internal_reasoning": 0.0,
+        },
+        "per_request_limits": None,
+        "top_provider": None,
+        "upstream_provider_id": provider_id,
+        "canonical_slug": None,
+        "alias_ids": [],
+        "enabled": True,
+        "forwarded_model_id": "custom-cache-model",
+    }
+
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_admin_provider_model_api_persists_cache_pricing_on_create_and_update(
+    integration_client: AsyncClient,
+    integration_session: AsyncSession,
+) -> None:
+    provider = UpstreamProviderRow(
+        provider_type="generic",
+        base_url="https://custom-upstream.example/v1",
+        api_key="test-key",
+        provider_fee=1.0,
+    )
+    integration_session.add(provider)
+    await integration_session.commit()
+    await integration_session.refresh(provider)
+    assert provider.id is not None
+    await reinitialize_upstreams()
+
+    headers = _admin_headers()
+    create_payload = _model_payload(
+        provider.id,
+        cache_read=2.8e-9,
+        cache_write=3.5e-9,
+    )
+
+    with patch("routstr.payment.models.sats_usd_price", return_value=1e-6):
+        create_response = await integration_client.post(
+            f"/admin/api/upstream-providers/{provider.id}/models",
+            headers=headers,
+            json=create_payload,
+        )
+
+    assert create_response.status_code == 200
+    create_body = create_response.json()
+    assert create_body["pricing"]["input_cache_read"] == pytest.approx(2.8e-9)
+    assert create_body["pricing"]["input_cache_write"] == pytest.approx(3.5e-9)
+
+    row = await integration_session.get(ModelRow, ("custom-cache-model", provider.id))
+    assert row is not None
+    stored_pricing = json.loads(row.pricing)
+    assert stored_pricing["input_cache_read"] == pytest.approx(2.8e-9)
+    assert stored_pricing["input_cache_write"] == pytest.approx(3.5e-9)
+
+    update_payload = _model_payload(
+        provider.id,
+        cache_read=1.25e-9,
+        cache_write=4.5e-9,
+    )
+
+    with patch("routstr.payment.models.sats_usd_price", return_value=1e-6):
+        update_response = await integration_client.post(
+            f"/admin/api/upstream-providers/{provider.id}/models",
+            headers=headers,
+            json=update_payload,
+        )
+
+    assert update_response.status_code == 200
+    update_body = update_response.json()
+    assert update_body["pricing"]["input_cache_read"] == pytest.approx(1.25e-9)
+    assert update_body["pricing"]["input_cache_write"] == pytest.approx(4.5e-9)
+
+    await integration_session.refresh(row)
+    updated_pricing = json.loads(row.pricing)
+    assert updated_pricing["input_cache_read"] == pytest.approx(1.25e-9)
+    assert updated_pricing["input_cache_write"] == pytest.approx(4.5e-9)
+
+    model = get_model_instance("custom-cache-model")
+    assert model is not None
+    assert model.sats_pricing is not None
+    assert model.sats_pricing.input_cache_read == pytest.approx(0.00125)
+    assert model.sats_pricing.input_cache_write == pytest.approx(0.0045)
+
+    with patch("routstr.payment.cost_calculation.sats_usd_price", return_value=1e-6):
+        cost = await calculate_cost(
+            {
+                "model": "custom-cache-model",
+                "usage": {
+                    "prompt_tokens": 1000,
+                    "completion_tokens": 100,
+                    "prompt_tokens_details": {"cached_tokens": 800},
+                },
+            },
+            max_cost=1_000_000,
+        )
+
+    assert isinstance(cost, CostData)
+    assert cost.input_tokens == 200
+    assert cost.cache_read_input_tokens == 800
+    assert cost.cache_read_msats == 1000
+    assert cost.output_msats == 28000
+    assert cost.input_msats == 29000
+    assert cost.total_msats == 57000
+
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+async def test_upstream_response_cost_uses_model_cache_pricing(
+    integration_session: AsyncSession,
+    patched_db_engine: None,
+) -> None:
+    """A model's configured cache price must discount upstream cached-token usage."""
+    provider = UpstreamProviderRow(
+        provider_type="generic",
+        base_url="https://cache-priced-upstream.example/v1",
+        api_key="test-key",
+        provider_fee=1.0,
+    )
+    integration_session.add(provider)
+    await integration_session.commit()
+    await integration_session.refresh(provider)
+    assert provider.id is not None
+
+    row = ModelRow(
+        id="cache-priced-model",
+        name="Cache Priced Model",
+        description="model seeded with explicit cache pricing",
+        created=0,
+        context_length=128000,
+        architecture=json.dumps(
+            {
+                "modality": "text",
+                "input_modalities": ["text"],
+                "output_modalities": ["text"],
+                "tokenizer": "unknown",
+                "instruct_type": None,
+            }
+        ),
+        pricing=json.dumps(
+            {
+                "prompt": 1.4e-7,
+                "completion": 2.8e-7,
+                "input_cache_read": 1.25e-9,
+                "input_cache_write": 4.5e-9,
+            }
+        ),
+        upstream_provider_id=provider.id,
+        enabled=True,
+        forwarded_model_id="cache-priced-model",
+    )
+    integration_session.add(row)
+    await integration_session.commit()
+
+    with patch("routstr.payment.models.sats_usd_price", return_value=1e-6):
+        await reinitialize_upstreams()
+
+    with patch("routstr.payment.cost_calculation.sats_usd_price", return_value=1e-6):
+        cost = await calculate_cost(
+            {
+                "model": "cache-priced-model",
+                "usage": {
+                    "prompt_tokens": 1000,
+                    "completion_tokens": 100,
+                    "prompt_tokens_details": {"cached_tokens": 800},
+                },
+            },
+            max_cost=1_000_000,
+        )
+
+    assert isinstance(cost, CostData)
+    # prompt 1.4e-7 USD/token -> 0.14 sats/token -> 140 msats/token
+    # cache 1.25e-9 USD/token -> 0.00125 sats/token -> 1.25 msats/token
+    # completion 2.8e-7 USD/token -> 0.28 sats/token -> 280 msats/token
+    assert cost.input_tokens == 200
+    assert cost.cache_read_input_tokens == 800
+    assert cost.cache_read_msats == 1000
+    assert cost.input_msats == 29000
+    assert cost.output_msats == 28000
+    assert cost.total_msats == 57000

