update completion path

routstr/payment/helpers.py

@@ -287,16 +287,21 @@ def _sum_string_chars(node: Any) -> int:
     return 0
 
 
+def _count_prompt_token_ids(node: Any) -> int:
+    if isinstance(node, int) and not isinstance(node, bool):
+        return 1
+    if isinstance(node, list):
+        return sum(_count_prompt_token_ids(item) for item in node)
+    return 0
+
+
 def estimate_prompt_tokens(body: dict) -> int:
     """Conservatively estimate prompt tokens for the whole provider-bound body.
 
-    Unlike ``estimate_tokens`` (message text only), this walks every field, so
-    prompt weight hidden in tool schemas, tool-call arguments, ``system``, or
-    any field forwarded in future cannot escape the reservation estimate. It
-    over-estimates rather than under-estimates: the result only shrinks a
-    discount against a reservation that settlement later refunds.
+    Every string counts, as do token IDs in legacy ``prompt`` arrays, so no
+    forwarded field can hide prompt weight and shrink its reservation.
     """
-    return _sum_string_chars(body) // 3
+    return _sum_string_chars(body) // 3 + _count_prompt_token_ids(body.get("prompt"))
 
 
 def _get_image_dimensions(image_data: bytes) -> tuple[int, int]:

routstr/upstream/base.py

@@ -248,6 +248,13 @@ def _is_json_content_type(content_type: str | None) -> bool:
     return main.startswith("application/") and main.endswith("+json")
 
 
+def _openai_completion_path(path: str) -> str | None:
+    canonical = "/" + path.rstrip("/")
+    if canonical.endswith("/chat/completions"):
+        return "chat/completions"
+    return "completions" if canonical.endswith("/completions") else None
+
+
 class TopupData(BaseModel):
     """Universal top-up data schema for Lightning Network invoices."""
 
@@ -653,17 +660,20 @@ class BaseUpstreamProvider:
         return "openrouter.ai" in (self.base_url or "")
 
     def prepare_request_body(
-        self, body: bytes | None, model_obj: Model
+        self,
+        body: bytes | None,
+        model_obj: Model,
+        include_stream_usage: bool = False,
     ) -> bytes | None:
         """Transform request body for provider-specific requirements.
 
-        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``.
+        Automatically transforms model names and opts streaming OpenAI
+        completion endpoints into emitting per-chunk ``usage`` so cost
+        tracking can read real token counts.
 
         Args:
             body: Original request body bytes
+            include_stream_usage: Opt a streaming completion into usage chunks
 
         Returns:
             Transformed request body bytes
@@ -706,14 +716,8 @@ class BaseUpstreamProvider:
         # 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 must use
-        # the local request/response estimator. 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)
-        ):
+        # the local request/response estimator.
+        if data.get("stream") is True and include_stream_usage:
             existing = data.get("stream_options")
             merged = dict(existing) if isinstance(existing, dict) else {}
             if merged.get("include_usage") is not True:
@@ -1022,6 +1026,7 @@ class BaseUpstreamProvider:
         reservation_snapshot: ReservationSnapshot | None = None,
         client: httpx.AsyncClient | None = None,
         request_body: bytes | None = None,
+        legacy_completion: bool = False,
     ) -> StreamingResponse:
         """Handle streaming chat completion responses with token usage tracking and cost adjustment.
 
@@ -1060,6 +1065,7 @@ class BaseUpstreamProvider:
             last_model_seen: str | None = None
             usage_chunk_data: dict | None = None
             done_seen: bool = False
+            stream_id: str | None = None
 
             async def finalize_db_only() -> None:
                 nonlocal usage_finalized
@@ -1118,7 +1124,7 @@ class BaseUpstreamProvider:
                 * ``[DONE]`` is swallowed so it can be re-emitted exactly once at
                   end of stream.
                 """
-                nonlocal last_model_seen, usage_chunk_data, done_seen
+                nonlocal last_model_seen, usage_chunk_data, done_seen, stream_id
 