tests/unit/test_balance.py

@@ -104,6 +104,64 @@ async def test_refund_x_cashu_not_found_raises_404() -> None:
     assert exc_info.value.status_code == 404
 
 
+@pytest.mark.asyncio
+async def test_refund_x_cashu_pending_raises_425() -> None:
+    """in row exists with a request_id but out row not yet created → 425.
+
+    This is the race condition where /v1/wallet/refund is polled while the
+    upstream request is still in flight. The endpoint must signal "retry"
+    rather than a permanent 404.
+    """
+    from fastapi import HTTPException
+
+    x_cashu_token = "cashuApending_token"
+    in_tx = _make_cashu_tx(
+        token=x_cashu_token, amount=0, unit="msat", type="in", request_id="req-pending"
+    )
+
+    session = MagicMock()
+    session.exec = AsyncMock(side_effect=[_exec_result(in_tx), _exec_result(None)])
+    session.add = MagicMock()
+    session.commit = AsyncMock()
+
+    with pytest.raises(HTTPException) as exc_info:
+        await refund_wallet_endpoint(
+            authorization="Bearer sk-somekey",
+            x_cashu=x_cashu_token,
+            session=session,
+        )
+
+    assert exc_info.value.status_code == 425
+    assert exc_info.value.headers == {"Retry-After": "2"}
+
+
+@pytest.mark.asyncio
+async def test_refund_x_cashu_in_tx_without_request_id_raises_404() -> None:
+    """in row exists but has no request_id (cannot link to a refund) → 404.
+
+    This is a genuine "no refund will ever exist" case, distinct from the
+    pending 425 path.
+    """
+    from fastapi import HTTPException
+
+    x_cashu_token = "cashuAnoreqid_token"
+    in_tx = _make_cashu_tx(
+        token=x_cashu_token, amount=0, unit="msat", type="in", request_id=None
+    )
+
+    session = MagicMock()
+    session.exec = AsyncMock(side_effect=[_exec_result(in_tx)])
+
+    with pytest.raises(HTTPException) as exc_info:
+        await refund_wallet_endpoint(
+            authorization="Bearer sk-somekey",
+            x_cashu=x_cashu_token,
+            session=session,
+        )
+
+    assert exc_info.value.status_code == 404
+
+
 @pytest.mark.asyncio
 async def test_refund_x_cashu_swept_raises_410() -> None:
     from fastapi import HTTPException

tests/unit/test_cost_calculation_caching.py

@@ -404,6 +404,67 @@ async def test_deepseek_malformed_hit_tokens_coerce_to_zero() -> None:
     assert result.cache_read_input_tokens == 0
 