                 event = raw_event.strip(b"\r\n")
                 if not event:
@@ -1171,9 +1177,12 @@ class BaseUpstreamProvider:
                         or not isinstance(obj["id"], str)
                         or obj["id"] == "existing-id"
                     ):
-                        if not hasattr(self, "_current_stream_id"):
-                            self._current_stream_id = f"chatcmpl-{uuid.uuid4()}"
-                        obj["id"] = self._current_stream_id
+                        if stream_id is None:
+                            id_prefix = "cmpl" if legacy_completion else "chatcmpl"
+                            stream_id = f"{id_prefix}-{uuid.uuid4()}"
+                        obj["id"] = stream_id
+                    else:
+                        stream_id = obj["id"]
                     if isinstance(obj.get("usage"), dict):
                         # Capture usage for end-of-stream cost reconciliation.
                         # Some models (e.g. Gemini thinking models over the
@@ -1285,11 +1294,14 @@ class BaseUpstreamProvider:
                             raise
 
                         if usage_chunk_data is None:
-                            if not hasattr(self, "_current_stream_id"):
-                                self._current_stream_id = f"chatcmpl-{uuid.uuid4()}"
+                            if stream_id is None:
+                                id_prefix = "cmpl" if legacy_completion else "chatcmpl"
+                                stream_id = f"{id_prefix}-{uuid.uuid4()}"
                             usage_chunk_data = {
-                                "id": self._current_stream_id,
-                                "object": "chat.completion.chunk",
+                                "id": stream_id,
+                                "object": "text_completion"
+                                if legacy_completion
+                                else "chat.completion.chunk",
                                 "model": last_model_seen or "unknown",
                                 "choices": [],
                                 "usage": {
@@ -1361,6 +1373,7 @@ class BaseUpstreamProvider:
         model_obj: Model | None = None,
         reservation_snapshot: ReservationSnapshot | None = None,
         request_body: bytes | None = None,
+        legacy_completion: bool = False,
     ) -> Response:
         """Handle non-streaming chat completion responses with token usage tracking and cost adjustment.
 
@@ -1400,9 +1413,11 @@ class BaseUpstreamProvider:
             if requested_model:
                 response_json["model"] = requested_model
             if "id" not in response_json or not isinstance(response_json["id"], str):
-                response_json["id"] = f"chatcmpl-{uuid.uuid4()}"
+                prefix = "cmpl" if legacy_completion else "chatcmpl"
+                response_json["id"] = f"{prefix}-{uuid.uuid4()}"
 
-            if not isinstance(response_json.get("usage"), dict):
+            usage = response_json.get("usage")
+            if not isinstance(usage, dict) or not usage:
                 usage_estimator = MissingUsageEstimator(request_body, model_obj)
                 usage_estimator.observe(response_json)
                 response_json["usage"] = usage_estimator.openai_response_data(
@@ -2923,6 +2938,7 @@ class BaseUpstreamProvider:
         Returns:
             Response or StreamingResponse from upstream with cost tracking
         """
+        completion_path = _openai_completion_path(path)
         path = self.normalize_request_path(path, model_obj)
 
         if (
@@ -2951,7 +2967,11 @@ class BaseUpstreamProvider:
             (model_obj.forwarded_model_id or model_obj.id) if model_obj else None
         )
 
-        transformed_body = self.prepare_request_body(request_body, model_obj)
+        transformed_body = self.prepare_request_body(
+            request_body,
+            model_obj,
+            include_stream_usage=completion_path is not None,
+        )
 
         logger.debug(
             "Forwarding request to upstream",
@@ -3048,7 +3068,7 @@ class BaseUpstreamProvider:
                 return mapped_error
 
             if (
-                path.endswith("chat/completions")
+                completion_path is not None
                 or path.endswith("embeddings")
                 or path.endswith("messages")
                 or path.endswith("messages/count_tokens")
@@ -3117,7 +3137,7 @@ class BaseUpstreamProvider:
                             await response.aclose()
                             await client.aclose()
 
-                if path.endswith("chat/completions"):
+                if completion_path is not None:
                     client_wants_streaming = False
                     if request_body:
                         try:
@@ -3163,6 +3183,7 @@ class BaseUpstreamProvider:
                             reservation_snapshot=reservation_snapshot,
                             client=client,
                             request_body=request_body,
+                            legacy_completion=completion_path == "completions",
                         )
 
                 # Handle both non-streaming chat completions and embeddings
@@ -3177,6 +3198,7 @@ class BaseUpstreamProvider:
                             model_obj=model_obj,
                             reservation_snapshot=reservation_snapshot,
                             request_body=request_body,
+                            legacy_completion=completion_path == "completions",
                         )
                     finally:
                         await response.aclose()
@@ -4213,6 +4235,7 @@ class BaseUpstreamProvider:
         Returns:
             Response or StreamingResponse with refund if applicable
         """
+        completion_path = _openai_completion_path(path)
         if path.startswith("v1/"):
             path = path.replace("v1/", "")
 
@@ -4241,7 +4264,11 @@ class BaseUpstreamProvider:
 
         url = f"{self.base_url}/{path}"
 
-        transformed_body = self.prepare_request_body(request_body, model_obj)
+        transformed_body = self.prepare_request_body(
+            request_body,
+            model_obj,
+            include_stream_usage=completion_path is not None,
+        )
 
         logger.debug(
             "Forwarding request to upstream",
@@ -4338,7 +4365,7 @@ class BaseUpstreamProvider:
                     return error_response
 
                 if (
-                    path.endswith("chat/completions")
+                    completion_path is not None
                     or path.endswith("embeddings")
                     or path.endswith("messages")
                     or path.endswith("messages/count_tokens")

routstr/upstream/count_tokens.py

@@ -23,7 +23,11 @@ import litellm
 from fastapi.responses import Response
 
 from ..core import get_logger
-from ..payment.helpers import estimate_prompt_tokens, estimate_tokens
+from ..payment.helpers import (
+    _count_prompt_token_ids,
+    estimate_prompt_tokens,
+    estimate_tokens,
+)
 from ..payment.models import Model
 
 logger = get_logger(__name__)
@@ -46,11 +50,28 @@ def _model_name(model_obj: Model | None, body: dict[str, Any]) -> str:
     return body_model if isinstance(body_model, str) else ""
 
 
-def _count_with_litellm(model: str, body: dict[str, Any]) -> int:
+def _count_with_litellm(
+    model: str, body: dict[str, Any], include_legacy_prompt: bool = False
+) -> int:
     messages = body.get("messages")
     if not isinstance(messages, list):
         messages = []
 
+    prompt_token_ids = 0
+    if include_legacy_prompt:
+        prompt = body.get("prompt")
+        if isinstance(prompt, str):
+            prompt_texts = [prompt]
+        elif isinstance(prompt, list):
+            prompt_texts = [item for item in prompt if isinstance(item, str)]
+        else:
+            prompt_texts = []
+        prompt_token_ids = _count_prompt_token_ids(prompt)
+        messages = [
+            *({"role": "user", "content": text} for text in prompt_texts if text),
+            *messages,
+        ]
+
     system = body.get("system")
     if isinstance(system, str) and system:
         messages = [{"role": "system", "content": system}, *messages]
@@ -65,7 +86,7 @@ def _count_with_litellm(model: str, body: dict[str, Any]) -> int:
 
     tools = body.get("tools") if isinstance(body.get("tools"), list) else None
 
-    return int(
+    return prompt_token_ids + int(
         litellm.token_counter(
             model=model,
             messages=messages,
@@ -133,7 +154,9 @@ class MissingUsageEstimator:
         if self._input_tokens is not None:
             return self._input_tokens
         try:
-            self._input_tokens = _count_with_litellm(self.model_name, self.body)
+            self._input_tokens = _count_with_litellm(
+                self.model_name, self.body, include_legacy_prompt=True
+            )
         except Exception as exc:
             self._input_tokens = estimate_prompt_tokens(self.body)
             logger.debug(