 
+# ============================================================================
+# Truly-empty response with a non-zero USD cost → full refund
+#
+# When an upstream reports a USD cost but the response carries NO tokens at all
+# (input, output, cache-read and cache-creation all zero), billing the
+# USD-derived cost charges the user for nothing. Refund in full. The gate is
+# tightened relative to PR #489: a cache-read/-creation-only turn legitimately
+# reports zero prompt/completion tokens with a real cost and must still bill.
+# ============================================================================
+@pytest.mark.asyncio
+async def test_truly_empty_usd_cost_response_is_refunded(
+    mock_fixed_pricing: None,
+) -> None:
+    """0 input + 0 output + 0 cache tokens with a non-zero USD cost → refund."""
+    response = {
+        "model": "gpt-4",
+        "usage": {
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "total_cost": 0.01,  # non-zero USD cost despite no tokens
+        },
+    }
+    result = await calculate_cost(response, max_cost=100000)
+
+    assert isinstance(result, CostData)
+    assert result.total_msats == 0  # full refund
+    assert result.input_msats == 0
+    assert result.output_msats == 0
+    assert result.total_usd == 0.0
+    assert result.input_tokens == 0
+    assert result.output_tokens == 0
+    assert result.cache_read_input_tokens == 0
+    assert result.cache_creation_input_tokens == 0
+
+
+@pytest.mark.asyncio
+async def test_cache_read_only_usd_cost_response_is_billed(
+    mock_fixed_pricing: None,
+) -> None:
+    """Cache-read-only turn (0 prompt/completion, non-zero cost) still bills."""
+    response = {
+        "model": "claude-3-5-sonnet",
+        "usage": {
+            "input_tokens": 0,
+            "output_tokens": 0,
+            "cache_read_input_tokens": 1000,  # real cached usage
+            "cache_creation_input_tokens": 0,
+            "total_cost": 0.01,  # non-zero USD cost
+        },
+    }
+    result = await calculate_cost(response, max_cost=100000)
+
+    assert isinstance(result, CostData)
+    # NOT refunded — the USD cost is billed in full. Pinning the exact value
+    # guards against any future regression that would over-refund a cache-only
+    # turn (the bug in PR #489, which refunded whenever prompt+completion == 0).
+    assert result.total_msats == 200000
+    assert result.total_usd == 0.01
+    assert result.cache_read_input_tokens == 1000
+
+
 # ============================================================================
 # Test 13: Missing Usage Block
 # ============================================================================

tests/unit/test_require_parameters.py

@@ -1,95 +0,0 @@
-import json
-import os
-
-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.upstream import GenericUpstreamProvider
-from routstr.upstream.openrouter import OpenRouterUpstreamProvider
-
-
-def _model(model_id: str = "openai/gpt-4o"):  # type: ignore[no-untyped-def]
-    from routstr.payment.models import Architecture, Model, Pricing
-
-    return Model(
-        id=model_id,
-        name=model_id,
-        created=0,
-        description="",
-        context_length=128000,
-        architecture=Architecture(
-            modality="text->text",
-            input_modalities=["text"],
-            output_modalities=["text"],
-            tokenizer="GPT",
-            instruct_type=None,
-        ),
-        pricing=Pricing(prompt=0.0, completion=0.0),
-    )
-
-
-def _tool_body() -> dict:
-    return {
-        "model": "openai/gpt-4o",
-        "messages": [{"role": "user", "content": "What's the weather?"}],
-        "tools": [
-            {
-                "type": "function",
-                "function": {"name": "get_weather", "parameters": {}},
-            }
-        ],
-    }
-
-
-def _prepare(provider, body: dict) -> dict:  # type: ignore[no-untyped-def]
-    out = provider.prepare_request_body(json.dumps(body).encode(), _model())
-    assert out is not None
-    return json.loads(out)
-
-
-def test_injects_require_parameters_for_tool_request() -> None:
-    data = _prepare(OpenRouterUpstreamProvider(api_key="test"), _tool_body())
-    assert data["provider"]["require_parameters"] is True
-
-
-def test_generic_provider_on_openrouter_url_is_left_alone() -> None:
-    # Only OpenRouterUpstreamProvider injects; a generic provider pointed at the
-    # same base URL doesn't.
-    provider = GenericUpstreamProvider(base_url="https://openrouter.ai/api/v1")
-    data = _prepare(provider, _tool_body())
-    assert "provider" not in data
-
-
-def test_no_injection_without_tools() -> None:
-    body = {"model": "openai/gpt-4o", "messages": [{"role": "user", "content": "hi"}]}
-    data = _prepare(OpenRouterUpstreamProvider(api_key="test"), body)
-    assert "provider" not in data
-
-
-def test_empty_tools_list_does_not_inject() -> None:
-    body = _tool_body()
-    body["tools"] = []
-    data = _prepare(OpenRouterUpstreamProvider(api_key="test"), body)
-    assert "provider" not in data
-
-
-def test_direct_provider_does_not_inject() -> None:
-    provider = GenericUpstreamProvider(base_url="https://api.openai.com/v1")
-    data = _prepare(provider, _tool_body())
-    assert "provider" not in data
-
-
-def test_keeps_client_set_require_parameters() -> None:
-    body = _tool_body()
-    body["provider"] = {"require_parameters": False}
-    data = _prepare(OpenRouterUpstreamProvider(api_key="test"), body)
-    assert data["provider"]["require_parameters"] is False
-
-
-def test_preserves_other_provider_fields() -> None:
-    body = _tool_body()
-    body["provider"] = {"order": ["openai", "azure"]}
-    data = _prepare(OpenRouterUpstreamProvider(api_key="test"), body)
-    assert data["provider"]["order"] == ["openai", "azure"]
-    assert data["provider"]["require_parameters"] is True