tests/unit/test_completions_billing.py

@@ -0,0 +1,384 @@
+import json
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+from fastapi.responses import Response
+from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
+from sqlmodel import SQLModel
+from sqlmodel.ext.asyncio.session import AsyncSession
+
+import routstr.auth as auth_module
+from routstr.auth import ReservationSnapshot, get_reservation_snapshot, pay_for_request
+from routstr.core.db import ApiKey, ReservationRelease
+from routstr.payment.models import Architecture, Model, Pricing
+from routstr.upstream.base import BaseUpstreamProvider
+
+BALANCE = 100_000
+RESERVED = 5_000
+# 1 msat per prompt token, 2 msat per completion token.
+MODEL = Model(
+    id="glm-test",
+    name="glm-test",
+    created=0,
+    description="",
+    context_length=64_000,
+    architecture=Architecture(
+        modality="text->text",
+        input_modalities=["text"],
+        output_modalities=["text"],
+        tokenizer="Other",
+        instruct_type=None,
+    ),
+    pricing=Pricing(prompt=0.001, completion=0.002),
+    sats_pricing=Pricing(prompt=0.001, completion=0.002),
+)
+USAGE = {"prompt_tokens": 400, "completion_tokens": 100, "total_tokens": 500}
+EXPECTED_MSATS = 400 * 1 + 100 * 2
+
+COMPLETION_BODY = {
+    "model": MODEL.id,
+    "prompt": "Once upon a time, in a land far away, " * 20,
+    "max_tokens": 100,
+}
+CHAT_BODY = {"model": MODEL.id, "messages": [{"role": "user", "content": "hi"}]}
+
+COMPLETION_JSON = {
+    "id": "cmpl-1",
+    "object": "text_completion",
+    "model": MODEL.id,
+    "choices": [{"text": " there was", "index": 0, "finish_reason": "stop"}],
+    "usage": USAGE,
+}
+COMPLETION_CHUNKS = [
+    {
+        "id": "cmpl-1",
+        "object": "text_completion",
+        "model": MODEL.id,
+        "choices": [{"text": " there", "index": 0, "finish_reason": None}],
+    },
+    {
+        "id": "cmpl-1",
+        "object": "text_completion",
+        "model": MODEL.id,
+        "choices": [{"text": " was", "index": 0, "finish_reason": "stop"}],
+    },
+]
+USAGE_CHUNK = {
+    "id": "cmpl-1",
+    "object": "text_completion",
+    "model": MODEL.id,
+    "choices": [],
+    "usage": USAGE,
+}
+
+
+def _sse(chunks: list[dict]) -> bytes:
+    body = b"".join(b"data: " + json.dumps(c).encode() + b"\n\n" for c in chunks)
+    return body + b"data: [DONE]\n\n"
+
+
+@pytest.fixture(autouse=True)
+def patch_sats_usd_price() -> Any:
+    with patch("routstr.payment.cost_calculation.sats_usd_price", return_value=5.0e-4):
+        yield
+
+
+async def _engine() -> AsyncEngine:
+    engine = create_async_engine("sqlite+aiosqlite://")
+    async with engine.begin() as connection:
+        await connection.run_sync(SQLModel.metadata.create_all)
+    return engine
+
+
+def _upstream(content: bytes, content_type: str) -> httpx.Response:
+    return httpx.Response(
+        200,
+        content=content,
+        headers={"content-type": content_type},
+        request=httpx.Request("POST", "http://upstream"),
+    )
+
+
+async def _drain(response: Any) -> bytes:
+    body = b""
+    if hasattr(response, "body_iterator"):
+        async for chunk in response.body_iterator:
+            body += chunk if isinstance(chunk, bytes) else chunk.encode()
+    else:
+        body = response.body
+    return body
+
+
+async def _forward(
+    engine: AsyncEngine,
+    path: str,
+    body: dict,
+    upstream: httpx.Response,
+) -> tuple[bytes, ReservationSnapshot, AsyncMock]:
+    """Reserve, forward through the real ``forward_request`` and settle."""
+    provider = BaseUpstreamProvider(
+        base_url="http://upstream", api_key="k", provider_fee=1.0
+    )
+    request = MagicMock()
+    request.method = "POST"
+    request.query_params = {}
+    send = AsyncMock(return_value=upstream)
+
+    async with AsyncSession(engine, expire_on_commit=False) as session:
+        key = ApiKey(hashed_key="key", balance=BALANCE)
+        session.add(key)
+        await session.commit()
+        await pay_for_request(key, RESERVED, session)
+        snapshot = await get_reservation_snapshot(key, session)
+
+        with (
+            patch("httpx.AsyncClient.send", send),
+            patch(
+                "routstr.upstream.base.create_session",
+                side_effect=lambda: AsyncSession(engine, expire_on_commit=False),
+            ),
+            patch(
+                "routstr.upstream.base.adjust_payment_for_tokens",
+                auth_module.adjust_payment_for_tokens,
+            ),
+        ):
+            response = await provider.forward_request(
+                request,
+                path,
+                {},
+                json.dumps(body).encode(),
+                key,
+                RESERVED,
+                session,
+                MODEL,
+                snapshot,
+            )
+            out = await _drain(response)
+    return out, snapshot, send
+
+
+async def _ledger(
+    engine: AsyncEngine, snapshot: ReservationSnapshot
+) -> tuple[int, int, int, str | None]:
+    async with AsyncSession(engine, expire_on_commit=False) as session:
+        key = await session.get(ApiKey, snapshot.key_hash)
+        record = await session.get(ReservationRelease, snapshot.release_id)
+        assert key is not None
+        return (
+            key.balance,
+            key.total_spent,
+            key.reserved_balance,
+            record.status if record else None,
+        )
+
+
+def _sse_objects(out: bytes) -> list[dict]:
+    objs = []
+    for line in out.split(b"\n"):
+        if line.startswith(b"data: ") and line[6:].strip() != b"[DONE]":
+            objs.append(json.loads(line[6:]))
+    return objs
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "path",
+    [
+        "completions",
+        "v1/completions",
+        "v1/completions/",
+        "openai/v1/completions",
+    ],
+)
+async def test_non_streaming_completion_with_usage_is_charged(path: str) -> None:
+    engine = await _engine()
+    out, snapshot, _ = await _forward(
+        engine,
+        path,
+        COMPLETION_BODY,
+        _upstream(json.dumps(COMPLETION_JSON).encode(), "application/json"),
+    )
+
+    balance, spent, reserved, status = await _ledger(engine, snapshot)
+    assert (balance, spent, reserved, status) == (
+        BALANCE - EXPECTED_MSATS,
+        EXPECTED_MSATS,
+        0,
+        "charged",
+    )
+    body = json.loads(out)
+    assert body["object"] == "text_completion"
+    assert body["usage"]["cost"]["total_msats"] == EXPECTED_MSATS
+    await engine.dispose()
+
+
+@pytest.mark.asyncio
+async def test_streaming_completion_with_final_usage_is_charged() -> None:
+    engine = await _engine()
+    out, snapshot, send = await _forward(
+        engine,
+        "v1/completions",
+        {**COMPLETION_BODY, "stream": True},
+        _upstream(_sse([*COMPLETION_CHUNKS, USAGE_CHUNK]), "text/event-stream"),
+    )
+
+    balance, spent, reserved, status = await _ledger(engine, snapshot)
+    assert (balance, spent, reserved, status) == (
+        BALANCE - EXPECTED_MSATS,
+        EXPECTED_MSATS,
+        0,
+        "charged",
+    )
+
+    forwarded = json.loads(send.call_args.args[0].content)
+    assert forwarded["stream_options"] == {"include_usage": True}
+
+    objs = _sse_objects(out)
+    assert [o["choices"][0]["text"] for o in objs if o["choices"]] == [
+        " there",
+        " was",
+    ]
+    assert objs[-1]["object"] == "text_completion"
+    assert objs[-1]["usage"]["cost"]["total_msats"] == EXPECTED_MSATS
+    assert out.endswith(b"data: [DONE]\n\n")
+    await engine.dispose()
+
+
+@pytest.mark.asyncio
+async def test_non_streaming_completion_with_empty_usage_is_estimated() -> None:
+    """Missing usage is estimated from ``prompt`` and ``text``, never free."""
+    engine = await _engine()
+    no_usage = {k: v for k, v in COMPLETION_JSON.items() if k != "id"}
+    no_usage["usage"] = {}
+    out, snapshot, _ = await _forward(
+        engine,
+        "v1/completions",
+        COMPLETION_BODY,
+        _upstream(json.dumps(no_usage).encode(), "application/json"),
+    )
+
+    balance, spent, reserved, status = await _ledger(engine, snapshot)
+    assert 0 < spent <= RESERVED
+    assert (BALANCE - balance, reserved, status) == (spent, 0, "charged")
+    body = json.loads(out)