tests/unit/test_upstream_generic.py

@@ -0,0 +1,524 @@
+"""Unit tests for ``GenericUpstreamProvider.fetch_models`` price/metadata resolution.
+
+A generic upstream is any OpenAI-compatible API. Most (DeepSeek, OpenAI,
+Groq, ...) answer ``/models`` with bare ``{id, object, owned_by}`` entries that
+carry *no* pricing. The provider must not fabricate a price for those: it
+resolves through native ``model_spec`` (Venice's bespoke schema) → litellm's
+bundled cost map → the OpenRouter feed, and only when every source misses does
+it import the model **disabled** with a warning rather than invent a number.
+
+These tests drive that behaviour through the public ``fetch_models`` API. The
+``/models`` HTTP call is faked at ``httpx.AsyncClient``; the OpenRouter feed is
+patched at its source (``routstr.payment.models.async_fetch_openrouter_models``)
+so the resolver's lazy import picks up the stub. litellm's real bundled cost map
+is used unmocked — the DeepSeek rates it ships are the assertion's ground truth.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from routstr.upstream.generic import GenericUpstreamProvider
+
+
+class _FakeResponse:
+    def __init__(self, payload: dict[str, Any]) -> None:
+        self._payload = payload
+
+    def raise_for_status(self) -> None:
+        return None
+
+    def json(self) -> dict[str, Any]:
+        return self._payload
+
+
+class _FakeAsyncClient:
+    """Stand-in for ``httpx.AsyncClient`` returning a canned ``/models`` body."""
+
+    def __init__(self, payload: dict[str, Any]) -> None:
+        self._payload = payload
+
+    async def __aenter__(self) -> "_FakeAsyncClient":
+        return self
+
+    async def __aexit__(self, *exc: object) -> bool:
+        return False
+
+    async def get(self, url: str, headers: dict[str, str] | None = None) -> _FakeResponse:
+        return _FakeResponse(self._payload)
+
+
+def _patch_models_endpoint(payload: dict[str, Any]) -> Any:
+    """Patch the provider's ``httpx.AsyncClient`` to serve ``payload``."""
+    return patch(
+        "routstr.upstream.generic.httpx.AsyncClient",
+        lambda *args, **kwargs: _FakeAsyncClient(payload),
+    )
+
+
+def _model_by_id(models: list[Any], model_id: str) -> Any:
+    return next(m for m in models if m.id == model_id)
+
+
+# ---------------------------------------------------------------------------
+# native model_spec (Venice) — must keep resolving, and capture its metadata
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_native_model_spec_resolves_and_captures_metadata() -> None:
+    """A Venice-shaped ``model_spec`` is authoritative: prices/context come
+    straight from it and vision capability becomes an image input modality."""
+    payload = {
+        "data": [
+            {
+                "id": "venice-llama",
+                "owned_by": "venice",
+                "model_spec": {
+                    "name": "Venice Llama",
+                    "availableContextTokens": 65536,
+                    "pricing": {
+                        "input": {"usd": 0.5},
+                        "output": {"usd": 1.5},
+                    },
+                    "capabilities": {"supportsVision": True},
+                },
+            }
+        ]
+    }
+
+    with _patch_models_endpoint(payload):
+        or_feed = AsyncMock(return_value=[])
+        with patch(
+            "routstr.payment.models.async_fetch_openrouter_models", or_feed
+        ):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    model = _model_by_id(models, "venice-llama")
+    assert model.enabled is True
+    assert model.pricing.prompt == pytest.approx(0.5 / 1_000_000)
+    assert model.pricing.completion == pytest.approx(1.5 / 1_000_000)
+    assert model.context_length == 65536
+    assert "image" in model.architecture.input_modalities
+    # Vision capability must be reflected in the combined modality string, not
+    # flattened to "text->text".
+    assert model.architecture.modality == "text+image->text"
+    # A native price never needs the OpenRouter feed.
+    or_feed.assert_not_awaited()
+
+
+# ---------------------------------------------------------------------------
+# native model_spec validation — a bogus native price is not authoritative
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_native_both_zero_price_falls_through_to_litellm() -> None:
+    """A native ``model_spec`` that prices both tokens at 0 is not a real price
+    (the same free-tier trap the litellm/OpenRouter rungs already reject). It
+    must not be treated as authoritative and served free; the resolver falls
+    through, so a litellm-known model lands on litellm's real rate instead."""
+    payload = {
+        "data": [
+            {
+                "id": "deepseek-chat",
+                "owned_by": "deepseek",
+                "model_spec": {
+                    "pricing": {"input": {"usd": 0}, "output": {"usd": 0}},
+                },
+            }
+        ]
+    }
+
+    with _patch_models_endpoint(payload):