+    usage = body["usage"]
+    assert body["id"].startswith("cmpl-")
+    assert usage["estimated"] is True
+    assert usage["prompt_tokens"] > 100
+    assert usage["completion_tokens"] > 0
+    await engine.dispose()
+
+
+@pytest.mark.asyncio
+async def test_streaming_completion_without_usage_is_estimated() -> None:
+    engine = await _engine()
+    out, snapshot, _ = await _forward(
+        engine,
+        "v1/completions",
+        {**COMPLETION_BODY, "stream": True},
+        _upstream(_sse(COMPLETION_CHUNKS), "text/event-stream"),
+    )
+
+    balance, spent, reserved, status = await _ledger(engine, snapshot)
+    assert 0 < spent <= RESERVED
+    assert (BALANCE - balance, reserved, status) == (spent, 0, "charged")
+    trailer = _sse_objects(out)[-1]
+    assert trailer["id"] == "cmpl-1"
+    assert trailer["object"] == "text_completion"
+    assert trailer["usage"]["prompt_tokens"] > 100
+    assert trailer["usage"]["cost"]["total_msats"] == spent
+    await engine.dispose()
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("path", ["v1/chat/completions/", "openai/v1/chat/completions"])
+async def test_chat_completion_aliases_are_charged(path: str) -> None:
+    engine = await _engine()
+    chat_json = {
+        "id": "chatcmpl-1",
+        "object": "chat.completion",
+        "model": MODEL.id,
+        "choices": [
+            {
+                "message": {"role": "assistant", "content": "hi"},
+                "index": 0,
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": USAGE,
+    }
+    out, snapshot, _ = await _forward(
+        engine,
+        path,
+        CHAT_BODY,
+        _upstream(json.dumps(chat_json).encode(), "application/json"),
+    )
+
+    balance, spent, reserved, status = await _ledger(engine, snapshot)
+    assert (balance, spent, reserved, status) == (
+        BALANCE - EXPECTED_MSATS,
+        EXPECTED_MSATS,
+        0,
+        "charged",
+    )
+    assert json.loads(out)["usage"]["cost"]["total_msats"] == EXPECTED_MSATS
+    await engine.dispose()
+
+
+def test_stream_usage_option_is_scoped_to_completion_endpoints() -> None:
+    provider = BaseUpstreamProvider(base_url="http://upstream", api_key="k")
+    body = json.dumps({"prompt": "draw this", "stream": True}).encode()
+
+    assert provider.prepare_request_body(body, MODEL) == body
+
+
+async def _forward_x_cashu(
+    path: str, body: dict, upstream: httpx.Response
+) -> tuple[AsyncMock, AsyncMock]:
+    """Run ``forward_x_cashu_request`` with the settlement handler stubbed out."""
+    provider = BaseUpstreamProvider(
+        base_url="http://upstream", api_key="k", provider_fee=1.0
+    )
+    request = MagicMock()
+    request.method = "POST"
+    request.query_params = {}
+    request.state.request_id = "req-1"
+    request.body = AsyncMock(return_value=json.dumps(body).encode())
+    send = AsyncMock(return_value=upstream)
+    settle = AsyncMock(return_value=Response(content=b"{}", status_code=200))
+
+    with (
+        patch("httpx.AsyncClient.send", send),
+        patch.object(provider, "handle_x_cashu_chat_completion", settle),
+    ):
+        await provider.forward_x_cashu_request(
+            request, path, {}, 10, "sat", RESERVED, MODEL
+        )
+    return settle, send
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("path", ["completions", "v1/completions", "v1/completions/"])
+async def test_x_cashu_legacy_completion_is_settled(path: str) -> None:
+    """Legacy completions must reach refund settlement, not raw passthrough."""
+    settle, _ = await _forward_x_cashu(
+        path,
+        COMPLETION_BODY,
+        _upstream(json.dumps(COMPLETION_JSON).encode(), "application/json"),
+    )
+
+    assert settle.await_count == 1
+
+
+@pytest.mark.asyncio
+async def test_x_cashu_streaming_completion_requests_usage() -> None:
+    _, send = await _forward_x_cashu(
+        "v1/completions",
+        {**COMPLETION_BODY, "stream": True},
+        _upstream(_sse([*COMPLETION_CHUNKS, USAGE_CHUNK]), "text/event-stream"),
+    )
+
+    forwarded = json.loads(send.call_args.args[0].content)
+    assert forwarded["stream_options"] == {"include_usage": True}