+        or_feed = AsyncMock(return_value=[])
+        with patch("routstr.payment.models.async_fetch_openrouter_models", or_feed):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    model = _model_by_id(models, "deepseek-chat")
+    assert model.enabled is True
+    assert model.pricing.prompt == pytest.approx(2.8e-07)
+    assert model.pricing.completion == pytest.approx(4.2e-07)
+
+
+@pytest.mark.asyncio
+async def test_native_negative_price_falls_through_to_litellm() -> None:
+    """A negative native price would credit the caller's balance on every
+    request (a fund drain, not a discount). Reject it like any other unusable
+    price and fall through to the chain."""
+    payload = {
+        "data": [
+            {
+                "id": "deepseek-chat",
+                "owned_by": "deepseek",
+                "model_spec": {
+                    "pricing": {"input": {"usd": -0.5}, "output": {"usd": -1.5}},
+                },
+            }
+        ]
+    }
+
+    with _patch_models_endpoint(payload):
+        or_feed = AsyncMock(return_value=[])
+        with patch("routstr.payment.models.async_fetch_openrouter_models", or_feed):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    model = _model_by_id(models, "deepseek-chat")
+    assert model.enabled is True
+    assert model.pricing.prompt == pytest.approx(2.8e-07)
+    assert model.pricing.completion == pytest.approx(4.2e-07)
+
+
+@pytest.mark.asyncio
+async def test_native_non_numeric_price_does_not_break_catalog() -> None:
+    """A non-numeric native price (``"free"``) must not raise while parsing —
+    an unguarded ``"free" / 1_000_000`` throws and the outer catch drops the
+    *entire* provider catalog. It has to fail closed for that one model while
+    every other model in the same response still resolves."""
+    payload = {
+        "data": [
+            {
+                "id": "broken-price",
+                "owned_by": "mystery",
+                "model_spec": {
+                    "pricing": {"input": {"usd": "free"}, "output": {"usd": "free"}},
+                },
+            },
+            {"id": "deepseek-chat", "object": "model", "owned_by": "deepseek"},
+        ]
+    }
+
+    with _patch_models_endpoint(payload):
+        or_feed = AsyncMock(return_value=[])
+        with patch("routstr.payment.models.async_fetch_openrouter_models", or_feed):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    # One malformed entry must not empty the catalog.
+    assert {m.id for m in models} == {"broken-price", "deepseek-chat"}
+    broken = _model_by_id(models, "broken-price")
+    assert broken.enabled is False
+    healthy = _model_by_id(models, "deepseek-chat")
+    assert healthy.enabled is True
+    assert healthy.pricing.prompt == pytest.approx(2.8e-07)
+
+
+# ---------------------------------------------------------------------------
+# litellm rescue — the money-critical case (DeepSeek bare /models)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_bare_deepseek_resolves_via_litellm() -> None:
+    """DeepSeek's ``/models`` carries no pricing. The old code fabricated
+    ``$0.001`` + ctx 4096; the resolver must instead pull DeepSeek's real
+    rates from litellm's bundled cost map (``$0.28``/``$0.42`` per 1M, ctx
+    131072) and keep the model enabled."""
+    payload = {
+        "data": [
+            {"id": "deepseek-chat", "object": "model", "owned_by": "deepseek"},
+        ]
+    }
+
+    with _patch_models_endpoint(payload):
+        or_feed = AsyncMock(return_value=[])
+        with patch(
+            "routstr.payment.models.async_fetch_openrouter_models", or_feed
+        ):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    model = _model_by_id(models, "deepseek-chat")
+    assert model.enabled is True
+    assert model.pricing.prompt == pytest.approx(2.8e-07)
+    assert model.pricing.completion == pytest.approx(4.2e-07)
+    assert model.context_length == 131072
+    # Richer metadata than the two base prices is captured too.
+    assert model.pricing.input_cache_read == pytest.approx(2.8e-08)
+    assert model.top_provider is not None
+    assert model.top_provider.max_completion_tokens == 8192
+    # litellm answered, so the OpenRouter feed is never consulted.
+    or_feed.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_litellm_zero_price_entry_fails_closed(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    """A litellm entry that lists a model but prices it at 0/0 (free-tier
+    moderation/rerank models do this) is not a real price — treating it as one
+    would silently serve the model for free. The resolver must reject a both-zero
+    litellm hit and fall through, so the model imports disabled, not at $0."""
+    payload = {
+        "data": [
+            {"id": "omni-moderation-latest", "object": "model", "owned_by": "openai"},
+        ]
+    }
+
+    gen_logger = logging.getLogger("routstr.upstream.generic")
+    gen_logger.addHandler(caplog.handler)
+    try:
+        with _patch_models_endpoint(payload):
+            or_feed = AsyncMock(return_value=[])
+            with patch(
+                "routstr.payment.models.async_fetch_openrouter_models", or_feed
+            ):
+                models = await GenericUpstreamProvider(
+                    base_url="http://x"
+                ).fetch_models()
+    finally:
+        gen_logger.removeHandler(caplog.handler)
+
+    model = _model_by_id(models, "omni-moderation-latest")
+    assert model.enabled is False
+    assert model.pricing.prompt == 0.0
+    assert model.pricing.completion == 0.0
+    assert any(
+        "omni-moderation-latest" in rec.getMessage()
+        for rec in caplog.records
+        if rec.levelno >= logging.WARNING
+    )
+
+
+@pytest.mark.asyncio
+async def test_litellm_output_cap_not_used_as_context() -> None:
+    """litellm's ``max_tokens`` is the completion cap, not the context window
+    (it tracks ``max_output_tokens`` for ~94% of models). When a model reports
+    no ``max_input_tokens``, the resolver must not smuggle the output cap in as
+    the context window; it falls back to the id-based estimate instead, while
+    ``max_tokens`` still feeds the completion limit."""
+    payload = {
+        "data": [
+            {
+                "id": "gemini/gemini-gemma-2-9b-it",
+                "object": "model",
+                "owned_by": "google",
+            },
+        ]
+    }
+
+    with _patch_models_endpoint(payload):
+        or_feed = AsyncMock(return_value=[])
+        with patch(
+            "routstr.payment.models.async_fetch_openrouter_models", or_feed
+        ):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    model = _model_by_id(models, "gemini/gemini-gemma-2-9b-it")
+    assert model.enabled is True
+    # litellm gives this model max_input_tokens=None, max_tokens=8192 (an output
+    # cap). Context must come from the estimate (4096), never the 8192 cap.
+    assert model.context_length == 4096
+    # The 8192 output cap still lands where it belongs: the completion limit.
+    assert model.top_provider is not None
+    assert model.top_provider.max_completion_tokens == 8192
+
+
+# ---------------------------------------------------------------------------
+# OpenRouter fallback — litellm misses, OR carries a full payload
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_unknown_to_litellm_resolves_via_openrouter() -> None:
+    """A model litellm has never heard of still resolves if the OpenRouter
+    feed lists it, pulling price + context from that entry."""
+    payload = {
+        "data": [
+            {"id": "exotic/model-9000", "object": "model", "owned_by": "exotic"},
+        ]
+    }
+    or_entry = {
+        "id": "exotic/model-9000",
+        "name": "Exotic 9000",
+        "context_length": 65536,
+        "architecture": {
+            "modality": "text+image->text",
+            "input_modalities": ["text", "image"],
+            "output_modalities": ["text"],
+            "tokenizer": "Other",
+            "instruct_type": None,
+        },
+        "pricing": {"prompt": "0.000005", "completion": "0.000010"},
+        "top_provider": {
+            "context_length": 65536,
+            "max_completion_tokens": 4096,
+            "is_moderated": False,
+        },
+    }
+
+    with _patch_models_endpoint(payload):
+        or_feed = AsyncMock(return_value=[or_entry])
+        with patch(
+            "routstr.payment.models.async_fetch_openrouter_models", or_feed
+        ):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    model = _model_by_id(models, "exotic/model-9000")
+    assert model.enabled is True
+    assert model.pricing.prompt == pytest.approx(5e-06)
+    assert model.pricing.completion == pytest.approx(1e-05)
+    assert model.context_length == 65536
+    # The feed's own modality string is carried through verbatim, not recomputed.
+    assert model.architecture.modality == "text+image->text"
+    or_feed.assert_awaited()
+
+
+@pytest.mark.asyncio
+async def test_openrouter_bare_tail_collision_picks_highest_price() -> None:
+    """When a bare model id matches several OpenRouter entries by tail
+    (``model`` ↔ ``a/model``, ``b/model``), the match must be deterministic and
+    money-safe: pick the highest-priced candidate regardless of feed order, so
+    ordering can never leave the node charging below true cost. (The live feed
+    has zero such collisions today; this guards the latent case.)"""
+    payload = {
+        "data": [
+            {"id": "zzz-phantom-model", "object": "model", "owned_by": "mystery"},
+        ]
+    }
+    # Same bare tail, different resellers; the pricier one is listed *second*
+    # so a first-wins match would pick the cheaper (undercharging) entry.
+    or_feed = AsyncMock(
+        return_value=[
+            {
+                "id": "cheapco/zzz-phantom-model",
+                "context_length": 8192,
+                "pricing": {"prompt": "0.000001", "completion": "0.000002"},
+            },
+            {
+                "id": "premiumco/zzz-phantom-model",
+                "context_length": 8192,
+                "pricing": {"prompt": "0.000009", "completion": "0.000010"},
+            },
+        ]
+    )
+
+    with _patch_models_endpoint(payload):
+        with patch("routstr.payment.models.async_fetch_openrouter_models", or_feed):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    model = _model_by_id(models, "zzz-phantom-model")
+    assert model.pricing.prompt == pytest.approx(9e-06)
+    assert model.pricing.completion == pytest.approx(1e-05)
+
+
+@pytest.mark.asyncio
+async def test_openrouter_bare_tail_tie_breaks_on_combined_cost() -> None:
+    """The bare-tail tie-break must weigh *both* rates, not prompt alone.
+    Given two colliding entries where one is cheaper on prompt but far dearer
+    on completion, ranking by prompt would pick the entry that undercharges
+    output-heavy traffic. Pick the highest *combined* per-token cost so the
+    money-safe choice holds whichever way the traffic leans."""
+    payload = {
+        "data": [
+            {"id": "yyy-phantom-model", "object": "model", "owned_by": "mystery"},
+        ]
+    }
+    # dear-overall is listed first with the *lower* prompt, so a prompt-only max
+    # would wrongly pick the second (cheaper-overall) entry.
+    or_feed = AsyncMock(
+        return_value=[
+            {
+                "id": "dearco/yyy-phantom-model",
+                "context_length": 8192,
+                "pricing": {"prompt": "0.000001", "completion": "0.000100"},
+            },
+            {
+                "id": "cheapco/yyy-phantom-model",
+                "context_length": 8192,
+                "pricing": {"prompt": "0.000009", "completion": "0.000002"},
+            },
+        ]
+    )
+
+    with _patch_models_endpoint(payload):
+        with patch("routstr.payment.models.async_fetch_openrouter_models", or_feed):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    model = _model_by_id(models, "yyy-phantom-model")
+    assert model.pricing.prompt == pytest.approx(1e-06)
+    assert model.pricing.completion == pytest.approx(1e-04)
+
+
+@pytest.mark.asyncio
+async def test_openrouter_feed_fetched_once_per_discovery() -> None:
+    """Two models both missing litellm must share a single OpenRouter fetch —
+    the feed is not re-downloaded per model."""
+    payload = {
+        "data": [
+            {"id": "exotic/model-a", "object": "model", "owned_by": "exotic"},
+            {"id": "exotic/model-b", "object": "model", "owned_by": "exotic"},
+        ]
+    }
+    or_feed = AsyncMock(
+        return_value=[
+            {
+                "id": "exotic/model-a",
+                "context_length": 8192,
+                "pricing": {"prompt": "0.000001", "completion": "0.000002"},
+            },
+            {
+                "id": "exotic/model-b",
+                "context_length": 8192,
+                "pricing": {"prompt": "0.000003", "completion": "0.000004"},
+            },
+        ]
+    )
+
+    with _patch_models_endpoint(payload):
+        with patch("routstr.payment.models.async_fetch_openrouter_models", or_feed):
+            models = await GenericUpstreamProvider(base_url="http://x").fetch_models()
+
+    assert {m.id for m in models} == {"exotic/model-a", "exotic/model-b"}
+    assert or_feed.await_count == 1
+
+
+# ---------------------------------------------------------------------------
+# fail closed — no source resolves → disabled + warned, never fabricated
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_unresolvable_model_fails_closed(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    """When native, litellm and OpenRouter all miss, the model is imported
+    disabled with a warning naming it — and no price is invented (the old
+    ``$0.001`` placeholder is gone)."""
+    payload = {
+        "data": [
+            {
+                "id": "nobody-has-priced-this-xyz",
+                "object": "model",
+                "owned_by": "mystery",
+            },
+        ]
+    }
+
+    # routstr loggers set propagate=False, so caplog's root handler misses
+    # them; attach its handler to the provider logger directly.
+    gen_logger = logging.getLogger("routstr.upstream.generic")
+    gen_logger.addHandler(caplog.handler)
+    try:
+        with _patch_models_endpoint(payload):
+            or_feed = AsyncMock(return_value=[])
+            with patch(
+                "routstr.payment.models.async_fetch_openrouter_models", or_feed
+            ):
+                models = await GenericUpstreamProvider(
+                    base_url="http://x"
+                ).fetch_models()
+    finally:
+        gen_logger.removeHandler(caplog.handler)
+
+    model = _model_by_id(models, "nobody-has-priced-this-xyz")
+    assert model.enabled is False
+    assert model.pricing.prompt == 0.0
+    assert model.pricing.completion == 0.0
+    assert any(
+        "nobody-has-priced-this-xyz" in rec.getMessage()
+        for rec in caplog.records
+        if rec.levelno >= logging.WARNING
+    )

ui/components/add-provider-model-dialog.tsx

@@ -68,6 +68,8 @@ const FormSchema = z.object({
   upstream_provider_id: z.string().default(''),
   input_cost: z.coerce.number().min(0).default(0),
   output_cost: z.coerce.number().min(0).default(0),
+  cache_read_cost: z.coerce.number().min(0).default(0),
+  cache_write_cost: z.coerce.number().min(0).default(0),
   request_cost: z.coerce.number().min(0).default(0),
   image_cost: z.coerce.number().min(0).default(0),
   web_search_cost: z.coerce.number().min(0).default(0),
@@ -125,6 +127,8 @@ export function AddProviderModelDialog({
       upstream_provider_id: '',
       input_cost: 0,
       output_cost: 0,
+      cache_read_cost: 0,
+      cache_write_cost: 0,
       request_cost: 0,
       image_cost: 0,
       web_search_cost: 0,
@@ -190,6 +194,8 @@ export function AddProviderModelDialog({
             : initialData.upstream_provider_id?.toString() || '',
         input_cost: pricing?.prompt ?? 0,
         output_cost: pricing?.completion ?? 0,
+        cache_read_cost: pricing?.input_cache_read ?? 0,
+        cache_write_cost: pricing?.input_cache_write ?? 0,
         request_cost: pricing?.request ?? 0,
         image_cost: pricing?.image ?? 0,
         web_search_cost: pricing?.web_search ?? 0,
@@ -231,6 +237,8 @@ export function AddProviderModelDialog({
         upstream_provider_id: '',
         input_cost: 0,
         output_cost: 0,
+        cache_read_cost: 0,
+        cache_write_cost: 0,
         request_cost: 0,
         image_cost: 0,
         web_search_cost: 0,
@@ -294,6 +302,8 @@ export function AddProviderModelDialog({
     );
     form.setValue('input_cost', pricing?.prompt ?? 0);
     form.setValue('output_cost', pricing?.completion ?? 0);
+    form.setValue('cache_read_cost', pricing?.input_cache_read ?? 0);
+    form.setValue('cache_write_cost', pricing?.input_cache_write ?? 0);
     form.setValue('request_cost', pricing?.request ?? 0);
     form.setValue('image_cost', pricing?.image ?? 0);
     form.setValue('web_search_cost', pricing?.web_search ?? 0);
@@ -365,6 +375,8 @@ export function AddProviderModelDialog({
         pricing: {
           prompt: data.input_cost,
           completion: data.output_cost,
+          input_cache_read: data.cache_read_cost,
+          input_cache_write: data.cache_write_cost,
           request: data.request_cost,
           image: data.image_cost,
           web_search: data.web_search_cost,
@@ -810,6 +822,38 @@ export function AddProviderModelDialog({
                     </FormItem>
                   )}
                 />
+                <FormField
+                  control={form.control}
+                  name='cache_read_cost'
+                  render={({ field }) => (
+                    <FormItem>
+                      <FormLabel>Cache Read Cost</FormLabel>
+                      <FormControl>
+                        <Input type='number' step='0.000001' {...field} />
+                      </FormControl>
+                      <FormDescription>
+                        Discounted cached-input read price per 1M tokens.
+                      </FormDescription>
+                      <FormMessage />
+                    </FormItem>
+                  )}
+                />
+                <FormField
+                  control={form.control}
+                  name='cache_write_cost'
+                  render={({ field }) => (
+                    <FormItem>
+                      <FormLabel>Cache Write Cost</FormLabel>
+                      <FormControl>
+                        <Input type='number' step='0.000001' {...field} />
+                      </FormControl>
+                      <FormDescription>
+                        Cached-input creation price per 1M tokens.
+                      </FormDescription>
+                      <FormMessage />
+                    </FormItem>
+                  )}
+                />
                 <FormField
                   control={form.control}
                   name='request_cost'

ui/lib/api/services/admin.ts

@@ -53,6 +53,8 @@ export const AdminModelPricingSchema = z.object({
   image: z.number().optional(),
   web_search: z.number().optional(),
   internal_reasoning: z.number().optional(),
+  input_cache_read: z.number().optional(),
+  input_cache_write: z.number().optional(),
 });
 
 export const AdminModelArchitectureSchema = z.object({
@@ -149,7 +151,7 @@ export class AdminService {
     if (!pricing) return pricing;
     const result = { ...pricing };
 
-    // Only prompt and completion are per-token and need scaling to per-1M
+    // Token-priced fields are stored per-token by the API and shown per-1M in the UI.
     const convertField = (field: string) => {
       const val = result[field];
       if (val !== undefined && val !== null) {
@@ -164,6 +166,8 @@ export class AdminService {
 
     convertField('prompt');
     convertField('completion');
+    convertField('input_cache_read');
+    convertField('input_cache_write');
 
     // Other fields (request, image, etc.) are already flat fees (per item)
     // so we do NOT scale them.
@@ -177,7 +181,7 @@ export class AdminService {
     if (!pricing) return pricing;
     const result = { ...pricing };
 
-    // Only prompt and completion are per-1M in UI and need scaling down to per-token
+    // Token-priced fields are per-1M in the UI and need scaling down to per-token.
     const convertField = (field: string) => {
       const val = result[field];
       if (val !== undefined && val !== null) {
@@ -190,6 +194,8 @@ export class AdminService {
 
     convertField('prompt');
     convertField('completion');
+    convertField('input_cache_read');
+    convertField('input_cache_write');
 
     // Other fields stay as flat fees
 

uv.lock

@@ -17,7 +17,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.15"
+version = "3.14.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -26,61 +26,111 @@ dependencies = [
     { name = "frozenlist" },
     { name = "multidict" },
     { name = "propcache" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
     { name = "yarl" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/82/78/8ea7308cac6934de8c74a14f3d5f65d1c89287426688be79538d0e5c013d/aiohttp-3.14.1.tar.gz", hash = "sha256:307f2cff90a764d329e77040603fa032db89c5c24fdad50c4c15334cba744035", size = 7955794, upload-time = "2026-06-07T21:09:35.529Z" }
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