tests/unit/test_count_tokens_local.py

@@ -229,6 +229,15 @@ def test_missing_usage_estimator_openai_dialect() -> None:
     }
 
 
+def test_missing_usage_estimator_counts_legacy_token_prompt() -> None:
+    model = _make_model()
+    request_body = _body({"model": model.id, "prompt": [[1, 2], [3, 4, 5]]})
+
+    usage = MissingUsageEstimator(request_body, model).openai_response_data()["usage"]
+
+    assert usage["prompt_tokens"] >= 5
+
+
 def test_missing_usage_estimator_does_not_count_response_metadata() -> None:
     estimator = MissingUsageEstimator(b"{}", None)
     estimator.observe(

tests/unit/test_payment_helpers.py

@@ -183,6 +183,32 @@ def test_estimate_prompt_tokens_counts_every_string_in_the_body() -> None:
     # value prefix can buy a discount, so both still count in full.
     assert estimate_prompt_tokens({"tools": [{"data": hidden}]}) >= 1_000
     assert estimate_prompt_tokens({"system": "data:" + hidden}) >= 1_000
+    assert estimate_prompt_tokens({"prompt": [[1, 2], [3, 4, 5]]}) >= 5
+
+
+async def test_discount_counts_legacy_token_id_prompt() -> None:
+    from routstr.payment.helpers import calculate_discounted_max_cost
+
+    pricing = Mock()
+    pricing.prompt = 0.001
+    pricing.completion = 0.0
+    pricing.max_prompt_cost = 50.0
+    pricing.max_completion_cost = 0.0
+
+    model_obj = Mock()
+    model_obj.sats_pricing = pricing
+    model_obj.top_provider = None
+    model_obj.context_length = None
+
+    body = {"model": "test-model", "prompt": list(range(50_000)), "max_tokens": 0}
+    with (
+        patch.object(settings, "fixed_pricing", False),
+        patch.object(settings, "tolerance_percentage", 0),
+        patch.object(settings, "min_request_msat", 1000),
+    ):
+        cost = await calculate_discounted_max_cost(50_000, body, model_obj)
+
+    assert cost == 50_000
 
 
 async def test_discount_cannot_be_dodged_by_hiding_prompt_in_tools() -> None: