litellm/tests/unit/test_cost_calculator.py

import json
import asyncio
import datetime
import time
from collections.abc import Mapping
from types import MappingProxyType, SimpleNamespace
from typing import Final, Literal, cast

import httpx
import pytest
import respx
from openai.types.completion_usage import CompletionUsage
from pydantic import BaseModel

import litellm
from litellm import TranscriptionResponse, model_cost
from litellm.cost_calculator import BaseTokenUsageProcessor, RealtimeAPITokenUsageProcessor, ResponsesWebSocketTokenUsageProcessor, batch_cost_calculator, completion_cost, cost_per_token, handle_realtime_stream_cost_calculation, response_cost_calculator
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
from litellm.litellm_core_utils.litellm_logging import Logging
from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
    convert_to_model_response_object,
)
from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo
from litellm.llms.custom_httpx.http_handler import HTTPHandler
from litellm.llms.fireworks_ai.cost_calculator import get_base_model_for_pricing
from litellm.llms.together_ai.cost_calculator import get_model_params_and_category
from litellm.types.llms.base import CachedTokensDetails
from litellm.types.llms.openai import OpenAIRealtimeStreamList, ResponseAPIUsage, ResponsesAPIResponse
from litellm.types.rerank import RerankResponse
from litellm.types.utils import CallTypes, Choices, CompletionTokensDetailsWrapper, EmbeddingResponse, ImageObject, ImageResponse, ImageUsage, ImageUsageInputTokensDetails, LiteLLMRealtimeStreamLoggingObject, Message, ModelInfo, ModelResponse, PromptTokensDetails, PromptTokensDetailsWrapper, Usage
from litellm.types.videos.main import VideoObject
from litellm.utils import supports_prompt_caching
import os
import threading
from unittest.mock import MagicMock, patch


class ResponseCostCapture(CustomLogger):
    def __init__(self) -> None:
        self.success_cost: float | None = None
        self.success_event: Final = threading.Event()
        self.failure_cost: float | None = None

    def log_success_event(
        self, kwargs: dict[str, object], response_obj: object, start_time: object, end_time: object
    ) -> None:
        response_cost: Final = kwargs.get("response_cost")
        self.success_cost = float(response_cost) if isinstance(response_cost, (int, float)) else None
        self.success_event.set()

    def log_failure_event(
        self, kwargs: dict[str, object], response_obj: object, start_time: object, end_time: object
    ) -> None:
        self.failure_cost = float(kwargs["response_cost"])

    async def async_log_success_event(
        self, kwargs: dict[str, object], response_obj: object, start_time: object, end_time: object
    ) -> None:
        response_cost: Final = kwargs.get("response_cost")
        self.success_cost = float(response_cost) if isinstance(response_cost, (int, float)) else None
        self.success_event.set()

    async def async_log_failure_event(
        self, kwargs: dict[str, object], response_obj: object, start_time: object, end_time: object
    ) -> None:
        self.failure_cost = float(kwargs["response_cost"])


@pytest.fixture
def _local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))


def test_cost_per_token_duplicate_openai_prefix_matches_model_cost(monkeypatch):
    """
    Router/proxy configs may use deployment ids like openai/openai/<model>. Cost lookup must
    resolve to model_prices keys (e.g. gpt-5.5), not fail or multiply prefixes.
    """
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    prompt_usd, completion_usd = cost_per_token(
        model="openai/openai/gpt-5.5",
        prompt_tokens=100,
        completion_tokens=50,
        custom_llm_provider="openai",
    )

    assert prompt_usd + completion_usd > 0


def test_cost_per_token_non_string_model_does_not_hang():
    """
    The provider-prefix dedup loop must not spin forever when `model` is a
    non-string object (e.g. a MagicMock from a mocked transport). It should
    return or raise promptly instead of looping on a truthy `.startswith()`.
    """
    import threading
    from unittest.mock import MagicMock

    result: dict = {}

    def _run():
        try:
            cost_per_token(
                model=MagicMock(),
                prompt_tokens=10,
                completion_tokens=5,
                custom_llm_provider="anthropic",
            )
            result["status"] = "returned"
        except Exception:
            result["status"] = "raised"

    worker = threading.Thread(target=_run, daemon=True)
    worker.start()
    worker.join(timeout=10)

    assert not worker.is_alive(), "cost_per_token hung on a non-string model"
    assert result.get("status") in ("returned", "raised")


def test_completion_cost_uses_response_model_for_dynamic_routing(_local_model_cost_map):
    """
    Test that completion_cost uses the model from the response object
    when the input model (e.g., azure-model-router) is not in model_cost.
    This supports Azure Model Router and similar dynamic routing scenarios.
    """

    # Simulate Azure Model Router: input is generic router, response has actual model
    response = ModelResponse(
        id="test-id",
        model="azure_ai/gpt-4o-2024-08-06",  # Response contains actual model used
        choices=[],
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Should calculate cost using the response model, not the input model
    cost = completion_cost(
        completion_response=response,
        model="azure_ai/azure-model-router",  # Input model doesn't exist in model_cost
        custom_llm_provider="azure_ai",
    )

    assert cost > 0, "Cost should be calculated using response model"


def test_completion_cost_strips_dated_azure_snapshot_model(_local_model_cost_map: None) -> None:
    dated_response = ModelResponse(
        model="gpt-5.6-luna-2099-01-01",
        choices=[Choices(index=0, message=Message(role="assistant", content="hi"), finish_reason="stop")],
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )
    dated_response._hidden_params = {"custom_llm_provider": "azure"}

    undated_response = ModelResponse(
        model="gpt-5.6-luna",
        choices=[Choices(index=0, message=Message(role="assistant", content="hi"), finish_reason="stop")],
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )
    undated_response._hidden_params = {"custom_llm_provider": "azure"}

    dated_cost = litellm.completion_cost(completion_response=dated_response)
    undated_cost = litellm.completion_cost(completion_response=undated_response)

    assert dated_cost == undated_cost
    assert dated_cost > 0


def test_cost_calculator_with_response_cost_in_additional_headers():
    class MockResponse(BaseModel):
        _hidden_params = {"additional_headers": {"llm_provider-x-litellm-response-cost": 1000}}

    result = response_cost_calculator(
        response_object=MockResponse(),
        model="",
        custom_llm_provider=None,
        call_type="",
        optional_params={},
        cache_hit=None,
        base_model=None,
    )

    assert result == 1000


@pytest.mark.parametrize(
    ("input_cost_per_token", "output_cost_per_token", "expected_cost"),
    ((0.0, 0.0, 0.0), (0.001, 0.002, 10 * 0.001 + 253 * 0.002)),
)
def test_response_cost_calculator_custom_pricing_ignores_upstream_response_cost_header(
    monkeypatch: pytest.MonkeyPatch,
    input_cost_per_token: float,
    output_cost_per_token: float,
    expected_cost: float,
) -> None:
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
    litellm.register_model(
        model_cost={
            "cc-custom-priced-chat": {
                "litellm_provider": "openai",
                "mode": "chat",
                "input_cost_per_token": input_cost_per_token,
                "output_cost_per_token": output_cost_per_token,
            }
        }
    )
    response: Final = ModelResponse(
        model="cc-custom-priced-chat",
        choices=[Choices(message=Message(content="ok"))],
        usage=Usage(prompt_tokens=10, completion_tokens=253, total_tokens=263),
    )
    response._hidden_params = {"additional_headers": {"llm_provider-x-litellm-response-cost": 0.000413}}

    cost: Final = response_cost_calculator(
        response_object=response,
        model="cc-custom-priced-chat",
        custom_llm_provider="openai",
        call_type="completion",
        optional_params={},
        custom_pricing=True,
    )

    assert cost == pytest.approx(expected_cost)


def test_response_cost_calculator_keeps_optional_params_out_of_hidden_params():
    class MockResponse(BaseModel):
        pass

    response = MockResponse()
    response._hidden_params = {"custom_llm_provider": "openai"}
    optional_params = {
        "dimensions": 256,
        "extra_headers": {"x-goog-api-key": "goog-secret"},
        "aws_session_token": "session-secret",
    }

    response_cost_calculator(
        response_object=response,
        model="text-embedding-3-small",
        custom_llm_provider="openai",
        call_type="embedding",
        optional_params=optional_params,
    )

    assert response._hidden_params == {"custom_llm_provider": "openai"}
    assert optional_params["extra_headers"] == {"x-goog-api-key": "goog-secret"}
    assert optional_params["aws_session_token"] == "session-secret"


def test_embedding_success_logging_and_spend_log_carry_no_forwarded_credentials(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    from litellm.proxy import proxy_server
    from litellm.proxy.spend_tracking.spend_tracking_utils import _get_proxy_server_request_for_spend_logs_payload

    monkeypatch.setattr(proxy_server, "general_settings", {"store_prompts_in_spend_logs": True})
    shared_metadata: dict[str, object] = {"user_api_key_alias": "alias"}
    proxy_server_request: Final = {"body": {"model": "emb", "input": "hi", "metadata": shared_metadata}}
    shared_optional_params: dict[str, object] = {"encoding_format": "float"}
    logging_obj = Logging(
        model="text-embedding-3-small",
        messages=[{"role": "user", "content": "hi"}],
        stream=False,
        call_type="aembedding",
        start_time=datetime.datetime.now(),
        litellm_call_id="embedding-hidden-params",
        function_id="f",
    )
    logging_obj.update_environment_variables(
        model="text-embedding-3-small",
        litellm_params={"metadata": shared_metadata, "proxy_server_request": proxy_server_request},
        optional_params=shared_optional_params,
        custom_llm_provider="openai",
    )
    shared_optional_params["extra_headers"] = {"x-goog-api-key": "goog-secret"}
    response = EmbeddingResponse(model="text-embedding-3-small", data=[], usage=Usage(prompt_tokens=3, total_tokens=3))
    response._hidden_params = {"custom_llm_provider": "openai"}

    logging_obj._process_hidden_params_and_response_cost(
        response,
        start_time=datetime.datetime.now(),
        end_time=datetime.datetime.now(),
    )

    litellm_params = logging_obj.model_call_details["litellm_params"]
    stored_request: Final = _get_proxy_server_request_for_spend_logs_payload(
        metadata=shared_metadata,
        litellm_params=litellm_params,
        kwargs=logging_obj.model_call_details,
    )
    hidden_params = litellm_params["metadata"]["hidden_params"]
    assert isinstance(hidden_params, dict)
    assert "optional_params" not in hidden_params
    assert '"hidden_params"' in stored_request
    assert "goog-secret" not in stored_request
    assert "goog-secret" not in str(logging_obj.model_call_details["standard_logging_object"])
    assert logging_obj.model_call_details["response_cost"] is not None
    assert logging_obj.optional_params["extra_headers"] == {"x-goog-api-key": "goog-secret"}


def test_realtime_stream_combines_text_and_audio_token_details():
    """Realtime response.done usage with input_token_details / output_token_details."""
    from litellm.cost_calculator import RealtimeAPITokenUsageProcessor

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": "gpt-4o-realtime-preview"}},
        {
            "type": "response.done",
            "response": {
                "usage": {
                    "input_tokens": 10,
                    "output_tokens": 20,
                    "total_tokens": 30,
                    "input_token_details": {"text_tokens": 8, "audio_tokens": 2},
                    "output_token_details": {"text_tokens": 12, "audio_tokens": 8},
                }
            },
        },
        {
            "type": "response.done",
            "response": {
                "usage": {
                    "input_tokens": 5,
                    "output_tokens": 15,
                    "total_tokens": 20,
                    "input_token_details": {"text_tokens": 3, "audio_tokens": 2},
                    "output_token_details": {"text_tokens": 5, "audio_tokens": 10},
                }
            },
        },
    ]

    combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
        results=results,
    )

    assert combined.prompt_tokens_details is not None
    assert combined.prompt_tokens_details.text_tokens == 11
    assert combined.prompt_tokens_details.audio_tokens == 4

    assert combined.completion_tokens_details is not None
    assert combined.completion_tokens_details.text_tokens == 17
    assert combined.completion_tokens_details.audio_tokens == 18


def test_realtime_logging_object_allows_null_transcript_in_conversation_item_added():
    results: OpenAIRealtimeStreamList = [
        {
            "type": "conversation.item.added",
            "event_id": "event_added",
            "item": {
                "id": "item_123",
                "type": "message",
                "role": "assistant",
                "status": "in_progress",
                "content": [{"type": "audio", "transcript": None}],
            },
        },
        {
            "type": "response.done",
            "event_id": "event_done",
            "response": {
                "id": "resp_123",
                "object": "realtime.response",
                "status": "completed",
                "usage": {"input_tokens": 11, "output_tokens": 7, "total_tokens": 18},
            },
        },
    ]

    usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results)
    logging_result = RealtimeAPITokenUsageProcessor.create_logging_realtime_object(
        usage=usage,
        results=results,
    )
    assert logging_result.usage.total_tokens == 18
    assert logging_result.results[0]["item"]["content"][0]["transcript"] is None
    assert logging_result.results[0]["item"]["content"][0]["transcript"] is None


def test_realtime_logging_object_does_not_validate_unknown_event_types():
    """
    A realtime session emits events outside the OpenAIRealtimeEvents union (e.g.
    rate_limits.updated, response.function_call_arguments.delta). Building the
    logging object must not revalidate every event against the union; doing so
    produces thousands of Pydantic ValidationErrors per session, blocks the event
    loop, and the raised error discards the session's usage. The events must
    survive verbatim, the combined usage must be preserved, and serialization
    must stay clean.
    """
    import warnings

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "event_id": "ev0", "session": {"id": "s"}},
    ]
    for i in range(50):
        results += [
            {
                "type": "rate_limits.updated",
                "event_id": f"rl{i}",
                "rate_limits": [{"name": "requests", "limit": 1000, "remaining": 900}],
            },
            {
                "type": "response.function_call_arguments.delta",
                "event_id": f"fc{i}",
                "delta": "{}",
            },
            {
                "type": "response.done",
                "event_id": f"rd{i}",
                "response": {
                    "usage": {
                        "input_tokens": 4,
                        "output_tokens": 6,
                        "total_tokens": 10,
                    }
                },
            },
        ]

    usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results)
    # On unfixed code this raises pydantic ValidationError instead of returning.
    logging_result = RealtimeAPITokenUsageProcessor.create_logging_realtime_object(
        usage=usage,
        results=results,
    )

    assert logging_result.usage.total_tokens == 500
    assert len(logging_result.results) == len(results)
    unknown_types = {
        r["type"]
        for r in logging_result.results
        if r["type"] in ("rate_limits.updated", "response.function_call_arguments.delta")
    }
    assert unknown_types == {
        "rate_limits.updated",
        "response.function_call_arguments.delta",
    }

    with warnings.catch_warnings():
        warnings.simplefilter("error")
        dumped = logging_result.model_dump()
    assert len(dumped["results"]) == len(results)


def test_realtime_transcription_honors_deployment_pricing_override(monkeypatch: pytest.MonkeyPatch) -> None:
    """A deployment's pricing override must reach transcription events too.

    Transcription is billed separately from response usage inside the same realtime
    session, so a deployment registered at zero rates has to zero both. Resolving
    transcription against the public ASR model instead billed a zero-rated
    deployment for every .completed event.
    """
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    deployment_id = "deployment-hash-zero-rated-asr"
    litellm.register_model(
        model_cost={
            deployment_id: {
                "litellm_provider": "openai",
                "mode": "realtime",
                "input_cost_per_second": 0.0,
                "input_cost_per_token": 0.0,
                "output_cost_per_token": 0.0,
                "input_cost_per_audio_token": 0.0,
            }
        }
    )

    results: OpenAIRealtimeStreamList = [
        {
            "type": "session.created",
            "session": {
                "type": "transcription",
                "audio": {"input": {"transcription": {"model": "gpt-realtime-whisper"}}},
            },
        },
        {
            "type": "conversation.item.input_audio_transcription.completed",
            "usage": {"type": "duration", "seconds": 120.0},
        },
    ]

    public_rate_cost = 120.0 * litellm.model_cost["gpt-realtime-whisper"]["input_cost_per_second"]
    assert public_rate_cost > 0, "the public ASR rate must be non-zero for this test to mean anything"

    without_override = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=Usage(),
        custom_llm_provider="openai",
        litellm_model_name="gpt-realtime-whisper",
    )
    assert abs(without_override - public_rate_cost) < 1e-9

    with_override = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=Usage(),
        custom_llm_provider="openai",
        litellm_model_name="gpt-realtime-whisper",
        custom_pricing_model=deployment_id,
    )
    assert with_override == 0.0, "the zero-rated deployment must not be billed for transcription"


def test_realtime_transcription_partial_override_keeps_unset_rates(monkeypatch: pytest.MonkeyPatch) -> None:
    """An override must not blank the rates it does not set.

    A deployment that prices tokens but omits input_cost_per_second would otherwise
    bill duration-based transcription at nothing, because the cost helpers read
    `.get(key) or 0.0`. Only the fields the operator actually set may win.
    """
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    deployment_id = "deployment-hash-tokens-only"
    litellm.register_model(
        model_cost={
            deployment_id: {
                "litellm_provider": "openai",
                "mode": "realtime",
                "input_cost_per_token": 0.0,
                "output_cost_per_token": 0.0,
            }
        }
    )

    results: OpenAIRealtimeStreamList = [
        {
            "type": "session.created",
            "session": {
                "type": "transcription",
                "audio": {"input": {"transcription": {"model": "gpt-realtime-whisper"}}},
            },
        },
        {
            "type": "conversation.item.input_audio_transcription.completed",
            "usage": {"type": "duration", "seconds": 120.0},
        },
    ]

    cost = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=Usage(),
        custom_llm_provider="openai",
        litellm_model_name="gpt-realtime-whisper",
        custom_pricing_model=deployment_id,
    )

    expected = 120.0 * litellm.model_cost["gpt-realtime-whisper"]["input_cost_per_second"]
    assert expected > 0, "the public ASR per-second rate must be non-zero for this test to mean anything"
    assert cost == pytest.approx(expected, rel=1e-9), (
        "duration must keep the ASR per-second rate the override left unset"
    )


@pytest.mark.parametrize(
    "label,override,expected_audio_rate,expected_per_second",
    [
        ("tokens only", {"input_cost_per_token": 0.0}, 0.0, 0.017 / 60),
        ("audio zeroed", {"input_cost_per_audio_token": 0.0}, 0.0, 0.017 / 60),
        ("per second only", {"input_cost_per_second": 0.001}, 6e-06, 0.001),
        ("empty override", {}, 6e-06, 0.017 / 60),
        ("no override", None, 6e-06, 0.017 / 60),
    ],
)
def test_transcription_rate_precedence(
    monkeypatch: pytest.MonkeyPatch,
    label: str,
    override: dict[str, float] | None,
    expected_audio_rate: float,
    expected_per_second: float,
) -> None:
    """Rates resolve within one entry before moving to the next, and zero is a real value.

    An override that prices only tokens must apply its own token rate to audio rather
    than reaching past itself for the public audio rate, a deliberate zero must win
    instead of being treated as unset, and a rate the override never mentions must keep
    the base entry's value.
    """
    from litellm.cost_calculator import handle_realtime_transcription_cost_calculation

    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    base_model = "asr-precedence-base"
    deployment_id = "asr-precedence-deployment"
    litellm.register_model(
        model_cost={
            base_model: {
                "litellm_provider": "openai",
                "mode": "audio_transcription",
                "input_cost_per_audio_token": 6e-06,
                "input_cost_per_token": 2.5e-06,
                "input_cost_per_second": 0.017 / 60,
            }
        }
    )
    if override is not None:
        litellm.register_model(
            model_cost={deployment_id: {"litellm_provider": "openai", "mode": "audio_transcription", **override}}
        )

    def cost_for(usage: dict[str, object]) -> float:
        return handle_realtime_transcription_cost_calculation(
            results=[
                {"type": "transcription_session.created", "session": {"model": base_model}},
                {"type": "conversation.item.input_audio_transcription.completed", "usage": usage},
            ],
            custom_llm_provider="openai",
            litellm_model_name=base_model,
            custom_pricing_model=deployment_id if override is not None else None,
        )

    audio_cost = cost_for({"type": "tokens", "input_token_details": {"audio_tokens": 100}})
    assert audio_cost == pytest.approx(100 * expected_audio_rate, rel=1e-9), f"{label}: audio rate"

    per_second_cost = cost_for({"type": "duration", "seconds": 120.0})
    assert per_second_cost == pytest.approx(120.0 * expected_per_second, rel=1e-9), (
        f"{label}: an override must never blank a rate it does not set"
    )


def test_realtime_transcription_per_second_override_keeps_public_token_rates(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    """A per-second override must not zero the token rates ``get_model_info`` synthesizes.

    ``get_model_info`` defaults input_cost_per_token and output_cost_per_token to 0 for entries
    that omit them, so a deployment priced only per second looked like it had declared token
    rates of 0. Token-shaped transcription then billed nothing instead of falling through to the
    public ASR rates, while the per-second rate the operator did set stayed in force.
    """
    from litellm.cost_calculator import handle_realtime_transcription_cost_calculation

    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    asr_model = "gpt-4o-transcribe"
    per_second_rate = 0.001
    deployment_id = "deployment-hash-per-second-only"
    litellm.register_model(
        model_cost={
            deployment_id: {
                "litellm_provider": "openai",
                "mode": "audio_transcription",
                "input_cost_per_second": per_second_rate,
            }
        }
    )

    public = litellm.model_cost[asr_model]
    session_event = {"type": "transcription_session.created", "session": {"model": asr_model}}

    def cost_for(usage: dict[str, object]) -> float:
        return handle_realtime_transcription_cost_calculation(
            results=[session_event, {"type": "conversation.item.input_audio_transcription.completed", "usage": usage}],
            custom_llm_provider="openai",
            litellm_model_name=asr_model,
            custom_pricing_model=deployment_id,
        )

    token_cost = cost_for(
        {
            "type": "tokens",
            "input_token_details": {"audio_tokens": 400, "text_tokens": 12},
            "output_tokens": 30,
        }
    )
    expected_token_cost = (
        400 * public["input_cost_per_audio_token"]
        + 12 * public["input_cost_per_token"]
        + 30 * public["output_cost_per_token"]
    )
    assert expected_token_cost > 0, "the public ASR token rates must be non-zero for this test to mean anything"
    assert token_cost == pytest.approx(expected_token_cost, rel=1e-9), (
        "an override that prices only seconds must leave the public token rates in place"
    )

    assert cost_for({"type": "duration", "seconds": 120.0}) == pytest.approx(120.0 * per_second_rate, rel=1e-9)


def test_realtime_transcription_no_completed_events_is_zero(monkeypatch):
    """A realtime stream without transcription completed events adds no extra cost."""
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    from litellm.cost_calculator import handle_realtime_transcription_cost_calculation

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": "gpt-realtime-whisper"}},
        {"type": "response.done", "response": {"usage": {}}},
    ]
    assert (
        handle_realtime_transcription_cost_calculation(
            results=results,
            custom_llm_provider="openai",
            litellm_model_name="gpt-realtime-whisper",
        )
        == 0.0
    )


def test_transcription_usage_cost_returns_zero_for_unknown_type():
    """An unrecognized usage type yields 0 (safe fallback, no exception)."""
    from litellm.cost_calculator import _transcription_usage_cost

    assert _transcription_usage_cost({"type": "future_billing_type"}, {}) == 0.0
    assert _transcription_usage_cost({}, {}) == 0.0


def test_get_transcription_model_falls_back_to_session_model(monkeypatch):
    """session.model is used when transcription-specific model fields are absent."""
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    from litellm.cost_calculator import _get_transcription_model_name_from_results

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": "gpt-realtime-whisper"}},
    ]
    assert _get_transcription_model_name_from_results(results) == "gpt-realtime-whisper"

    from litellm import Router

    router = Router(
        model_list=[
            {
                "model_name": "prod/claude-3-5-sonnet-20240620",
                "litellm_params": {
                    "model": "anthropic/claude-sonnet-4-5-20250929",
                    "api_key": "test_api_key",
                },
                "model_info": {
                    "id": "my-unique-model-id",
                    "input_cost_per_token": 0.000006,
                    "output_cost_per_token": 0.00003,
                    "cache_creation_input_token_cost": 0.0000075,
                    "cache_read_input_token_cost": 0.0000006,
                },
            },
            {
                "model_name": "claude-3-5-sonnet-20240620",
                "litellm_params": {
                    "model": "anthropic/claude-sonnet-4-5-20250929",
                    "api_key": "test_api_key",
                },
                "model_info": {
                    "input_cost_per_token": 100,
                    "output_cost_per_token": 200,
                },
            },
        ]
    )

    result = router.completion(
        model="claude-3-5-sonnet-20240620",
        messages=[{"role": "user", "content": "Hello, world!"}],
        mock_response=True,
    )

    result_2 = router.completion(
        model="prod/claude-3-5-sonnet-20240620",
        messages=[{"role": "user", "content": "Hello, world!"}],
        mock_response=True,
    )

    assert result._hidden_params["response_cost"] > result_2._hidden_params["response_cost"]

    model_info = router.get_deployment_model_info(
        model_id="my-unique-model-id", model_name="anthropic/claude-sonnet-4-5-20250929"
    )
    assert model_info is not None
    assert model_info["input_cost_per_token"] == 0.000006
    assert model_info["output_cost_per_token"] == 0.00003
    assert model_info["cache_creation_input_token_cost"] == 0.0000075
    assert model_info["cache_read_input_token_cost"] == 0.0000006


def test_custom_pricing_cost_calc_uses_router_model_id_from_litellm_metadata():
    """When custom pricing is in litellm_metadata.model_info,
    use_custom_pricing_for_model should return True and
    _select_model_name_for_cost_calc should use router_model_id.

    This tests the full chain that was broken for /messages and /responses
    endpoints. Regression test for #23185.
    """
    from litellm.cost_calculator import select_model_name_for_cost_calc
    from litellm.litellm_core_utils.litellm_logging import use_custom_pricing_for_model

    custom_model_id = "claude-sonnet-4-custom-pricing-test"
    custom_pricing_info = {
        "input_cost_per_token": 0.0003,
        "output_cost_per_token": 0.0015,
        "max_tokens": 8192,
        "litellm_provider": "anthropic",
    }
    litellm.register_model(model_cost={custom_model_id: custom_pricing_info})

    litellm_params = {
        "litellm_metadata": {
            "model_info": {
                "id": custom_model_id,
                "input_cost_per_token": 0.0003,
                "output_cost_per_token": 0.0015,
            },
        },
    }

    custom_pricing = use_custom_pricing_for_model(litellm_params)
    assert custom_pricing is True

    # _select_model_name_for_cost_calc appends provider prefix to the
    # selected router_model_id, so the result is "anthropic/<model_id>"
    selected_model = select_model_name_for_cost_calc(
        model="anthropic/claude-sonnet-4-20250514",
        completion_response=None,
        custom_pricing=custom_pricing,
        custom_llm_provider="anthropic",
        router_model_id=custom_model_id,
    )
    assert selected_model is not None
    assert custom_model_id in selected_model

    # Without custom_pricing, the router_model_id is NOT selected
    selected_model_no_custom = select_model_name_for_cost_calc(
        model="anthropic/claude-sonnet-4-20250514",
        completion_response=None,
        custom_pricing=False,
        custom_llm_provider="anthropic",
        router_model_id=custom_model_id,
    )
    assert custom_model_id not in (selected_model_no_custom or "")


def test_per_request_custom_pricing_with_router():
    """When custom pricing is passed as per-request kwargs (not in model_list),
    _select_model_name_for_cost_calc should fall back to the model name
    (where register_model stored the pricing) instead of the router_model_id
    (which has no pricing data).

    Regression test for the bug where response._hidden_params["response_cost"]
    returned 0.0 for per-request custom pricing via Router.
    """
    from litellm import Router
    from litellm.cost_calculator import select_model_name_for_cost_calc

    router = Router(
        model_list=[
            {
                "model_name": "openai/gpt-3.5-turbo",
                "litellm_params": {
                    "model": "openai/gpt-3.5-turbo",
                    "api_key": "test_api_key",
                },
            },
        ]
    )

    # Get the deployment's model_id (hash) that the router registered
    deployment = router.model_list[0]
    router_model_id = deployment["model_info"]["id"]

    # The router registered this hash in model_cost but without custom pricing
    assert router_model_id in litellm.model_cost
    entry = litellm.model_cost[router_model_id]
    # No custom pricing was set in model_list, so these should be None
    assert entry.get("input_cost_per_token") is None

    # Now simulate what completion() does: register custom pricing under the model name
    litellm.register_model(
        {
            "openai/gpt-3.5-turbo": {
                "input_cost_per_token": 2.0,
                "output_cost_per_token": 2.0,
                "litellm_provider": "openai",
            }
        }
    )

    # _select_model_name_for_cost_calc should pick the model name (which has pricing),
    # NOT the router_model_id (which has no pricing)
    selected = select_model_name_for_cost_calc(
        model="openai/gpt-3.5-turbo",
        completion_response=None,
        custom_pricing=True,
        custom_llm_provider="openai",
        router_model_id=router_model_id,
    )
    assert selected is not None
    assert router_model_id not in selected
    assert "gpt-3.5-turbo" in selected


def test_tiered_pricing_only_deployment_selects_router_model_id():
    """A deployment priced solely via ``tiered_pricing`` (no flat
    input/output cost) must resolve cost against its ``router_model_id``
    entry, which holds the tiered table, instead of the shared backend alias
    that has custom pricing fields stripped. Regression for tier-only models
    (e.g. dashscope/qwen3.7-plus) being billed as free.
    """
    from litellm import Router
    from litellm.cost_calculator import select_model_name_for_cost_calc

    router = Router(
        model_list=[
            {
                "model_name": "qwen-tier-only",
                "litellm_params": {
                    "model": "dashscope/qwen-tier-only-test",
                    "api_key": "sk-fake",
                },
                "model_info": {
                    "tiered_pricing": [
                        {
                            "input_cost_per_token": 4e-07,
                            "output_cost_per_token": 1.6e-06,
                            "range": [0, 256000],
                        },
                    ],
                },
            },
        ]
    )
    router_model_id = router.model_list[0]["model_info"]["id"]

    entry = litellm.model_cost[router_model_id]
    assert entry.get("input_cost_per_token") is None
    assert entry.get("tiered_pricing") is not None
    # The stripped shared alias must not carry tiered pricing.
    assert litellm.model_cost["dashscope/qwen-tier-only-test"].get("tiered_pricing") is None

    selected = select_model_name_for_cost_calc(
        model="dashscope/qwen-tier-only-test",
        completion_response=None,
        custom_pricing=True,
        custom_llm_provider="dashscope",
        router_model_id=router_model_id,
    )
    assert selected is not None
    assert router_model_id in selected


@pytest.mark.parametrize("metadata_key", ["metadata", "litellm_metadata"])
def test_completion_cost_image_generation_reads_deployment_model_info_price_from_logging_metadata(
    _local_model_cost_map: None, metadata_key: str
) -> None:
    cost = completion_cost(
        completion_response=ImageResponse(data=[ImageObject(url="https://example.com/img.png")]),
        model="fal_ai/fal-ai/unlisted-image-model",
        call_type="image_generation",
        custom_pricing=True,
        litellm_logging_obj=SimpleNamespace(
            litellm_params={metadata_key: {"model_info": {"output_cost_per_image": 0.08}}}
        ),
    )

    assert cost == pytest.approx(0.08)


def test_completion_cost_image_generation_registered_deployment_price_keeps_map_token_rates(
    _local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch
) -> None:
    deployment_id: Final = "gemini-image-deployment-priced-per-image"
    monkeypatch.setitem(
        litellm.model_cost,
        deployment_id,
        {"mode": "image_generation", "litellm_provider": "gemini", "output_cost_per_image": 0.1},
    )
    map_model: Final = "gemini/gemini-3.1-flash-image"
    row: Final = litellm.model_cost[map_model]
    usage: Final = ImageUsage(
        input_tokens=10,
        input_tokens_details=ImageUsageInputTokensDetails(image_tokens=0, text_tokens=10),
        output_tokens=1290,
        total_tokens=1300,
    )

    cost = completion_cost(
        completion_response=ImageResponse(data=[ImageObject(url="https://example.com/img.png")], usage=usage),
        model=map_model,
        custom_llm_provider="gemini",
        call_type="image_generation",
        custom_pricing=True,
        router_model_id=deployment_id,
        litellm_logging_obj=SimpleNamespace(litellm_params={"metadata": {"model_info": {"id": deployment_id}}}),
    )

    expected: Final = (
        usage.input_tokens * row["input_cost_per_token"] + usage.output_tokens * row["output_cost_per_image_token"]
    )
    assert cost == pytest.approx(expected)


def test_completion_cost_image_generation_ignores_deployment_model_info_without_custom_pricing(
    _local_model_cost_map: None,
) -> None:
    cost = completion_cost(
        completion_response=ImageResponse(data=[ImageObject(url="https://example.com/img.png")]),
        model="fal_ai/openai/gpt-image-2",
        call_type="image_generation",
        custom_pricing=False,
        optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
        litellm_logging_obj=SimpleNamespace(
            litellm_params={"litellm_metadata": {"model_info": {"output_cost_per_image": 0.5}}}
        ),
    )

    assert cost == pytest.approx(0.211)


async def test_router_image_generation_bills_litellm_params_output_cost_per_image() -> None:
    from litellm import Router

    router = Router(
        model_list=[
            {
                "model_name": "img",
                "litellm_params": {
                    "model": "fal_ai/fal-ai/unlisted-image-model",
                    "api_key": "sk-fake",
                    "output_cost_per_image": 0.08,
                },
            }
        ]
    )

    response = await router.aimage_generation(model="img", prompt="x", mock_response="https://example.com/img.png")

    assert response._hidden_params["response_cost"] == pytest.approx(0.08)


def test_tiered_pricing_only_deployment_completion_cost_is_nonzero():
    """End-to-end: a tier-only deployment must produce the tiered cost, not
    $0. Mirrors the reported dashscope/qwen3.7-plus trace (12 prompt + 377
    completion tokens).
    """
    from litellm import Router
    from litellm.types.utils import Choices, Message

    router = Router(
        model_list=[
            {
                "model_name": "qwen-3.7-plus",
                "litellm_params": {
                    "model": "dashscope/qwen3.7-plus",
                    "api_key": "sk-fake",
                },
                "model_info": {
                    "tiered_pricing": [
                        {
                            "input_cost_per_token": 4e-07,
                            "output_cost_per_token": 1.6e-06,
                            "range": [0, 256000],
                        },
                        {
                            "input_cost_per_token": 1.2e-06,
                            "output_cost_per_token": 4.8e-06,
                            "range": [256000, 1000000],
                        },
                    ],
                },
            },
        ]
    )
    router_model_id = router.model_list[0]["model_info"]["id"]

    response = ModelResponse(
        model="dashscope/qwen3.7-plus",
        choices=[Choices(index=0, message=Message(role="assistant", content="hi"))],
        usage=Usage(prompt_tokens=12, completion_tokens=377, total_tokens=389),
    )
    response._hidden_params = {"custom_llm_provider": "dashscope", "model_id": router_model_id}

    cost = completion_cost(
        completion_response=response,
        model="dashscope/qwen3.7-plus",
        custom_llm_provider="dashscope",
        custom_pricing=True,
        router_model_id=router_model_id,
    )

    expected = 12 * 4e-07 + 377 * 1.6e-06
    assert cost == pytest.approx(expected)
    assert cost > 0


def test_per_query_priced_rerank_deployment_completion_cost_is_nonzero():
    """A rerank deployment priced only via ``input_cost_per_query`` must resolve
    cost against its ``router_model_id`` entry: the shared backend alias has
    custom pricing stripped, so pricing it there bills every search unit as $0.
    """
    from litellm import Router

    router: Final = Router(
        model_list=[
            {
                "model_name": "semantic-ranker-custom-test",
                "litellm_params": {
                    "model": "vertex_ai/semantic-ranker-custom-test",
                    "vertex_project": "test-project",
                    "vertex_location": "us-east5",
                },
                "model_info": {"input_cost_per_query": 0.001},
            },
        ]
    )
    router_model_id: Final = router.model_list[0]["model_info"]["id"]
    assert litellm.model_cost.get("vertex_ai/semantic-ranker-custom-test", {}).get("input_cost_per_query") is None

    response: Final = RerankResponse(
        id="vertex_ai_rerank_test",
        results=[{"index": 3, "relevance_score": 0.48}],
        meta={"billed_units": {"search_units": 3}},
    )

    cost: Final = completion_cost(
        completion_response=response,
        model="vertex_ai/semantic-ranker-custom-test",
        custom_llm_provider="vertex_ai",
        call_type="arerank",
        custom_pricing=True,
        router_model_id=router_model_id,
    )

    assert cost == pytest.approx(3 * 0.001)


@pytest.mark.parametrize(
    "model",
    (
        "vertex_ai/semantic-ranker-default",
        "vertex_ai/semantic-ranker-default@latest",
        "vertex_ai/semantic-ranker-default-004",
        "vertex_ai/semantic-ranker-default-003",
        "vertex_ai/semantic-ranker-default-002",
        "vertex_ai/semantic-ranker-fast",
        "vertex_ai/semantic-ranker-fast@latest",
        "vertex_ai/semantic-ranker-fast-004",
    ),
)
def test_vertex_semantic_ranker_rerank_completion_cost_is_nonzero(
    model: str, _local_model_cost_map: None
) -> None:
    response: Final = RerankResponse(
        id="vertex_ai_rerank_pricing_test",
        results=[],
        meta={"billed_units": {"search_units": 3}},
    )

    cost: Final = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="vertex_ai",
        call_type="rerank",
    )

    assert cost > 0
    assert cost == pytest.approx(3 * litellm.model_cost[model]["input_cost_per_query"])


def test_azure_realtime_cost_calculator(_local_model_cost_map):

    cost = handle_realtime_stream_cost_calculation(
        results=[
            {
                "type": "session.created",
                "session": {"model": "gpt-4o-realtime-preview-2024-12-17"},
            },
        ],
        combined_usage_object=Usage(
            prompt_tokens=100,
            completion_tokens=100,
            prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=10, audio_tokens=90),
        ),
        custom_llm_provider="azure",
        litellm_model_name="my-custom-azure-deployment",
    )

    assert cost > 0


def test_azure_audio_output_cost_calculation(_local_model_cost_map):
    """
    Test that Azure audio models correctly calculate costs for audio output tokens.

    Reproduces issue: https://github.com/BerriAI/litellm/issues/19764
    Audio tokens should be charged at output_cost_per_audio_token rate,
    not at the text token rate (output_cost_per_token).
    """
    from litellm.types.utils import Choices, CompletionTokensDetailsWrapper, Message

    # Scenario from issue #19764:
    # Input: 17 text tokens, 0 audio tokens
    # Output: 110 text tokens, 482 audio tokens
    usage_object = Usage(
        prompt_tokens=17,
        completion_tokens=592,  # 110 text + 482 audio
        total_tokens=609,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            audio_tokens=0,
            cached_tokens=0,
            text_tokens=17,
            image_tokens=0,
        ),
        completion_tokens_details=CompletionTokensDetailsWrapper(
            audio_tokens=482,
            reasoning_tokens=0,
            text_tokens=110,
        ),
    )

    completion = ModelResponse(
        id="test-azure-audio-cost",
        choices=[
            Choices(
                finish_reason="stop",
                index=0,
                message=Message(
                    content="Test response",
                    role="assistant",
                ),
            )
        ],
        created=1729282652,
        model="azure/gpt-audio-2025-08-28",
        object="chat.completion",
        usage=usage_object,
    )

    cost = completion_cost(completion, model="azure/gpt-audio-2025-08-28")

    model_info = litellm.get_model_info("azure/gpt-audio-2025-08-28")

    # Calculate expected cost
    expected_input_cost = model_info["input_cost_per_token"] * 17  # text tokens
    expected_output_cost = (
        model_info["output_cost_per_token"] * 110  # text tokens
        + model_info["output_cost_per_audio_token"] * 482  # audio tokens
    )
    expected_total_cost = expected_input_cost + expected_output_cost

    # The bug was: all output tokens charged at text rate
    wrong_output_cost = model_info["output_cost_per_token"] * 592
    wrong_total_cost = expected_input_cost + wrong_output_cost

    # Verify audio tokens are NOT charged at text rate (the bug)
    assert abs(cost - wrong_total_cost) > 0.001, "Bug: Audio tokens are being charged at text token rate"

    # Verify cost matches
    assert abs(cost - expected_total_cost) < 0.0000001, f"Expected cost {expected_total_cost}, got {cost}"


def test_default_image_cost_calculator(monkeypatch):
    from litellm.cost_calculator import default_image_cost_calculator

    temp_object = {
        "litellm_provider": "azure",
        "input_cost_per_pixel": 10,
    }

    monkeypatch.setattr(
        litellm,
        "model_cost",
        {"azure/bf9001cd7209f5734ecb4ab937a5a0e2ba5f119708bd68f184db362930f9dc7b": temp_object},
    )

    args = {
        "model": "azure/bf9001cd7209f5734ecb4ab937a5a0e2ba5f119708bd68f184db362930f9dc7b",
        "custom_llm_provider": "azure",
        "quality": "standard",
        "n": 1,
        "size": "1024-x-1024",
        "optional_params": {},
    }
    cost = default_image_cost_calculator(**args)
    assert cost == 10485760


@pytest.mark.parametrize(
    ("model", "quality", "size", "priced_key", "pixels"),
    [
        ("azure/dall-e-3", "standard", "1024x1024", "azure/standard/1024-x-1024/dall-e-3", 1024 * 1024),
        ("azure/dall-e-3", "hd", "1024x1792", "azure/hd/1024-x-1792/dall-e-3", 1024 * 1792),
        ("dall-e-3", "hd", "1024x1792", "azure/hd/1024-x-1792/dall-e-3", 1024 * 1792),
    ],
)
def test_default_image_cost_calculator_matches_provider_first_quality_key(
    monkeypatch, model: str, quality: str, size: str, priced_key: str, pixels: int
):
    from litellm.cost_calculator import default_image_cost_calculator

    monkeypatch.setattr(
        litellm,
        "model_cost",
        {
            "azure/standard/1024-x-1024/dall-e-3": {"litellm_provider": "azure", "input_cost_per_pixel": 1e-08},
            "azure/hd/1024-x-1792/dall-e-3": {"litellm_provider": "azure", "input_cost_per_pixel": 3e-08},
        },
    )

    cost = default_image_cost_calculator(
        model=model,
        custom_llm_provider="azure",
        quality=quality,
        n=1,
        size=size,
        optional_params={},
    )

    assert cost == litellm.model_cost[priced_key]["input_cost_per_pixel"] * pixels


def test_cost_calculator_with_cache_creation():
    from litellm import completion_cost
    from litellm.types.utils import Choices, Message, Usage

    litellm_model_response = ModelResponse(
        id="chatcmpl-cc5638bc-fdfe-48e4-8884-57c8f4fb7c63",
        created=1750733889,
        model=None,
        object="chat.completion",
        system_fingerprint=None,
        choices=[
            Choices(
                finish_reason="stop",
                index=0,
                message=Message(
                    content="Hello! How can I help you today?",
                    role="assistant",
                    tool_calls=None,
                    function_call=None,
                    provider_specific_fields=None,
                ),
            )
        ],
        usage=Usage(
            **{
                "total_tokens": 28508,
                "prompt_tokens": 28495,
                "completion_tokens": 13,
                "prompt_tokens_details": {"audio_tokens": None, "cached_tokens": 0},
                "cache_read_input_tokens": 28491,
                "completion_tokens_details": {
                    "audio_tokens": None,
                    "reasoning_tokens": 0,
                    "accepted_prediction_tokens": None,
                    "rejected_prediction_tokens": None,
                },
                "cache_creation_input_tokens": 15,
            }
        ),
    )
    model = "claude-sonnet-4@20250514"

    assert litellm_model_response.usage.prompt_tokens_details.cached_tokens == 28491

    result = completion_cost(
        completion_response=litellm_model_response,
        model=model,
        custom_llm_provider="vertex_ai",
    )

    print(result)


def test_bedrock_cost_calculator_comparison_with_without_cache():
    """Test that Bedrock caching reduces costs compared to non-cached requests"""
    from litellm import completion_cost
    from litellm.types.utils import Choices, Message, Usage

    # Response WITHOUT caching
    response_no_cache = ModelResponse(
        id="msg_no_cache",
        created=1750733889,
        model="anthropic.claude-sonnet-4-20250514-v1:0",
        object="chat.completion",
        choices=[
            Choices(
                finish_reason="stop",
                index=0,
                message=Message(
                    content="Response without cache",
                    role="assistant",
                ),
            )
        ],
        usage=Usage(
            total_tokens=28508,
            prompt_tokens=28495,
            completion_tokens=13,
        ),
    )

    # Response WITH caching (same total tokens, but most are cached)
    response_with_cache = ModelResponse(
        id="msg_with_cache",
        created=1750733889,
        model="anthropic.claude-sonnet-4-20250514-v1:0",
        object="chat.completion",
        choices=[
            Choices(
                finish_reason="stop",
                index=0,
                message=Message(
                    content="Response with cache",
                    role="assistant",
                ),
            )
        ],
        usage=Usage(
            **{
                "total_tokens": 28508,
                "prompt_tokens": 28495,
                "completion_tokens": 13,
                "prompt_tokens_details": {"audio_tokens": None, "cached_tokens": 0},
                "cache_read_input_tokens": 28491,  # Most tokens are read from cache (cheaper)
                "completion_tokens_details": {
                    "audio_tokens": None,
                    "reasoning_tokens": 0,
                    "accepted_prediction_tokens": None,
                    "rejected_prediction_tokens": None,
                },
                "cache_creation_input_tokens": 15,  # Only 15 new tokens added to cache
            }
        ),
    )

    # Calculate costs
    cost_no_cache = completion_cost(
        completion_response=response_no_cache,
        model="bedrock/anthropic.claude-sonnet-4-20250514-v1:0",
        custom_llm_provider="bedrock",
    )

    cost_with_cache = completion_cost(
        completion_response=response_with_cache,
        model="bedrock/anthropic.claude-sonnet-4-20250514-v1:0",
        custom_llm_provider="bedrock",
    )

    # Verify that cached request is cheaper
    assert cost_with_cache < cost_no_cache
    print(f"Cost without cache: {cost_no_cache}")
    print(f"Cost with cache: {cost_with_cache}")


def test_gemini_25_explicit_caching_cost_direct_usage():
    """
    Test that Gemini 2.5 models correctly calculate costs with explicit caching.

    This test reproduces the issue from #11156 where cached tokens should receive
    a 75% discount.
    """
    from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
    from litellm.types.utils import (
        CompletionTokensDetailsWrapper,
        PromptTokensDetailsWrapper,
        Usage,
    )
    from litellm.utils import get_model_info

    model_info = get_model_info(model="gemini-2.5-pro", custom_llm_provider="gemini")

    usage = Usage(
        completion_tokens=2522,
        prompt_tokens=42001,
        total_tokens=44523,
        completion_tokens_details=CompletionTokensDetailsWrapper(
            accepted_prediction_tokens=None,
            audio_tokens=None,
            reasoning_tokens=1908,
            rejected_prediction_tokens=None,
            text_tokens=614,
        ),
        prompt_tokens_details=PromptTokensDetailsWrapper(
            audio_tokens=None, cached_tokens=40938, text_tokens=1063, image_tokens=None
        ),
    )

    input_cost, output_cost = generic_cost_per_token(
        model="gemini/gemini-2.5-pro",
        usage=usage,
        custom_llm_provider="gemini",
    )

    total_cost = input_cost + output_cost

    expected_higher_than_actual_cost = (
        model_info["input_cost_per_token"] * usage.prompt_tokens
        + model_info["output_cost_per_token"] * usage.completion_tokens
    )

    print(f"expected_higher_than_actual_cost: {expected_higher_than_actual_cost}")

    assert expected_higher_than_actual_cost > total_cost

    expected_actual_cost = (
        model_info["input_cost_per_token"] * usage.prompt_tokens_details.text_tokens
        + model_info["cache_read_input_token_cost"] * usage.prompt_tokens_details.cached_tokens
        + model_info["output_cost_per_token"] * usage.completion_tokens
    )

    print(
        f"model_info['input_cost_per_token']: {model_info['input_cost_per_token']}, usage.prompt_tokens_details.text_tokens: {usage.prompt_tokens_details.text_tokens}, model_info['cache_read_input_token_cost']: {model_info['cache_read_input_token_cost']}, model_info['output_cost_per_token']: {model_info['output_cost_per_token']}"
    )

    print(f"Expected actual cost: {expected_actual_cost}")

    assert expected_actual_cost == total_cost


def test_azure_ai_cache_cost_calculation(_local_model_cost_map):
    """
    Test that azure_ai provider correctly calculates cache costs using generic_cost_per_token.

    This verifies that azure_ai models with custom cache pricing in model_info
    will have their cache_creation_input_token_cost and cache_read_input_token_cost
    applied correctly.
    """
    from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
    from litellm.types.utils import PromptTokensDetailsWrapper, Usage

    # Register a custom azure_ai model with cache pricing
    test_model_id = "test-azure-ai-claude-model"
    litellm.register_model(
        model_cost={
            test_model_id: {
                "input_cost_per_token": 5.0e-06,
                "output_cost_per_token": 2.5e-05,
                "cache_creation_input_token_cost": 6.25e-06,
                "cache_read_input_token_cost": 5.0e-07,
                "litellm_provider": "azure_ai",
                "max_tokens": 200000,
            }
        }
    )

    # Create usage with cache tokens
    usage = Usage(
        completion_tokens=100,
        prompt_tokens=1000,
        total_tokens=1100,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            cached_tokens=800,  # 800 cache read tokens
            text_tokens=100,  # 100 regular text tokens
        ),
        cache_creation_input_tokens=100,  # 100 cache creation tokens
    )

    input_cost, output_cost = generic_cost_per_token(
        model=test_model_id,
        usage=usage,
        custom_llm_provider="azure_ai",
    )

    total_cost = input_cost + output_cost

    # Calculate expected cost manually
    model_info = litellm.model_cost[test_model_id]
    expected_input_cost = (
        model_info["input_cost_per_token"] * 100  # text tokens
        + model_info["cache_read_input_token_cost"] * 800  # cached tokens
        + model_info["cache_creation_input_token_cost"] * 100  # cache creation tokens
    )
    expected_output_cost = model_info["output_cost_per_token"] * 100

    print(f"Input cost: {input_cost}, Expected: {expected_input_cost}")
    print(f"Output cost: {output_cost}, Expected: {expected_output_cost}")
    print(f"Total cost: {total_cost}")

    assert abs(input_cost - expected_input_cost) < 1e-10, (
        f"Input cost mismatch: got {input_cost}, expected {expected_input_cost}"
    )
    assert abs(output_cost - expected_output_cost) < 1e-10, (
        f"Output cost mismatch: got {output_cost}, expected {expected_output_cost}"
    )


def test_vertex_regional_deployment_costs_uplift_over_global(monkeypatch):
    """
    Regression for https://github.com/BerriAI/litellm/issues/34393: two Vertex
    deployments differing only in vertex_location must not price identically.
    Google bills non-global endpoints at 1.1x for regional-pricing models, so the
    regional request costs 1.1x the global one for the exact same usage, through
    both vertex cost routes (Claude via cost_per_token, Gemini via
    cost_per_character's token fallback).
    """
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    usage = Usage(prompt_tokens=15, completion_tokens=5, total_tokens=20)
    for model in ("claude-haiku-4-5@20251001", "gemini-3.5-flash", "gemini-3.1-flash-image"):
        global_prompt, global_completion = cost_per_token(
            model=model,
            custom_llm_provider="vertex_ai",
            usage_object=usage,
            vertex_location="global",
        )
        regional_prompt, regional_completion = cost_per_token(
            model=model,
            custom_llm_provider="vertex_ai",
            usage_object=usage,
            vertex_location="us-east5",
        )
        global_total = global_prompt + global_completion
        regional_total = regional_prompt + regional_completion
        assert global_total > 0
        assert regional_total == pytest.approx(global_total * 1.10, rel=1e-9), (
            f"{model}: regional Vertex request must cost 1.1x the global one"
        )


@pytest.mark.parametrize(
    "usage",
    [
        ImageUsage(
            input_tokens=100,
            input_tokens_details=ImageUsageInputTokensDetails(image_tokens=0, text_tokens=100),
            output_tokens=1120,
            total_tokens=1220,
        ),
        None,
    ],
    ids=["token-priced", "per-image-fallback"],
)
def test_vertex_regional_image_generation_costs_uplift_over_global(monkeypatch, usage):
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(
        litellm,
        "model_cost",
        {
            **litellm.get_model_cost_map(url=""),
            "vertex_ai/fake-regional-image-model": {
                "litellm_provider": "vertex_ai-language-models",
                "mode": "image_generation",
                "input_cost_per_token": 5e-07,
                "output_cost_per_token": 3e-06,
                "output_cost_per_image_token": 6e-05,
                "output_cost_per_image": 0.0672,
                "regional_endpoint_uplift_multiplier": 1.1,
            },
        },
    )

    def image_cost(vertex_location: str) -> float:
        return completion_cost(
            completion_response=ImageResponse(data=[ImageObject(b64_json="img")], usage=usage),
            model="vertex_ai/fake-regional-image-model",
            call_type="image_generation",
            vertex_location=vertex_location,
        )

    global_cost: Final = image_cost("global")
    assert global_cost > 0
    assert image_cost("us-central1") == pytest.approx(global_cost * 1.10, rel=1e-9)


def test_vertex_gemini_flash_image_generation_regional_costs_uplift_over_global(monkeypatch):
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
    usage: Final = ImageUsage(
        input_tokens=100,
        input_tokens_details=ImageUsageInputTokensDetails(image_tokens=0, text_tokens=100),
        output_tokens=1120,
        total_tokens=1220,
    )

    def image_cost(vertex_location: str) -> float:
        return completion_cost(
            completion_response=ImageResponse(data=[ImageObject(b64_json="img")], usage=usage),
            model="vertex_ai/gemini-3.1-flash-image",
            call_type="image_generation",
            vertex_location=vertex_location,
        )

    global_cost: Final = image_cost("global")
    assert global_cost > 0
    assert image_cost("us-central1") == pytest.approx(global_cost * 1.10, rel=1e-9)


def test_vertex_uplift_composes_with_above_128k_pricing(monkeypatch):
    """The regional-endpoint uplift multiplies whatever rate the request priced at,
    including the above-128k dynamic rates, so a synthetic model carrying both keys
    prices regional above-128k usage at 1.1x the above-128k rate."""
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(
        litellm,
        "model_cost",
        {
            **litellm.get_model_cost_map(url=""),
            "vertex_ai/fake-regional-128k-model": {
                "litellm_provider": "vertex_ai",
                "mode": "chat",
                "input_cost_per_token": 1e-06,
                "output_cost_per_token": 2e-06,
                "input_cost_per_token_above_128k_tokens": 2e-06,
                "output_cost_per_token_above_128k_tokens": 4e-06,
                "regional_endpoint_uplift_multiplier": 1.1,
            },
        },
    )

    usage = Usage(prompt_tokens=200_000, completion_tokens=10, total_tokens=200_010)
    global_prompt, global_completion = cost_per_token(
        model="fake-regional-128k-model",
        custom_llm_provider="vertex_ai",
        usage_object=usage,
        vertex_location="global",
    )
    regional_prompt, regional_completion = cost_per_token(
        model="fake-regional-128k-model",
        custom_llm_provider="vertex_ai",
        usage_object=usage,
        vertex_location="europe-west1",
    )

    assert global_prompt == pytest.approx(200_000 * 2e-06, rel=1e-9)
    assert regional_prompt == pytest.approx(global_prompt * 1.10, rel=1e-9)
    assert regional_completion == pytest.approx(global_completion * 1.10, rel=1e-9)


def test_cost_discount_vertex_ai(monkeypatch):
    """
    Test that cost discount is applied correctly for Vertex AI provider
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    # Create mock response (use a model that exists in model_prices_and_context_window.json)
    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gemini-3-pro-preview",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Calculate cost without discount
    monkeypatch.setattr(litellm, "cost_discount_config", {})
    cost_without_discount = completion_cost(
        completion_response=response,
        model="vertex_ai/gemini-3-pro-preview",
        custom_llm_provider="vertex_ai",
    )

    # Set 5% discount for vertex_ai
    monkeypatch.setattr(litellm, "cost_discount_config", {"vertex_ai": 0.05})

    # Calculate cost with discount
    cost_with_discount = completion_cost(
        completion_response=response,
        model="vertex_ai/gemini-3-pro-preview",
        custom_llm_provider="vertex_ai",
    )

    # Verify discount is applied (5% off means 95% of original cost)
    expected_cost = cost_without_discount * 0.95
    assert cost_with_discount == pytest.approx(expected_cost, rel=1e-9)

    print("✓ Cost discount test passed:")
    print(f"  - Original cost: ${cost_without_discount:.6f}")
    print(f"  - Discounted cost (5% off): ${cost_with_discount:.6f}")
    print(f"  - Savings: ${cost_without_discount - cost_with_discount:.6f}")


def test_cost_discount_model_pattern_beats_bare_provider(monkeypatch):
    """
    Test that a <provider>/<model-pattern> discount key applies only to matching models
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    claude_response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="claude-sonnet-4-5",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )
    gemini_response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gemini-3-pro-preview",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    monkeypatch.setattr(litellm, "cost_discount_config", {})
    claude_undiscounted = completion_cost(
        completion_response=claude_response,
        model="vertex_ai/claude-sonnet-4-5",
        custom_llm_provider="vertex_ai",
    )
    gemini_undiscounted = completion_cost(
        completion_response=gemini_response,
        model="vertex_ai/gemini-3-pro-preview",
        custom_llm_provider="vertex_ai",
    )

    monkeypatch.setattr(litellm, "cost_discount_config", {"vertex_ai/claude-*": 0.2, "vertex_ai": 0.05})
    claude_discounted = completion_cost(
        completion_response=claude_response,
        model="vertex_ai/claude-sonnet-4-5",
        custom_llm_provider="vertex_ai",
    )
    gemini_discounted = completion_cost(
        completion_response=gemini_response,
        model="vertex_ai/gemini-3-pro-preview",
        custom_llm_provider="vertex_ai",
    )

    assert claude_discounted == pytest.approx(claude_undiscounted * 0.8, rel=1e-9)
    assert gemini_discounted == pytest.approx(gemini_undiscounted * 0.95, rel=1e-9)


def test_cost_discount_model_pattern_matches_custom_priced_deployment(monkeypatch):
    """
    A custom-priced deployment prices against its router model id, but the discount still matches the request model
    """
    from litellm import Router, completion_cost
    from litellm.types.utils import Choices, Message, Usage

    router: Final = Router(
        model_list=[
            {
                "model_name": "qwen-3.7-plus",
                "litellm_params": {"model": "dashscope/qwen3.7-plus", "api_key": "sk-fake"},
                "model_info": {"input_cost_per_token": 4e-07, "output_cost_per_token": 1.6e-06},
            }
        ]
    )
    router_model_id: Final = router.model_list[0]["model_info"]["id"]
    response: Final = ModelResponse(
        model="dashscope/qwen3.7-plus",
        choices=[Choices(index=0, message=Message(role="assistant", content="hi"))],
        usage=Usage(prompt_tokens=12, completion_tokens=377, total_tokens=389),
    )

    monkeypatch.setattr(litellm, "cost_discount_config", {"dashscope/qwen*": 0.2})
    discounted: Final = completion_cost(
        completion_response=response,
        model="dashscope/qwen3.7-plus",
        custom_llm_provider="dashscope",
        custom_pricing=True,
        router_model_id=router_model_id,
    )

    assert discounted == pytest.approx((12 * 4e-07 + 377 * 1.6e-06) * 0.8, rel=1e-9)


def test_cost_discount_together_ai_pattern_matches_unrewritten_model(monkeypatch):
    """
    Test that a <provider>/<model-pattern> discount matches the request model, not the
    pricing category the Together AI cost lookup rewrites it to
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="together_ai/my-org/Custom-70B-Instruct",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    monkeypatch.setattr(litellm, "cost_discount_config", {})
    undiscounted = completion_cost(
        completion_response=response,
        model="together_ai/my-org/Custom-70B-Instruct",
        custom_llm_provider="together_ai",
    )
    assert undiscounted > 0

    monkeypatch.setattr(
        litellm,
        "cost_discount_config",
        {"together_ai": 0.05, "together_ai/my-org/Custom-*": 0.20},
    )
    discounted = completion_cost(
        completion_response=response,
        model="together_ai/my-org/Custom-70B-Instruct",
        custom_llm_provider="together_ai",
    )

    assert discounted == pytest.approx(undiscounted * 0.8, rel=1e-9)


def test_cost_discount_not_applied_to_other_providers(monkeypatch):
    """
    Test that cost discount only applies to configured providers
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    # Create mock response for OpenAI
    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gpt-4",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Set discount only for vertex_ai (not openai)
    monkeypatch.setattr(litellm, "cost_discount_config", {"vertex_ai": 0.05})

    # Calculate cost for OpenAI - should NOT have discount applied
    cost_with_selective_discount = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Clear discount config
    monkeypatch.setattr(litellm, "cost_discount_config", {})
    cost_without_discount = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Costs should be the same (no discount applied to OpenAI)
    assert cost_with_selective_discount == cost_without_discount

    print("✓ Selective discount test passed:")
    print(f"  - OpenAI cost (no discount configured): ${cost_without_discount:.6f}")
    print(f"  - Cost remains unchanged: ${cost_with_selective_discount:.6f}")


def test_cost_margin_percentage(monkeypatch):
    """
    Test that percentage-based cost margin is applied correctly
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    # Create mock response
    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gpt-4",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Calculate cost without margin
    monkeypatch.setattr(litellm, "cost_margin_config", {})
    cost_without_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Set 10% margin for openai
    monkeypatch.setattr(litellm, "cost_margin_config", {"openai": 0.10})

    # Calculate cost with margin
    cost_with_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Verify margin is applied (10% margin means 110% of original cost)
    expected_cost = cost_without_margin * 1.10
    assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9)

    print("✓ Cost margin percentage test passed:")
    print(f"  - Original cost: ${cost_without_margin:.6f}")
    print(f"  - Cost with margin (10%): ${cost_with_margin:.6f}")
    print(f"  - Margin added: ${cost_with_margin - cost_without_margin:.6f}")


def test_cost_margin_fixed_amount(monkeypatch):
    """
    Test that fixed amount cost margin is applied correctly
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    # Create mock response
    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gpt-4",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Calculate cost without margin
    monkeypatch.setattr(litellm, "cost_margin_config", {})
    cost_without_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Set $0.001 fixed margin for openai
    monkeypatch.setattr(litellm, "cost_margin_config", {"openai": {"fixed_amount": 0.001}})

    # Calculate cost with margin
    cost_with_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Verify fixed margin is applied
    expected_cost = cost_without_margin + 0.001
    assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9)

    print("✓ Cost margin fixed amount test passed:")
    print(f"  - Original cost: ${cost_without_margin:.6f}")
    print(f"  - Cost with margin ($0.001): ${cost_with_margin:.6f}")
    print(f"  - Margin added: ${cost_with_margin - cost_without_margin:.6f}")


def test_cost_margin_combined(monkeypatch):
    """
    Test that combined percentage and fixed amount margin is applied correctly
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    # Create mock response
    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gpt-4",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Calculate cost without margin
    monkeypatch.setattr(litellm, "cost_margin_config", {})
    cost_without_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Set 8% margin + $0.0005 fixed for openai
    monkeypatch.setattr(litellm, "cost_margin_config", {"openai": {"percentage": 0.08, "fixed_amount": 0.0005}})

    # Calculate cost with margin
    cost_with_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Verify combined margin is applied
    expected_cost = cost_without_margin * 1.08 + 0.0005
    assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9)

    print("✓ Cost margin combined test passed:")
    print(f"  - Original cost: ${cost_without_margin:.6f}")
    print(f"  - Cost with margin (8% + $0.0005): ${cost_with_margin:.6f}")
    print(f"  - Margin added: ${cost_with_margin - cost_without_margin:.6f}")


def test_cost_margin_global(monkeypatch):
    """
    Test that global margin is applied when no provider-specific margin is configured
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    # Create mock response
    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gpt-4",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Calculate cost without margin
    monkeypatch.setattr(litellm, "cost_margin_config", {})
    cost_without_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Set 5% global margin (no provider-specific margin)
    monkeypatch.setattr(litellm, "cost_margin_config", {"global": 0.05})

    # Calculate cost with global margin
    cost_with_global_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Verify global margin is applied
    expected_cost = cost_without_margin * 1.05
    assert cost_with_global_margin == pytest.approx(expected_cost, rel=1e-9)

    print("✓ Cost margin global test passed:")
    print(f"  - Original cost: ${cost_without_margin:.6f}")
    print(f"  - Cost with global margin (5%): ${cost_with_global_margin:.6f}")
    print(f"  - Margin added: ${cost_with_global_margin - cost_without_margin:.6f}")


def test_cost_margin_provider_overrides_global(monkeypatch):
    """
    Test that provider-specific margin overrides global margin
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    # Create mock response
    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gpt-4",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Calculate cost without margin
    monkeypatch.setattr(litellm, "cost_margin_config", {})
    cost_without_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Set 5% global margin and 10% provider-specific margin
    monkeypatch.setattr(litellm, "cost_margin_config", {"global": 0.05, "openai": 0.10})

    # Calculate cost - should use provider-specific margin (10%), not global (5%)
    cost_with_provider_margin = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Verify provider-specific margin is used (not global)
    expected_cost = cost_without_margin * 1.10  # 10% from provider, not 5% from global
    assert cost_with_provider_margin == pytest.approx(expected_cost, rel=1e-9)

    print("✓ Cost margin provider override test passed:")
    print(f"  - Original cost: ${cost_without_margin:.6f}")
    print(f"  - Cost with provider margin (10%, overrides 5% global): ${cost_with_provider_margin:.6f}")
    print(f"  - Margin added: ${cost_with_provider_margin - cost_without_margin:.6f}")


def test_cost_margin_with_discount(monkeypatch):
    """
    Test that margin is applied after discount (independent calculation)
    """
    from litellm import completion_cost
    from litellm.types.utils import Usage

    # Create mock response
    response = ModelResponse(
        id="test-id",
        choices=[],
        created=1234567890,
        model="gpt-4",
        object="chat.completion",
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    # Calculate base cost
    monkeypatch.setattr(litellm, "cost_margin_config", {})
    monkeypatch.setattr(litellm, "cost_discount_config", {})
    base_cost = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Set 5% discount and 10% margin
    monkeypatch.setattr(litellm, "cost_discount_config", {"openai": 0.05})
    monkeypatch.setattr(litellm, "cost_margin_config", {"openai": 0.10})

    # Calculate cost with both discount and margin
    cost_with_both = completion_cost(
        completion_response=response,
        model="gpt-4",
        custom_llm_provider="openai",
    )

    # Verify: discount applied first, then margin
    # Base cost -> discount: base * 0.95 -> margin: (base * 0.95) * 1.10
    expected_cost = base_cost * 0.95 * 1.10
    assert cost_with_both == pytest.approx(expected_cost, rel=1e-9)

    print("✓ Cost margin with discount test passed:")
    print(f"  - Base cost: ${base_cost:.6f}")
    print(f"  - Cost with 5% discount + 10% margin: ${cost_with_both:.6f}")
    print(f"  - Expected: ${expected_cost:.6f}")


def test_completion_cost_extracts_service_tier_from_response(_local_model_cost_map):
    """Test that completion_cost extracts service_tier from completion_response object."""
    from litellm import completion_cost

    # Test with gpt-5-nano which has flex pricing
    model = "gpt-5-nano"

    # Create usage object
    usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)

    # Create ModelResponse with service_tier in the response object
    response_with_service_tier = ModelResponse(
        usage=usage,
        model=model,
    )
    # Set service_tier as an attribute on the response
    setattr(response_with_service_tier, "service_tier", "flex")

    # Test that flex pricing is used when service_tier is in response
    flex_cost = completion_cost(
        completion_response=response_with_service_tier,
        model=model,
        custom_llm_provider="openai",
    )

    # Create ModelResponse without service_tier (should use standard pricing)
    response_without_service_tier = ModelResponse(
        usage=usage,
        model=model,
    )

    # Test that standard pricing is used when service_tier is not in response
    standard_cost = completion_cost(
        completion_response=response_without_service_tier,
        model=model,
        custom_llm_provider="openai",
    )

    # Flex should be approximately 50% of standard
    assert flex_cost > 0, "Flex cost should be greater than 0"
    assert standard_cost > 0, "Standard cost should be greater than 0"
    assert flex_cost < standard_cost, "Flex cost should be less than standard cost"

    flex_ratio = flex_cost / standard_cost
    assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}"


def test_completion_cost_extracts_service_tier_from_usage(_local_model_cost_map):
    """Test that completion_cost extracts service_tier from usage object."""
    from litellm import completion_cost

    # Test with gpt-5-nano which has flex pricing
    model = "gpt-5-nano"

    # Create usage object with service_tier
    usage_with_service_tier = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
    # Set service_tier as an attribute on the usage object
    setattr(usage_with_service_tier, "service_tier", "flex")

    # Create ModelResponse with usage containing service_tier
    response = ModelResponse(
        usage=usage_with_service_tier,
        model=model,
    )

    # Test that flex pricing is used when service_tier is in usage
    flex_cost = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="openai",
    )

    # Create usage object without service_tier
    usage_without_service_tier = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)

    # Create ModelResponse with usage without service_tier
    response_standard = ModelResponse(
        usage=usage_without_service_tier,
        model=model,
    )

    # Test that standard pricing is used when service_tier is not in usage
    standard_cost = completion_cost(
        completion_response=response_standard,
        model=model,
        custom_llm_provider="openai",
    )

    # Flex should be approximately 50% of standard
    assert flex_cost > 0, "Flex cost should be greater than 0"
    assert standard_cost > 0, "Standard cost should be greater than 0"
    assert flex_cost < standard_cost, "Flex cost should be less than standard cost"

    flex_ratio = flex_cost / standard_cost
    assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}"


def test_completion_cost_service_tier_priority(_local_model_cost_map):
    """Test that the served tier wins over the requested tier: response > usage > request."""
    from litellm import completion_cost

    # Test with gpt-5-nano which has flex pricing
    model = "gpt-5-nano"

    # Create usage object with service_tier="flex"
    usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
    setattr(usage, "service_tier", "flex")

    # Create response with service_tier="priority"
    response = ModelResponse(
        usage=usage,
        model=model,
    )
    setattr(response, "service_tier", "priority")

    # A request-level tier loses to the tier the response actually served
    cost_from_params = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="openai",
        optional_params={"service_tier": "flex"},
    )

    # Response takes priority over usage
    cost_served_priority = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="openai",
    )

    # Create a new response without service_tier attribute so it falls back to usage
    response_no_tier = ModelResponse(
        usage=usage,
        model=model,
    )

    cost_from_usage = completion_cost(
        completion_response=response_no_tier,
        model=model,
        custom_llm_provider="openai",
    )

    assert cost_from_params > 0, "Cost from params should be greater than 0"
    assert cost_from_usage > 0, "Cost from usage should be greater than 0"

    # Requested flex is ignored once the response reports served priority
    assert cost_from_params == pytest.approx(cost_served_priority), (
        "request-level service_tier must defer to the served tier on the response"
    )


def test_completion_cost_service_tier_for_bedrock(_local_model_cost_map):
    """Test that Bedrock cost calculation applies service_tier-specific pricing."""
    from litellm import completion_cost

    model = "bedrock/us-east-1/test-bedrock-service-tier-cost-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 0.001,
                "output_cost_per_token": 0.002,
                "input_cost_per_token_priority": 0.01,
                "output_cost_per_token_priority": 0.02,
                "input_cost_per_token_flex": 0.0005,
                "output_cost_per_token_flex": 0.001,
                "litellm_provider": "bedrock",
                "max_tokens": 8192,
            }
        }
    )

    usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150)
    response = ModelResponse(usage=usage, model=model)

    default_cost = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="bedrock",
    )

    priority_cost = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="bedrock",
        optional_params={"service_tier": "priority"},
    )

    response_with_flex_tier = ModelResponse(usage=usage, model=model)
    setattr(response_with_flex_tier, "service_tier", "flex")
    flex_cost = completion_cost(
        completion_response=response_with_flex_tier,
        model=model,
        custom_llm_provider="bedrock",
    )

    assert priority_cost > default_cost > flex_cost > 0


def test_completion_cost_service_tier_for_anthropic(_local_model_cost_map):
    """
    Anthropic priority-tier requests must be priced at the priority rate.

    Regression for LIT-3771: the Anthropic cost route dropped ``service_tier``,
    so priority requests (whose tier is reported on the response usage) were
    always billed at the standard rate. The tier is captured by the
    transformation and must flow through to ``generic_cost_per_token``.
    """
    from litellm import completion_cost
    from litellm.llms.anthropic.chat.transformation import AnthropicConfig

    model = "claude-test-service-tier-cost-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 3e-6,
                "output_cost_per_token": 15e-6,
                "input_cost_per_token_priority": 6e-6,
                "output_cost_per_token_priority": 30e-6,
                "litellm_provider": "anthropic",
                "max_tokens": 8192,
            }
        }
    )

    def _cost_for_tier(service_tier):
        usage = AnthropicConfig().calculate_usage(
            usage_object={
                "input_tokens": 1000,
                "output_tokens": 500,
                "service_tier": service_tier,
            },
            reasoning_content=None,
        )
        response = ModelResponse(usage=usage, model=model)
        return completion_cost(
            completion_response=response,
            model=model,
            custom_llm_provider="anthropic",
        )

    standard_cost = _cost_for_tier("standard")
    priority_cost = _cost_for_tier("priority")

    expected_standard = 1000 * 3e-6 + 500 * 15e-6
    assert standard_cost == pytest.approx(expected_standard)
    # priority rates are exactly 2x standard for both input and output
    assert priority_cost == pytest.approx(2 * standard_cost)


def test_completion_cost_anthropic_auto_tier_uses_served_priority_rate(_local_model_cost_map):
    """
    Proxy billing path regression for LIT-3771.

    Priority is opted into with ``service_tier="auto"``; Anthropic then serves
    "priority" and reports it on the response usage. The proxy forwards the
    request-level "auto" into ``completion_cost`` (via ``response_cost_calculator``),
    and that preference must not shadow the served tier, otherwise priority
    requests are silently billed at the standard rate.
    """
    from litellm import completion_cost
    from litellm.llms.anthropic.chat.transformation import AnthropicConfig

    model = "claude-test-auto-tier-cost-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 3e-6,
                "output_cost_per_token": 15e-6,
                "input_cost_per_token_priority": 6e-6,
                "output_cost_per_token_priority": 30e-6,
                "litellm_provider": "anthropic",
                "max_tokens": 8192,
            }
        }
    )

    usage = AnthropicConfig().calculate_usage(
        usage_object={
            "input_tokens": 1000,
            "output_tokens": 500,
            "service_tier": "priority",
        },
        reasoning_content=None,
    )
    response = ModelResponse(usage=usage, model=model)

    cost = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="anthropic",
        service_tier="auto",
        optional_params={"service_tier": "auto"},
    )

    expected_priority = 1000 * 6e-6 + 500 * 30e-6
    assert cost == pytest.approx(expected_priority)


def test_completion_cost_vertex_ai_gemini_flex_traffic_type(_local_model_cost_map):
    """
    Vertex AI flex-tier billing regression for issue #37647.

    Vertex Gemini 3.x models route through ``cost_per_character`` (the
    ``cost_router`` token-path gate only matches "gemini-2"), and its token
    fallbacks dropped ``service_tier``. A response served with
    ``trafficType=ON_DEMAND_FLEX`` must be billed at the flex rate, not the
    standard rate.
    """
    from litellm import completion_cost

    model = "gemini-3-test-flex-tier-cost-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 1.5e-6,
                "output_cost_per_token": 9e-6,
                "input_cost_per_token_flex": 7.5e-7,
                "output_cost_per_token_flex": 4.5e-6,
                "litellm_provider": "vertex_ai",
                "max_tokens": 8192,
            }
        }
    )

    def _cost_for_traffic_type(traffic_type):
        usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
        response = ModelResponse(usage=usage, model=model)
        response._hidden_params["provider_specific_fields"] = {"traffic_type": traffic_type}
        return completion_cost(
            completion_response=response,
            model=model,
            custom_llm_provider="vertex_ai",
        )

    standard_cost = _cost_for_traffic_type("ON_DEMAND")
    flex_cost = _cost_for_traffic_type("ON_DEMAND_FLEX")

    assert standard_cost == pytest.approx(1000 * 1.5e-6 + 500 * 9e-6)
    assert flex_cost == pytest.approx(1000 * 7.5e-7 + 500 * 4.5e-6)


def test_completion_cost_non_string_service_tier_defers_to_served_tier(_local_model_cost_map):
    """
    Regression: a non-string request-level ``service_tier`` (reachable via
    ``allowed_openai_params``/``drop_params``) must not crash cost tracking.

    Before the fix, ``completion_cost`` called ``service_tier.lower()`` on the
    request-level value, so a dict raised ``AttributeError``. ``response_cost_calculator``
    swallowed it and reported ``response_cost=None``, silently dropping the cost.
    The non-string preference must be ignored so pricing defers to the tier the
    provider actually served on the response usage.
    """
    from litellm import completion_cost
    from litellm.llms.anthropic.chat.transformation import AnthropicConfig

    model = "claude-test-non-string-tier-cost-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 3e-6,
                "output_cost_per_token": 15e-6,
                "input_cost_per_token_priority": 6e-6,
                "output_cost_per_token_priority": 30e-6,
                "litellm_provider": "anthropic",
                "max_tokens": 8192,
            }
        }
    )

    usage = AnthropicConfig().calculate_usage(
        usage_object={
            "input_tokens": 1000,
            "output_tokens": 500,
            "service_tier": "priority",
        },
        reasoning_content=None,
    )
    response = ModelResponse(usage=usage, model=model)

    cost = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="anthropic",
        optional_params={"service_tier": {"name": "auto"}},
    )

    expected_priority = 1000 * 6e-6 + 500 * 30e-6
    assert cost == pytest.approx(expected_priority)


def test_completion_cost_non_string_response_service_tier_defers_to_served_tier(_local_model_cost_map):
    """
    Regression: a non-string ``service_tier`` on the response object must not
    crash cost tracking.

    Before the fix ``completion_cost`` read the response-level value verbatim and
    passed it to ``_get_service_tier_cost_key``, which called ``service_tier.lower()``
    on the dict and raised ``AttributeError``. The non-string preference is not a
    billable tier, so pricing defers to the concrete tier the provider served on
    the usage object instead of crashing.
    """
    from litellm import completion_cost
    from litellm.llms.anthropic.chat.transformation import AnthropicConfig

    model = "claude-test-response-non-string-tier-cost-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 3e-6,
                "output_cost_per_token": 15e-6,
                "input_cost_per_token_priority": 6e-6,
                "output_cost_per_token_priority": 30e-6,
                "litellm_provider": "anthropic",
                "max_tokens": 8192,
            }
        }
    )

    usage = AnthropicConfig().calculate_usage(
        usage_object={
            "input_tokens": 1000,
            "output_tokens": 500,
            "service_tier": "priority",
        },
        reasoning_content=None,
    )
    response = ModelResponse(usage=usage, model=model, service_tier={"name": "priority"})

    cost = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="anthropic",
    )

    expected_priority = 1000 * 6e-6 + 500 * 30e-6
    assert cost == pytest.approx(expected_priority)


def test_completion_cost_non_string_usage_service_tier_prices_standard(_local_model_cost_map):
    """
    Regression: a non-string ``service_tier`` on the usage object must not crash
    cost tracking.

    The dict reaches ``completion_cost`` via the usage extraction path with no
    concrete tier to defer to, so pricing falls back to the standard rate instead
    of raising ``AttributeError`` in ``_get_service_tier_cost_key``.
    """
    from litellm import completion_cost

    model = "claude-test-usage-non-string-tier-cost-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 3e-6,
                "output_cost_per_token": 15e-6,
                "input_cost_per_token_priority": 6e-6,
                "output_cost_per_token_priority": 30e-6,
                "litellm_provider": "anthropic",
                "max_tokens": 8192,
            }
        }
    )

    usage = Usage(
        prompt_tokens=1000,
        completion_tokens=500,
        total_tokens=1500,
        service_tier={"name": "priority"},
    )
    response = ModelResponse(usage=usage, model=model)

    cost = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="anthropic",
    )

    expected_standard = 1000 * 3e-6 + 500 * 15e-6
    assert cost == pytest.approx(expected_standard)


def test_anthropic_cost_per_token_prices_cache_at_served_tier_with_multiplier(_local_model_cost_map):
    """
    Regression for the cache/tier interaction in the Anthropic geo/speed path.

    When a request is served at "priority" and also carries the ``fast`` speed
    multiplier, the cache portion must be priced at the served tier's cache
    rate and, per Anthropic's fast-mode pricing, scaled by the multiplier like
    every other token type.
    """
    from litellm.llms.anthropic.cost_calculation import (
        cost_per_token as anthropic_cost_per_token,
    )
    from litellm.types.utils import PromptTokensDetailsWrapper, Usage

    model = "claude-test-priority-cache-fast-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 3e-6,
                "output_cost_per_token": 15e-6,
                "cache_read_input_token_cost": 0.3e-6,
                "input_cost_per_token_priority": 6e-6,
                "output_cost_per_token_priority": 30e-6,
                "cache_read_input_token_cost_priority": 0.6e-6,
                "litellm_provider": "anthropic",
                "max_tokens": 8192,
                "provider_specific_entry": {"fast": 2.0},
            }
        }
    )

    usage = Usage(
        prompt_tokens=1000,
        completion_tokens=500,
        total_tokens=1500,
        prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=200),
    )
    usage.speed = "fast"

    prompt_cost, completion_cost = anthropic_cost_per_token(model=model, usage=usage, service_tier="priority")

    expected_prompt = ((1000 - 200) * 6e-6 + 200 * 0.6e-6) * 2
    expected_completion = 500 * 30e-6 * 2
    assert prompt_cost == pytest.approx(expected_prompt)
    assert completion_cost == pytest.approx(expected_completion)


def _register_anthropic_geo_cache_model(model: str) -> None:
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 5e-6,
                "output_cost_per_token": 25e-6,
                "cache_creation_input_token_cost": 6.25e-6,
                "cache_read_input_token_cost": 0.5e-6,
                "litellm_provider": "anthropic",
                "max_tokens": 8192,
                "provider_specific_entry": {"us": 1.1, "fast": 2.0},
            }
        }
    )


def test_anthropic_geo_multiplier_applies_to_cache_tokens(_local_model_cost_map, monkeypatch):
    """
    Regression: the regional (geo) uplift must scale cache read and cache write
    cost too, not just non-cache input and output.

    Anthropic's regional surcharge applies to every token type, so a cache-heavy
    row (nearly all cache-creation tokens) must still come in 10% above the
    global-priced row. Before the fix the uplift was applied only to the
    non-cache portion, so cache-heavy spend was under-reported by ~10%.
    """
    from litellm.llms.anthropic.cost_calculation import (
        cost_per_token as anthropic_cost_per_token,
    )
    from litellm.types.utils import PromptTokensDetailsWrapper, Usage

    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")

    model = "claude-test-geo-cache-model"
    _register_anthropic_geo_cache_model(model)

    def make_usage() -> "Usage":
        return Usage(
            prompt_tokens=1_000_000,
            completion_tokens=500,
            total_tokens=1_000_500,
            prompt_tokens_details=PromptTokensDetailsWrapper(
                cached_tokens=200_000,
                cache_creation_tokens=799_800,
            ),
        )

    base_usage = make_usage()
    base_prompt_cost, base_completion_cost = anthropic_cost_per_token(model=model, usage=base_usage)

    geo_usage = make_usage()
    geo_usage.inference_geo = "us"
    geo_prompt_cost, geo_completion_cost = anthropic_cost_per_token(model=model, usage=geo_usage)

    expected_base_prompt = 200 * 5e-6 + 200_000 * 0.5e-6 + 799_800 * 6.25e-6
    assert base_prompt_cost == pytest.approx(expected_base_prompt)
    assert geo_prompt_cost == pytest.approx(expected_base_prompt * 1.1)
    assert geo_completion_cost == pytest.approx(base_completion_cost * 1.1)


def test_anthropic_geo_and_fast_multipliers_compose(_local_model_cost_map, monkeypatch):
    """
    Anthropic's fast-mode pricing doubles every token type, cache reads and
    writes included, and the regional uplift stacks on top, so a fast +
    regional row prices as ``(non_cache + cache) * fast * geo``.
    """
    from litellm.llms.anthropic.cost_calculation import (
        cost_per_token as anthropic_cost_per_token,
    )
    from litellm.types.utils import PromptTokensDetailsWrapper, Usage

    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")

    model = "claude-test-geo-fast-cache-model"
    _register_anthropic_geo_cache_model(model)

    usage = Usage(
        prompt_tokens=10_000,
        completion_tokens=500,
        total_tokens=10_500,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            cached_tokens=2_000,
            cache_creation_tokens=6_000,
        ),
    )
    usage.inference_geo = "us"
    usage.speed = "fast"

    prompt_cost, completion_cost = anthropic_cost_per_token(model=model, usage=usage)

    cache_cost = 2_000 * 0.5e-6 + 6_000 * 6.25e-6
    non_cache_cost = 2_000 * 5e-6
    assert prompt_cost == pytest.approx((non_cache_cost + cache_cost) * 2.0 * 1.1)
    assert completion_cost == pytest.approx(500 * 25e-6 * 2.0 * 1.1)


@pytest.mark.parametrize(
    "model",
    ["claude-sonnet-4-6", "claude-mythos-5", "claude-mythos-preview"],
)
def test_anthropic_us_data_residency_uplift_on_claude_4_6_and_later_models(_local_model_cost_map, monkeypatch, model):
    """
    Anthropic bills every Claude 4.6+ model served with ``inference_geo="us"`` at
    1.1x, and echoes that geo back in the response usage, so each of these real
    cost-map entries has to carry the ``us`` multiplier or US-pinned traffic is
    under-reported by 10%.
    """
    from litellm.llms.anthropic.cost_calculation import (
        cost_per_token as anthropic_cost_per_token,
    )
    from litellm.types.utils import Usage

    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")

    def make_usage() -> "Usage":
        return Usage(prompt_tokens=1_000, completion_tokens=100, total_tokens=1_100)

    base_prompt_cost, base_completion_cost = anthropic_cost_per_token(model=model, usage=make_usage())

    geo_usage = make_usage()
    geo_usage.inference_geo = "us"
    geo_prompt_cost, geo_completion_cost = anthropic_cost_per_token(model=model, usage=geo_usage)

    assert base_prompt_cost > 0
    assert geo_prompt_cost == pytest.approx(base_prompt_cost * 1.1)
    assert geo_completion_cost == pytest.approx(base_completion_cost * 1.1)


def test_gemini_cache_tokens_details_no_negative_values():
    """
    Test for Issue #18750: Negative text_tokens with Gemini caching

    When using Gemini with explicit caching, the response includes cacheTokensDetails
    which breaks down cached tokens by modality. This test ensures that:
    1. text_tokens is never negative
    2. We correctly subtract cached tokens per modality (not total)
    """
    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
        VertexGeminiConfig,
    )

    # Scenario from issue #18750: Image + text with explicit caching
    # Real Gemini response structure when using cached content
    completion_response = {
        "usageMetadata": {
            "promptTokenCount": 9660,
            "candidatesTokenCount": 7,
            "totalTokenCount": 9667,
            "cachedContentTokenCount": 9651,
            # Total tokens by modality (includes cached + non-cached)
            "promptTokensDetails": [
                {"modality": "TEXT", "tokenCount": 9402},
                {"modality": "IMAGE", "tokenCount": 258},
            ],
            # Breakdown of cached tokens by modality
            "cacheTokensDetails": [
                {"modality": "TEXT", "tokenCount": 9393},
                {"modality": "IMAGE", "tokenCount": 258},
            ],
        }
    }

    usage = VertexGeminiConfig.calculate_usage(completion_response)

    # Text tokens should be non-cached text only: 9402 - 9393 = 9
    assert usage.prompt_tokens_details.text_tokens == 9, (
        f"Expected text_tokens=9, got {usage.prompt_tokens_details.text_tokens}"
    )

    # Image tokens should be non-cached image only: 258 - 258 = 0
    assert usage.prompt_tokens_details.image_tokens == 0, (
        f"Expected image_tokens=0, got {usage.prompt_tokens_details.image_tokens}"
    )

    # Total cached should match
    assert usage.prompt_tokens_details.cached_tokens == 9651, (
        f"Expected cached_tokens=9651, got {usage.prompt_tokens_details.cached_tokens}"
    )

    # MOST IMPORTANT: text_tokens should NEVER be negative
    assert usage.prompt_tokens_details.text_tokens >= 0, (
        f"BUG: text_tokens is negative ({usage.prompt_tokens_details.text_tokens})! This was the issue in #18750"
    )

    print("✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative")


def test_gemini_without_cache_tokens_details():
    """
    Test Gemini response without cacheTokensDetails (implicit caching or no cache)

    When cacheTokensDetails is not present, we should use promptTokensDetails as-is
    without subtracting anything.
    """
    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
        VertexGeminiConfig,
    )

    completion_response = {
        "usageMetadata": {
            "promptTokenCount": 264,
            "candidatesTokenCount": 15,
            "totalTokenCount": 279,
            "promptTokensDetails": [
                {"modality": "TEXT", "tokenCount": 6},
                {"modality": "IMAGE", "tokenCount": 258},
            ],
            # No cacheTokensDetails
        }
    }

    usage = VertexGeminiConfig.calculate_usage(completion_response)

    # Should use promptTokensDetails values directly
    assert usage.prompt_tokens_details.text_tokens == 6
    assert usage.prompt_tokens_details.image_tokens == 258
    assert usage.prompt_tokens_details.text_tokens >= 0

    print("✅ Gemini without cacheTokensDetails works correctly")


def test_additional_costs_only_for_azure_ai(_local_model_cost_map):
    """
    Test that _get_additional_costs is only called for azure_ai provider.

    completion_cost() guards the call with `if custom_llm_provider == "azure_ai"`.
    This test verifies that non-azure_ai providers get additional_costs=None
    (reflected by the absence of "additional_costs" in cost_breakdown),
    while azure_ai providers can include additional costs.
    """
    from litellm.cost_calculator import _get_additional_costs

    # Non-azure_ai providers should return None
    result = _get_additional_costs(
        model="gpt-4o",
        custom_llm_provider="openai",
        prompt_tokens=100,
        completion_tokens=50,
    )
    assert result is None, "Non-azure_ai providers should have no additional costs"

    result = _get_additional_costs(
        model="claude-sonnet-4-20250514",
        custom_llm_provider="anthropic",
        prompt_tokens=100,
        completion_tokens=50,
    )
    assert result is None, "Anthropic should have no additional costs"

    result = _get_additional_costs(
        model="gemini-2.0-flash",
        custom_llm_provider="vertex_ai",
        prompt_tokens=100,
        completion_tokens=50,
    )
    assert result is None, "Vertex AI should have no additional costs"


def test_custom_pricing_applies_cache_read_input_cost():
    """
    Bug 1 reproduction: custom_cost_per_token with cache_read_input_token_cost
    should bill cached prompt tokens at the cache rate, not the full input rate.
    """
    usage = Usage(
        prompt_tokens=6074,
        completion_tokens=285,
        total_tokens=6359,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            cached_tokens=3456,
            audio_tokens=0,
        ),
    )

    response = ModelResponse(
        id="test-id",
        created=1234567890,
        model="openai/gpt-5.4",
        object="chat.completion",
        choices=[],
        usage=usage,
    )

    cost = litellm.completion_cost(
        completion_response=response,
        model="openai/gpt-5.4",
        custom_llm_provider="openai",
        custom_cost_per_token={
            "input_cost_per_token": 0.0000025,
            "output_cost_per_token": 0.000015,
            "cache_read_input_token_cost": 0.00000025,
        },
    )

    expected = (6074 - 3456) * 0.0000025 + 3456 * 0.00000025 + 285 * 0.000015

    assert cost == pytest.approx(expected)


def test_custom_pricing_applies_cache_creation_input_cost_via_prompt_details():
    """
    OpenAI-compatible providers report cache-write tokens under
    prompt_tokens_details.cache_creation_tokens. The custom-pricing helper must
    bill those at cache_creation_input_token_cost, not the full input rate.
    """
    pt_details = PromptTokensDetailsWrapper(cached_tokens=1000, audio_tokens=0)
    pt_details.cache_creation_tokens = 500

    usage = Usage(
        prompt_tokens=4000,
        completion_tokens=100,
        total_tokens=4100,
        prompt_tokens_details=pt_details,
    )

    response = ModelResponse(
        id="test-id",
        created=1234567890,
        model="openai/gpt-5.4",
        object="chat.completion",
        choices=[],
        usage=usage,
    )

    cost = litellm.completion_cost(
        completion_response=response,
        model="openai/gpt-5.4",
        custom_llm_provider="openai",
        custom_cost_per_token={
            "input_cost_per_token": 0.0000025,
            "output_cost_per_token": 0.000015,
            "cache_read_input_token_cost": 0.00000025,
            "cache_creation_input_token_cost": 0.000003125,
        },
    )

    expected = (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125 + 100 * 0.000015

    assert cost == pytest.approx(expected)


def test_custom_pricing_applies_cache_creation_input_cost_via_cache_write_tokens_alias():
    """
    Some OpenAI-compatible providers (e.g. kimi-k2) emit cache-write tokens as
    `cache_write_tokens` rather than `cache_creation_tokens`. The cost
    calculator must mirror db_spend_update_writer and accept either name —
    otherwise daily aggregation counts the tokens but the per-request cost
    bills them at the full input rate.

    Drives `cost_per_token` directly with a SimpleNamespace usage stub so the
    `cache_write_tokens` alias survives the call (Pydantic's Usage init
    rebuilds prompt_tokens_details and drops dynamic attributes).
    """
    from types import SimpleNamespace

    from litellm.cost_calculator import cost_per_token

    pt_details = SimpleNamespace(cached_tokens=1000, cache_write_tokens=500)
    usage_stub = SimpleNamespace(
        prompt_tokens=4000,
        completion_tokens=100,
        total_tokens=4100,
        prompt_tokens_details=pt_details,
        cache_read_input_tokens=None,
        cache_creation_input_tokens=None,
    )

    prompt_cost, completion_cost = cost_per_token(
        model="moonshotai/kimi-k2",
        prompt_tokens=4000,
        completion_tokens=100,
        custom_llm_provider="openai",
        usage_object=usage_stub,
        custom_cost_per_token={
            "input_cost_per_token": 0.0000025,
            "output_cost_per_token": 0.000015,
            "cache_read_input_token_cost": 0.00000025,
            "cache_creation_input_token_cost": 0.000003125,
        },
    )

    expected_prompt = (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125
    expected_completion = 100 * 0.000015

    assert prompt_cost == pytest.approx(expected_prompt)
    assert completion_cost == pytest.approx(expected_completion)


# ---------------------------------------------------------------------------
# Bug 2 — db_spend_update_writer cache token extraction helpers.
# ---------------------------------------------------------------------------


def test_extract_cache_read_tokens_anthropic_top_level():
    from litellm.proxy.spend_tracking.savings import extract_cache_read_tokens as _extract_cache_read_tokens

    usage_obj = {
        "prompt_tokens": 100,
        "cache_read_input_tokens": 80,
        "prompt_tokens_details": {"cached_tokens": 80},
    }
    # Anthropic top-level value should win over prompt_tokens_details fallback.
    assert _extract_cache_read_tokens(usage_obj) == 80


def test_extract_cache_read_tokens_openai_compatible_fallback():
    from litellm.proxy.spend_tracking.savings import extract_cache_read_tokens as _extract_cache_read_tokens

    # Anthropic field absent — fall back to prompt_tokens_details.cached_tokens.
    usage_obj = {
        "prompt_tokens": 22583,
        "prompt_tokens_details": {"cached_tokens": 22016},
    }
    assert _extract_cache_read_tokens(usage_obj) == 22016


def test_extract_cache_read_tokens_zero_when_missing():
    from litellm.proxy.spend_tracking.savings import extract_cache_read_tokens as _extract_cache_read_tokens

    assert _extract_cache_read_tokens({}) == 0
    assert _extract_cache_read_tokens({"cache_read_input_tokens": None}) == 0
    assert _extract_cache_read_tokens({"prompt_tokens_details": {"cached_tokens": None}}) == 0


def test_extract_cache_creation_tokens_anthropic_top_level():
    from litellm.proxy.spend_tracking.savings import extract_cache_creation_tokens as _extract_cache_creation_tokens

    usage_obj = {
        "prompt_tokens": 100,
        "cache_creation_input_tokens": 50,
        "prompt_tokens_details": {"cache_write_tokens": 50},
    }
    # Anthropic top-level should short-circuit the fallback.
    assert _extract_cache_creation_tokens(usage_obj) == 50


def test_extract_cache_creation_tokens_openai_cache_write_alias():
    from litellm.proxy.spend_tracking.savings import extract_cache_creation_tokens as _extract_cache_creation_tokens

    # kimi-k2 emits cache_write_tokens.
    usage_obj = {
        "prompt_tokens": 1000,
        "prompt_tokens_details": {"cache_write_tokens": 200},
    }
    assert _extract_cache_creation_tokens(usage_obj) == 200


def test_extract_cache_creation_tokens_openai_cache_creation_alias():
    from litellm.proxy.spend_tracking.savings import extract_cache_creation_tokens as _extract_cache_creation_tokens

    # Other OpenAI-compatible providers emit cache_creation_tokens.
    usage_obj = {
        "prompt_tokens": 1000,
        "prompt_tokens_details": {"cache_creation_tokens": 300},
    }
    assert _extract_cache_creation_tokens(usage_obj) == 300


def test_extract_cache_creation_tokens_zero_when_missing():
    from litellm.proxy.spend_tracking.savings import extract_cache_creation_tokens as _extract_cache_creation_tokens

    assert _extract_cache_creation_tokens({}) == 0
    assert _extract_cache_creation_tokens({"cache_creation_input_tokens": None}) == 0
    assert _extract_cache_creation_tokens({"prompt_tokens_details": {"cache_write_tokens": None}}) == 0


def test_custom_pricing_anthropic_style_cache_tokens_not_double_counted():
    """
    Anthropic providers report cache tokens at the top level of Usage, and
    `prompt_tokens` EXCLUDES them. The helper expects `prompt_tokens` to
    include cache tokens, so cost_per_token must adjust before invoking it —
    otherwise regular_prompt_tokens goes negative and clamps to 0.
    """
    usage = Usage(
        prompt_tokens=2000,
        completion_tokens=100,
        total_tokens=2100,
        cache_read_input_tokens=1500,
        cache_creation_input_tokens=300,
    )

    response = ModelResponse(
        id="test-id",
        created=1234567890,
        model="anthropic/claude-3-5-sonnet",
        object="chat.completion",
        choices=[],
        usage=usage,
    )

    cost = litellm.completion_cost(
        completion_response=response,
        model="anthropic/claude-3-5-sonnet",
        custom_llm_provider="anthropic",
        custom_cost_per_token={
            "input_cost_per_token": 0.000003,
            "output_cost_per_token": 0.000015,
            "cache_read_input_token_cost": 0.0000003,
            "cache_creation_input_token_cost": 0.00000375,
        },
    )

    # Anthropic prompt_tokens=2000 excludes cache. After normalization the
    # helper sees 2000 + 1500 + 300 = 3800, of which 2000 are uncached.
    expected = 2000 * 0.000003 + 1500 * 0.0000003 + 300 * 0.00000375 + 100 * 0.000015

    assert cost == pytest.approx(expected)


def test_custom_pricing_without_cache_keys_preserves_legacy_behavior():
    """
    Backward compatibility: when custom_cost_per_token omits both cache rates,
    cached tokens must be billed at input_cost_per_token (matching the pre-fix
    behavior) so existing callers see no change.
    """
    usage = Usage(
        prompt_tokens=1000,
        completion_tokens=100,
        total_tokens=1100,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            cached_tokens=400,
            audio_tokens=0,
        ),
    )

    response = ModelResponse(
        id="test-id",
        created=1234567890,
        model="openai/gpt-5.4",
        object="chat.completion",
        choices=[],
        usage=usage,
    )

    cost = litellm.completion_cost(
        completion_response=response,
        model="openai/gpt-5.4",
        custom_llm_provider="openai",
        custom_cost_per_token={
            "input_cost_per_token": 0.0000025,
            "output_cost_per_token": 0.000015,
        },
    )

    # All 1000 prompt tokens billed at input rate, regardless of cached_tokens.
    expected = 1000 * 0.0000025 + 100 * 0.000015

    assert cost == pytest.approx(expected)


def test_completion_cost_logs_the_rates_it_billed_at(monkeypatch):
    """A caller reporting the cost lines beside their per-token rates reads both off this one call.
    completion_cost infers the provider, and xai's inclusive tier thresholds put a request sitting
    exactly on 200k at the tier rate, which a lookup made without that inferred provider would miss.
    """
    from datetime import datetime

    from litellm.litellm_core_utils.litellm_logging import Logging

    monkeypatch.setitem(
        litellm.model_cost,
        "xai/tiered-model",
        {
            "input_cost_per_token": 3e-6,
            "output_cost_per_token": 15e-6,
            "cache_read_input_token_cost": 3e-7,
            "input_cost_per_token_above_200k_tokens": 6e-6,
            "output_cost_per_token_above_200k_tokens": 3e-5,
            "cache_read_input_token_cost_above_200k_tokens": 6e-7,
            "litellm_provider": "xai",
            "mode": "chat",
        },
    )
    logging_obj = Logging(
        model="xai/tiered-model",
        messages=[{"role": "user", "content": "Hello"}],
        stream=False,
        call_type="completion",
        start_time=datetime.now(),
        litellm_call_id="billed-rates",
        function_id="f",
    )
    usage = Usage(
        prompt_tokens=200_000,
        completion_tokens=1_000,
        total_tokens=201_000,
        prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=100_000),
    )

    litellm.completion_cost(
        completion_response=ModelResponse(model="xai/tiered-model", usage=usage),
        model="xai/tiered-model",
        custom_llm_provider=None,
        litellm_logging_obj=logging_obj,
    )

    rates = logging_obj.billed_token_rates
    assert rates is not None
    assert rates.input_cost_per_token == pytest.approx(6e-6)
    assert rates.cache_read_input_token_cost == pytest.approx(6e-7)
    assert logging_obj.cost_breakdown["cache_read_cost"] == pytest.approx(100_000 * rates.cache_read_input_token_cost)
    assert logging_obj.cost_breakdown["output_cost"] == pytest.approx(1_000 * rates.output_cost_per_token)


def test_completion_cost_logs_cache_and_reasoning_breakdown_for_custom_pricing():
    """
    A custom-priced deployment bills cache tokens at its custom cache rates, but the
    breakdown stored for the spend logs carried no cache or reasoning lines for it.
    """
    from datetime import datetime

    from litellm.litellm_core_utils.litellm_logging import Logging
    from litellm.types.utils import CompletionTokensDetailsWrapper, CostPerToken

    logging_obj = Logging(
        model="openai/onprem-model",
        messages=[{"role": "user", "content": "Hello"}],
        stream=False,
        call_type="completion",
        start_time=datetime.now(),
        litellm_call_id="custom-pricing-breakdown",
        function_id="f",
    )
    response = ModelResponse(
        model="openai/onprem-model",
        usage=Usage(
            prompt_tokens=1000,
            completion_tokens=500,
            total_tokens=1500,
            prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=800, cache_creation_tokens=100),
            completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=200),
        ),
    )

    total = completion_cost(
        completion_response=response,
        model="openai/onprem-model",
        custom_llm_provider="openai",
        custom_cost_per_token=CostPerToken(
            input_cost_per_token=1e-6,
            output_cost_per_token=2e-6,
            cache_read_input_token_cost=1e-7,
            cache_creation_input_token_cost=1.25e-6,
        ),
        litellm_logging_obj=logging_obj,
    )

    assert logging_obj.cost_breakdown is not None
    assert logging_obj.cost_breakdown["cache_read_cost"] == pytest.approx(800 * 1e-7)
    assert logging_obj.cost_breakdown["cache_creation_cost"] == pytest.approx(100 * 1.25e-6)
    assert logging_obj.cost_breakdown["reasoning_cost"] == pytest.approx(200 * 2e-6)
    assert total == pytest.approx(100 * 1e-6 + 800 * 1e-7 + 100 * 1.25e-6 + 500 * 2e-6)


@pytest.mark.parametrize("custom_llm_provider", ["together_ai", "openai", "anthropic", "bedrock", "azure"])
def test_cost_per_token_per_second_pricing(monkeypatch, custom_llm_provider: str):
    """
    Models priced by input/output duration rates with no per-token rates
    must be billed as cost_per_second * response_time_ms / 1000 in cost_per_token,
    using only the input rate even when both are set, whether or not the provider has its own calculator.
    """
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    model = f"test-per-second-pricing-{custom_llm_provider}"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_second": 0.02,
                "output_cost_per_second": 0.04,
                "litellm_provider": custom_llm_provider,
                "mode": "chat",
            }
        }
    )

    prompt_cost, completion_cost_value = cost_per_token(
        model=model,
        custom_llm_provider=custom_llm_provider,
        prompt_tokens=10,
        completion_tokens=20,
        response_time_ms=1500.0,
    )

    assert (prompt_cost, completion_cost_value) == pytest.approx((0.02 * 1.5, 0.0))


def test_azure_chat_uses_token_rates_when_output_cost_per_second_is_set(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    model: Final = "test-azure-chat-token-and-output-second-pricing"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 1e-6,
                "output_cost_per_token": 2e-6,
                "output_cost_per_second": 0.4,
                "litellm_provider": "azure",
                "mode": "chat",
            }
        }
    )

    cost: Final = cost_per_token(
        model=model,
        custom_llm_provider="azure",
        prompt_tokens=10,
        completion_tokens=20,
        response_time_ms=1500.0,
    )

    assert cost == pytest.approx((10 * 1e-6, 20 * 2e-6))


def test_cost_per_token_ignores_cost_per_second_when_token_pricing_is_set(monkeypatch):
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    model = "test-token-and-per-second-pricing-model"
    litellm.register_model(
        model_cost={
            model: {
                "input_cost_per_token": 1e-6,
                "output_cost_per_token": 2e-6,
                "cost_per_second": 0.02,
                "litellm_provider": "openai",
                "mode": "chat",
            }
        }
    )

    prompt_cost, completion_cost_value = cost_per_token(
        model=model,
        custom_llm_provider="openai",
        prompt_tokens=10,
        completion_tokens=20,
        response_time_ms=1500.0,
    )

    assert prompt_cost == pytest.approx(10 * 1e-6)
    assert completion_cost_value == pytest.approx(20 * 2e-6)


@pytest.mark.parametrize(
    ("pricing_fields", "expected_rate"),
    [
        ({"cost_per_second": 0.02}, 0.02),
        ({"output_cost_per_second": 0.04}, 0.04),
        (
            {"cost_per_second": 0.05, "input_cost_per_second": 0.02, "output_cost_per_second": 0.04},
            0.05,
        ),
        ({"input_cost_per_second": 0.02}, 0.02),
    ],
)
def test_cost_per_token_resolves_per_second_rate_precedence(
    monkeypatch, pricing_fields: dict[str, float], expected_rate: float
):
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    model: Final = "test-chat-per-second-rate-precedence"
    entry: Final = {**pricing_fields, "litellm_provider": "together_ai", "mode": "chat"}
    litellm.register_model(model_cost={model: entry})

    assert cost_per_token(
        model=model,
        custom_llm_provider="together_ai",
        prompt_tokens=10,
        completion_tokens=20,
        response_time_ms=1500.0,
    ) == pytest.approx((expected_rate * 1.5, 0.0))


def _logging_obj_with_call_window(duration_ms: float) -> Logging:
    start_time: Final = datetime.datetime(2026, 9, 21, 12, 0, 0)
    logging_obj: Final = Logging(
        model="gpt-5.4-nano",
        messages=[{"role": "user", "content": "Hello"}],
        stream=False,
        call_type="completion",
        start_time=start_time,
        litellm_call_id="per-second-call-window",
        function_id="f",
    )
    logging_obj.model_call_details["start_time"] = start_time
    logging_obj.model_call_details["end_time"] = start_time + datetime.timedelta(milliseconds=duration_ms)
    return logging_obj


@pytest.mark.parametrize(
    ("stamped_response_ms", "total_time", "logged_duration_ms", "expected_seconds"),
    [(None, 0.0, 1500.0, 1.5), (3000.0, 0.0, 1500.0, 3.0), (None, 2500.0, 1500.0, 2.5), (3000.0, 2500.0, 1500.0, 3.0)],
)
def test_completion_cost_per_second_deployment_bills_the_call_duration(
    monkeypatch,
    stamped_response_ms: float | None,
    total_time: float,
    logged_duration_ms: float,
    expected_seconds: float,
):
    """
    A deployment priced only per second bills the stamped ``_response_ms`` when there is one,
    then the caller's explicit ``total_time``, and the logging object's start/end window otherwise
    (a streamed response is never stamped).
    """
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    deployment_id = "per-second-openai-deployment"
    litellm.register_model(
        model_cost={
            deployment_id: {
                "input_cost_per_second": 0.02,
                "output_cost_per_second": 0.04,
                "litellm_provider": "openai",
                "mode": "chat",
            }
        }
    )
    response = ModelResponse(
        model="gpt-5.4-nano",
        usage=Usage(prompt_tokens=11, completion_tokens=7, total_tokens=18),
    )
    response._response_ms = stamped_response_ms

    cost = completion_cost(
        completion_response=response,
        model="openai/gpt-5.4-nano",
        custom_llm_provider="openai",
        custom_pricing=True,
        router_model_id=deployment_id,
        total_time=total_time,
        litellm_logging_obj=_logging_obj_with_call_window(logged_duration_ms),
    )

    assert cost == pytest.approx(0.02 * expected_seconds)


@pytest.mark.parametrize("mode", ["audio_speech", "video_generation", "realtime"])
def test_cost_per_token_leaves_media_second_rates_to_their_dedicated_paths(monkeypatch, mode: str):
    """
    A media-mode entry's per-second rates price audio or video seconds, which the dedicated
    transcription, speech, video, and realtime paths bill from the media itself, so a call that
    reaches the generic path with only a wall-clock duration must not bill them.
    """
    model = f"test-media-per-second-{mode}"
    monkeypatch.setitem(
        litellm.model_cost,
        model,
        {"input_cost_per_second": 0.02, "output_cost_per_second": 0.4, "litellm_provider": "openai", "mode": mode},
    )

    assert cost_per_token(model=model, custom_llm_provider="openai", response_time_ms=2000.0) == (0.0, 0.0)


@pytest.mark.parametrize(
    ("audio_seconds", "expected_cost"),
    [(0.0, (0.04, 0.0)), (60.0, (1.2, 0.0))],
    ids=["no_audio_length_bills_request_time", "audio_length_bills_audio_seconds"],
)
def test_cost_per_token_bills_transcription_second_rates(
    monkeypatch: pytest.MonkeyPatch, audio_seconds: float, expected_cost: tuple[float, float]
) -> None:
    model: Final = "test-transcription-per-second"
    monkeypatch.setitem(
        litellm.model_cost,
        model,
        {"input_cost_per_second": 0.02, "litellm_provider": "deepgram", "mode": "audio_transcription"},
    )

    assert cost_per_token(
        model=model,
        custom_llm_provider="deepgram",
        response_time_ms=2000.0,
        audio_transcription_file_duration=audio_seconds,
    ) == pytest.approx(expected_cost)


def test_completion_cost_video_status_poll_bills_nothing_on_a_per_second_video_model(monkeypatch):
    """
    Polling a video job returns a ``VideoObject`` with no stamped duration, so the cost path falls
    back to the logging object's call window; on a video model priced per output second that
    window must not be billed, or every status poll would charge for the seconds it took to answer.
    """
    model = "test-veo-per-second-poll"
    monkeypatch.setitem(
        litellm.model_cost,
        model,
        {"output_cost_per_second": 0.4, "litellm_provider": "vertex_ai", "mode": "video_generation"},
    )
    video = VideoObject(id="video_1", object="video", status="completed", model=model, progress=100)

    cost = completion_cost(
        completion_response=video,
        model=model,
        custom_llm_provider="vertex_ai",
        call_type=CallTypes.video_retrieve.value,
        litellm_logging_obj=_logging_obj_with_call_window(2000.0),
    )

    assert cost == 0.0


def _batch_cache_usage() -> Usage:
    return Usage(
        prompt_tokens=11000,
        completion_tokens=200,
        total_tokens=11200,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            cached_tokens=8000,
            cache_creation_tokens=2000,
            text_tokens=1000,
        ),
        cache_creation_input_tokens=2000,
        cache_read_input_tokens=8000,
    )


def test_batch_cost_calculator_prices_multimodal_tokens_at_modality_rates():
    model_info: ModelInfo = {
        "input_cost_per_token_batches": 1e-7,
        "input_cost_per_audio_token_batches": 3.25e-6,
        "input_cost_per_image_token_batches": 2.25e-7,
        "input_cost_per_video_token_batches": 6e-6,
    }
    usage = Usage(
        prompt_tokens=100,
        completion_tokens=0,
        total_tokens=100,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            audio_tokens=64,
            image_tokens=10,
            video_tokens=6,
        ),
    )

    prompt_cost, _ = batch_cost_calculator(
        usage=usage,
        model="gemini-embedding-2",
        custom_llm_provider="vertex_ai",
        model_info=model_info,
    )

    assert prompt_cost == pytest.approx(20 * 1e-7 + 64 * 3.25e-6 + 10 * 2.25e-7 + 6 * 6e-6)


def test_batch_cost_calculator_falls_back_to_text_batch_rate_for_modalities():
    model_info: ModelInfo = {"input_cost_per_token_batches": 1e-7}
    usage = Usage(
        prompt_tokens=100,
        completion_tokens=0,
        total_tokens=100,
        prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=64),
    )

    prompt_cost, _ = batch_cost_calculator(
        usage=usage,
        model="gemini-embedding-2",
        custom_llm_provider="vertex_ai",
        model_info=model_info,
    )

    assert prompt_cost == pytest.approx(100 * 1e-7)


@pytest.mark.parametrize(
    ("image_batch_rate", "image_tokens", "expected_completion_cost"),
    (
        (5e-5, 80, 0.00408),
        (None, 80, 0.00328),
        (5e-5, 140, 0.006),
    ),
)
def test_batch_cost_calculator_prices_image_completion_tokens_at_image_batch_rate(
    image_batch_rate: float | None, image_tokens: int, expected_completion_cost: float
) -> None:
    model_info: Final[ModelInfo] = (
        {
            "output_cost_per_token_batches": 2e-6,
            "output_cost_per_image_token": 8e-5,
            "output_cost_per_image_token_batches": image_batch_rate,
        }
        if image_batch_rate is not None
        else {
            "output_cost_per_token_batches": 2e-6,
            "output_cost_per_image_token": 8e-5,
        }
    )
    usage: Final = Usage(
        prompt_tokens=0,
        completion_tokens=120,
        total_tokens=120,
        completion_tokens_details=CompletionTokensDetailsWrapper(image_tokens=image_tokens),
    )

    costs: Final = batch_cost_calculator(
        usage=usage,
        model="gemini-nano-banana-2.1",
        custom_llm_provider="vertex_ai",
        model_info=model_info,
    )

    assert costs[1] == pytest.approx(expected_completion_cost)


def test_batch_cost_calculator_merges_image_only_deployment_rate_with_global_pricing(
    _local_model_cost_map: None,
) -> None:
    model: Final = "gemini/gemini-3-pro-image"
    global_model_info: Final = litellm.get_model_info(model=model, custom_llm_provider="gemini")
    input_batch_rate: Final = global_model_info["input_cost_per_token_batches"]
    output_batch_rate: Final = global_model_info["output_cost_per_token_batches"]
    global_image_batch_rate: Final = global_model_info["output_cost_per_image_token_batches"]
    assert input_batch_rate is not None
    assert output_batch_rate is not None
    assert global_image_batch_rate is not None

    deployment_image_batch_rate: Final = 1e-6
    assert deployment_image_batch_rate != global_image_batch_rate

    text_tokens: Final = 300
    image_tokens: Final = 200
    usage: Final = Usage(
        prompt_tokens=1_000,
        completion_tokens=text_tokens + image_tokens,
        total_tokens=1_500,
        completion_tokens_details=CompletionTokensDetailsWrapper(image_tokens=image_tokens),
    )
    model_info: Final = ModelInfo(output_cost_per_image_token_batches=deployment_image_batch_rate)

    prompt_cost, completion_cost = batch_cost_calculator(
        usage=usage,
        model=model,
        custom_llm_provider="gemini",
        model_info=model_info,
    )

    assert prompt_cost > 0
    assert completion_cost > 0
    assert prompt_cost == pytest.approx(usage.prompt_tokens * input_batch_rate)
    expected_completion_cost: Final = text_tokens * output_batch_rate + image_tokens * deployment_image_batch_rate
    global_rate_completion_cost: Final = text_tokens * output_batch_rate + image_tokens * global_image_batch_rate
    assert completion_cost == pytest.approx(expected_completion_cost)
    assert completion_cost != pytest.approx(global_rate_completion_cost)


def test_batch_cost_calculator_prices_cache_creation_tokens_at_cache_write_rate():
    """
    LIT-4008 regression: anthropic batch usage is dominated by cache tokens.
    Cache creation tokens must be priced at cache_creation_input_token_cost / 2,
    not folded into the base input rate, and must not also be billed as base
    input tokens.
    """
    from litellm.cost_calculator import batch_cost_calculator

    model_info: ModelInfo = {
        "supported_openai_params": [],
        "input_cost_per_token": 3e-6,
        "output_cost_per_token": 15e-6,
        "cache_read_input_token_cost": 3e-7,
        "cache_creation_input_token_cost": 3.75e-6,
    }
    prompt_cost, completion_cost_value = batch_cost_calculator(
        usage=_batch_cache_usage(),
        model="claude-sonnet-4-5-20250929",
        custom_llm_provider="anthropic",
        model_info=model_info,
    )

    assert prompt_cost == pytest.approx((1000 * 3e-6 + 8000 * 3e-7 + 2000 * 3.75e-6) / 2)
    assert completion_cost_value == pytest.approx(200 * 15e-6 / 2)


def test_batch_cost_calculator_cache_creation_falls_back_to_input_rate():
    from litellm.cost_calculator import batch_cost_calculator

    model_info: ModelInfo = {
        "supported_openai_params": [],
        "input_cost_per_token": 3e-6,
        "output_cost_per_token": 15e-6,
        "cache_read_input_token_cost": 3e-7,
    }
    prompt_cost, _ = batch_cost_calculator(
        usage=_batch_cache_usage(),
        model="claude-sonnet-4-5-20250929",
        custom_llm_provider="anthropic",
        model_info=model_info,
    )

    assert prompt_cost == pytest.approx((1000 * 3e-6 + 8000 * 3e-7 + 2000 * 3e-6) / 2)


def test_completion_cost_bills_interactions_api_response():
    from litellm.types.interactions import InteractionsAPIResponse

    model_info = litellm.get_model_info(model="gemini-2.5-flash", custom_llm_provider="gemini")
    response = InteractionsAPIResponse(
        id="interactions/abc123",
        model="gemini-2.5-flash",
        status="completed",
        steps=[],
        usage={
            "total_tokens": 175,
            "total_input_tokens": 100,
            "input_tokens_by_modality": [{"modality": "text", "tokens": 100}],
            "total_cached_tokens": 0,
            "total_output_tokens": 50,
            "output_tokens_by_modality": [{"modality": "text", "tokens": 50}],
            "total_tool_use_tokens": 0,
            "total_thought_tokens": 25,
        },
    )

    cost = completion_cost(completion_response=response, custom_llm_provider="gemini")

    reasoning_rate = model_info.get("output_cost_per_reasoning_token") or model_info["output_cost_per_token"]
    expected = 100 * model_info["input_cost_per_token"] + 50 * model_info["output_cost_per_token"] + 25 * reasoning_rate
    assert cost == pytest.approx(expected)
    assert cost > 0


def test_completion_cost_bills_interactions_google_search_per_query():
    from litellm.types.interactions import InteractionsAPIResponse

    model_info = litellm.get_model_info(model="gemini-3-flash-preview", custom_llm_provider="gemini")
    response = InteractionsAPIResponse(
        id="interactions/search123",
        model="gemini-3-flash-preview",
        status="completed",
        steps=[],
        usage={
            "total_tokens": 680,
            "total_input_tokens": 103,
            "input_tokens_by_modality": [{"modality": "text", "tokens": 103}],
            "total_cached_tokens": 0,
            "total_output_tokens": 226,
            "total_tool_use_tokens": 0,
            "total_thought_tokens": 351,
            "grounding_tool_count": [{"type": "google_search", "count": 3}],
        },
    )

    cost = completion_cost(completion_response=response, custom_llm_provider="gemini")

    per_query_cost = model_info["search_context_cost_per_query"]["search_context_size_medium"]
    reasoning_rate = model_info.get("output_cost_per_reasoning_token") or model_info["output_cost_per_token"]
    expected = (
        103 * model_info["input_cost_per_token"]
        + 226 * model_info["output_cost_per_token"]
        + 351 * reasoning_rate
        + 3 * per_query_cost
    )
    assert model_info.get("web_search_billing_unit") == "per_query"
    assert cost == pytest.approx(expected)
    assert cost > 3 * per_query_cost


def test_completion_cost_bills_interactions_video_output_at_video_rate():
    from litellm.types.interactions import InteractionsAPIResponse

    model_info = litellm.get_model_info(model="gemini-omni-flash-preview", custom_llm_provider="gemini")
    video_tokens = 5792 * 8
    response = InteractionsAPIResponse(
        id="interactions/video123",
        model="gemini-omni-flash-preview",
        status="completed",
        steps=[],
        usage={
            "total_tokens": 10 + video_tokens,
            "total_input_tokens": 10,
            "input_tokens_by_modality": [{"modality": "text", "tokens": 10}],
            "total_cached_tokens": 0,
            "total_output_tokens": video_tokens,
            "output_tokens_by_modality": [{"modality": "video", "tokens": video_tokens}],
            "total_tool_use_tokens": 0,
            "total_thought_tokens": 0,
        },
    )

    cost = completion_cost(completion_response=response, custom_llm_provider="gemini")

    expected = 10 * model_info["input_cost_per_token"] + video_tokens * model_info["output_cost_per_video_token"]
    assert model_info["output_cost_per_video_token"] != model_info["output_cost_per_token"]
    assert cost == pytest.approx(expected)


@pytest.mark.parametrize("video_count", [2, 3])
def test_completion_cost_multiplies_video_cost_by_generated_video_count(video_count: int) -> None:
    """Regression for LIT-6896: a Veo request for N samples generates N videos and must be billed N times."""
    from litellm.types.videos.main import VideoObject

    def _video(usage: dict[str, object]) -> VideoObject:
        return VideoObject(id="v", object="video", status="processing", model="veo-3.1-fast-generate-001", usage=usage)

    single_cost = completion_cost(
        completion_response=_video({"duration_seconds": 4.0, "video_resolution": "720p"}),
        model="veo-3.1-fast-generate-001",
        custom_llm_provider="vertex_ai",
        call_type="create_video",
    )
    multi_cost = completion_cost(
        completion_response=_video({"duration_seconds": 4.0, "video_resolution": "720p", "video_count": video_count}),
        model="veo-3.1-fast-generate-001",
        custom_llm_provider="vertex_ai",
        call_type="create_video",
    )

    assert single_cost > 0
    assert multi_cost == pytest.approx(single_cost * video_count)


@pytest.mark.parametrize(
    "batch_rate,expected_prompt,expected_completion",
    [
        (0.0, 0.0, 0.0),
        (1e-6, 1000 * 1e-6, 500 * 1e-6),
        (None, 1000 * 3e-6 / 2, 500 * 15e-6 / 2),
    ],
    ids=["explicit-zero", "explicit-nonzero", "unset"],
)
def test_batch_cost_calculator_honors_an_explicitly_zero_batch_rate(
    batch_rate: float | None,
    expected_prompt: float,
    expected_completion: float,
) -> None:
    """A batch rate configured as 0.0 means free, not unset.

    Gating the batch fields on truthiness read an explicit 0.0 as absent and
    charged half the standard rate for that token direction instead.
    """
    from litellm.cost_calculator import batch_cost_calculator

    base_model_info: ModelInfo = {
        "supported_openai_params": [],
        "input_cost_per_token": 3e-6,
        "output_cost_per_token": 15e-6,
    }
    model_info: ModelInfo = (
        base_model_info
        if batch_rate is None
        else {
            **base_model_info,
            "input_cost_per_token_batches": batch_rate,
            "output_cost_per_token_batches": batch_rate,
        }
    )

    prompt_cost, completion_cost_value = batch_cost_calculator(
        usage=Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500),
        model="claude-sonnet-4-5-20250929",
        custom_llm_provider="anthropic",
        model_info=model_info,
    )

    assert prompt_cost == pytest.approx(expected_prompt)
    assert completion_cost_value == pytest.approx(expected_completion)


def test_combine_usage_objects_sums_mirrored_cache_write_fields_once():
    """
    cache_write_tokens and cache_creation_tokens mirror each other on
    PromptTokensDetailsWrapper, so field-iterating aggregation must sum the pair
    once: a single 50-token usage stays 50 and two combine to 100, not double.
    """
    single = Usage(
        prompt_tokens=100,
        completion_tokens=10,
        total_tokens=110,
        prompt_tokens_details=PromptTokensDetailsWrapper(cache_write_tokens=50),
    )
    combined = BaseTokenUsageProcessor.combine_usage_objects([single])
    assert combined.prompt_tokens_details is not None
    assert combined.prompt_tokens_details.cache_write_tokens == 50
    assert combined.prompt_tokens_details.cache_creation_tokens == 50

    anthropic_style = Usage(
        prompt_tokens=100,
        completion_tokens=10,
        total_tokens=110,
        cache_creation_input_tokens=50,
    )
    combined_pair = BaseTokenUsageProcessor.combine_usage_objects([anthropic_style, anthropic_style])
    assert combined_pair.prompt_tokens_details is not None
    assert combined_pair.prompt_tokens_details.cache_write_tokens == 100
    assert combined_pair.prompt_tokens_details.cache_creation_tokens == 100


def test_select_model_name_strips_unregistered_alias_prefix(_local_model_cost_map):
    """A router-facing model_name alias containing "/" whose leading segment is NOT a
    registered provider must not be double-prefixed into a non-existent cost key.

    Regression test for #38069: alias "vertex/claude-opus-5" (real deployment
    "vertex_ai/claude-opus-5") was re-prefixed into "vertex_ai/vertex/claude-opus-5",
    silently pricing every streamed request at $0.
    """

    from litellm.cost_calculator import select_model_name_for_cost_calc

    response = litellm.ModelResponse(
        id="x",
        choices=[
            {
                "index": 0,
                "message": {"role": "assistant", "content": "hi"},
                "finish_reason": "stop",
            }
        ],
        model="vertex/claude-opus-5",
    )
    response._hidden_params = {}

    selected = select_model_name_for_cost_calc(
        model=None,
        completion_response=response,
        custom_llm_provider="vertex_ai",
    )

    assert selected == "vertex_ai/claude-opus-5"


def test_select_model_name_strips_duplicated_region_segment(_local_model_cost_map):
    """A "region/model" alias whose leading segment repeats the request's region must
    resolve to the region-priced cost key instead of keeping the region segment twice."""

    from litellm.cost_calculator import select_model_name_for_cost_calc

    response = litellm.ModelResponse(
        id="x",
        choices=[
            {
                "index": 0,
                "message": {"role": "assistant", "content": "hi"},
                "finish_reason": "stop",
            }
        ],
        model="us-east-1/anthropic.claude-v2:1",
    )
    response._hidden_params = {"region_name": "us-east-1"}

    selected = select_model_name_for_cost_calc(
        model=None,
        completion_response=response,
        custom_llm_provider="bedrock",
    )

    assert selected == "bedrock/us-east-1/anthropic.claude-v2:1"


def _bedrock_response_with_private_model(model: str, region_name: str) -> litellm.ModelResponse:
    response = litellm.ModelResponse(
        id="x",
        choices=[
            {
                "index": 0,
                "message": {"role": "assistant", "content": "hi"},
                "finish_reason": "stop",
            }
        ],
        model=model,
    )
    response._hidden_params = {"provider_response_model": model, "region_name": region_name}
    return response


def test_select_model_name_applies_region_to_private_provider_response_model(_local_model_cost_map):
    """A Bedrock stream carries its requested model as the private provider model and must keep the
    request's region in the cost key, exactly as the same request does without streaming."""

    from litellm.cost_calculator import select_model_name_for_cost_calc

    selected = select_model_name_for_cost_calc(
        model=None,
        completion_response=_bedrock_response_with_private_model("anthropic.claude-v2:1", "us-east-1"),
        custom_llm_provider="bedrock",
    )

    assert selected == "bedrock/us-east-1/anthropic.claude-v2:1"


def test_completion_cost_region_name_prices_mantle_on_the_regional_row(_local_model_cost_map):
    """completion_cost(region_name=...) must price a Bedrock Mantle call from the
    bedrock_mantle/<region>/<model> row when one exists, for the bare and the provider-prefixed
    model alike, and keep the flat row for regions without their own row."""

    response = litellm.ModelResponse(
        id="x",
        choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}],
        model="xai.grok-4.3",
        usage={"prompt_tokens": 38, "completion_tokens": 20, "total_tokens": 58},
    )
    gov = litellm.model_cost["bedrock_mantle/us-gov-west-1/xai.grok-4.3"]
    flat = litellm.model_cost["bedrock_mantle/xai.grok-4.3"]
    expected_gov = 38 * gov["input_cost_per_token"] + 20 * gov["output_cost_per_token"]
    expected_flat = 38 * flat["input_cost_per_token"] + 20 * flat["output_cost_per_token"]
    assert expected_gov != expected_flat

    for model in ("xai.grok-4.3", "bedrock_mantle/xai.grok-4.3"):
        assert litellm.completion_cost(
            completion_response=response,
            model=model,
            custom_llm_provider="bedrock_mantle",
            region_name="us-gov-west-1",
        ) == pytest.approx(expected_gov)
        assert litellm.completion_cost(
            completion_response=response,
            model=model,
            custom_llm_provider="bedrock_mantle",
            region_name="eu-west-1",
        ) == pytest.approx(expected_flat)
    assert litellm.completion_cost(
        completion_response=response, model="xai.grok-4.3", custom_llm_provider="bedrock_mantle"
    ) == pytest.approx(expected_flat)


def test_completion_cost_discount_matches_region_stripped_model(monkeypatch, _local_model_cost_map):
    """A discount pattern written against the bare model name must still match when the
    cost-selected name carries a <provider>/<region>/ prefix from a priced regional row."""

    response = litellm.ModelResponse(
        id="x",
        choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}],
        model="xai.grok-4.3",
        usage={"prompt_tokens": 38, "completion_tokens": 20, "total_tokens": 58},
    )

    monkeypatch.setattr(litellm, "cost_discount_config", {})
    undiscounted = litellm.completion_cost(
        completion_response=response,
        model="xai.grok-4.3",
        custom_llm_provider="bedrock_mantle",
        region_name="us-gov-west-1",
    )
    assert undiscounted > 0

    monkeypatch.setattr(
        litellm,
        "cost_discount_config",
        {"bedrock_mantle": 0.05, "bedrock_mantle/xai.grok-*": 0.20},
    )
    discounted = litellm.completion_cost(
        completion_response=response,
        model="xai.grok-4.3",
        custom_llm_provider="bedrock_mantle",
        region_name="us-gov-west-1",
    )

    assert discounted == pytest.approx(undiscounted * 0.8, rel=1e-9)


def test_cost_per_token_region_name_applies_to_provider_prefixed_model(_local_model_cost_map):
    """A provider-prefixed model must still find its bedrock_mantle/<region>/<model> row instead of
    composing the region key with the provider segment twice."""

    prompt_cost, completion_cost = litellm.cost_per_token(
        model="bedrock_mantle/xai.grok-4.3",
        prompt_tokens=38,
        completion_tokens=20,
        custom_llm_provider="bedrock_mantle",
        region_name="us-gov-west-1",
    )
    gov = litellm.model_cost["bedrock_mantle/us-gov-west-1/xai.grok-4.3"]

    assert prompt_cost + completion_cost == pytest.approx(
        38 * gov["input_cost_per_token"] + 20 * gov["output_cost_per_token"]
    )


def test_completion_cost_mantle_native_messages_prices_claude_from_the_bedrock_row(_local_model_cost_map):
    """Mantle's native Messages API answers with Anthropic's canonical model name and the proxy
    resolves a Mantle region for every call, so the first cost candidate is
    bedrock_mantle/<region>/claude-sonnet-5. That name has no row of its own and must fall through to
    the deployment's bare Bedrock row instead of stopping on an unpriced capability rule at $0."""

    response = litellm.ModelResponse(
        id="msg_x",
        choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}],
        model="claude-sonnet-5",
        usage={"prompt_tokens": 100, "completion_tokens": 10, "total_tokens": 110},
    )
    row = litellm.model_cost["anthropic.claude-sonnet-5"]
    expected = 100 * row["input_cost_per_token"] + 10 * row["output_cost_per_token"]
    assert expected > 0

    for region_name in ("us-east-1", None):
        assert litellm.completion_cost(
            completion_response=response,
            model="bedrock_mantle/anthropic.claude-sonnet-5",
            custom_llm_provider="bedrock_mantle",
            region_name=region_name,
        ) == pytest.approx(expected)


@pytest.mark.parametrize(
    "response_model,mantle_row,deployment_models",
    [
        (
            "claude-haiku-4-5",
            "bedrock_mantle/anthropic.claude-haiku-4-5",
            (
                "bedrock_mantle/anthropic.claude-haiku-4-5",
                "bedrock_mantle/us-east-2/anthropic.claude-haiku-4-5",
            ),
        ),
        (
            "claude-opus-5-5",
            "bedrock_mantle/anthropic.claude-opus-5-5",
            ("bedrock_mantle/anthropic.claude-opus-5-5",),
        ),
        (
            "claude-sonnet-5-5",
            "bedrock_mantle/anthropic.claude-sonnet-5-5",
            ("bedrock_mantle/anthropic.claude-sonnet-5-5",),
        ),
    ],
)
def test_completion_cost_mantle_native_messages_prices_unversioned_claude_from_the_mantle_row(
    _local_model_cost_map, response_model, mantle_row, deployment_models
):
    """Mantle serves Anthropic's un-versioned Claude ids; the plain and region-prefixed deployment
    names must price from the model's own bedrock_mantle/ row instead of billing $0."""

    response = litellm.ModelResponse(
        id="msg_x",
        choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}],
        model=response_model,
        usage={"prompt_tokens": 100, "completion_tokens": 10, "total_tokens": 110},
    )
    row = litellm.model_cost[mantle_row]
    expected = 100 * row["input_cost_per_token"] + 10 * row["output_cost_per_token"]
    assert expected > 0

    for model in deployment_models:
        assert litellm.completion_cost(
            completion_response=response,
            model=model,
            custom_llm_provider="bedrock_mantle",
        ) == pytest.approx(expected), model


@pytest.mark.parametrize("model", ["anthropic.claude-opus-5-5", "anthropic.claude-sonnet-5-5"])
def test_completion_cost_region_without_its_own_row_prices_mantle_claude_from_the_mantle_row(
    _local_model_cost_map, model: str
):
    """The proxy resolves a Mantle region for every call. A region with no
    bedrock_mantle/<region>/<model> row must fall back to the model's own bedrock_mantle/ row, not to the
    bare Bedrock row that the bedrock provider family also matches."""

    response = litellm.ModelResponse(
        id="msg_x",
        choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}],
        model=model,
        usage={"prompt_tokens": 100, "completion_tokens": 10, "total_tokens": 110},
    )
    mantle: Final[Mapping[str, float]] = litellm.model_cost[f"bedrock_mantle/{model}"]
    bedrock: Final[Mapping[str, float]] = litellm.model_cost[model]
    expected: Final = 100 * mantle["input_cost_per_token"] + 10 * mantle["output_cost_per_token"]
    assert expected != 100 * bedrock["input_cost_per_token"] + 10 * bedrock["output_cost_per_token"]

    for deployment in (model, f"bedrock_mantle/{model}", f"bedrock_mantle/us-east-1/{model}"):
        assert litellm.completion_cost(
            completion_response=response,
            model=deployment,
            custom_llm_provider="bedrock_mantle",
            region_name="us-east-1",
        ) == pytest.approx(expected), deployment
    assert (
        litellm.get_model_info(f"bedrock_mantle/us-east-1/{model}", "bedrock_mantle")["key"]
        == f"bedrock_mantle/{model}"
    )


@pytest.mark.parametrize("model", ["anthropic.claude-opus-5-5", "anthropic.claude-sonnet-5-5"])
def test_cost_per_token_gov_region_prices_mantle_claude_on_the_gov_row(_local_model_cost_map, model):
    """A bedrock_mantle/ deployment in us-gov-west-1 must price from the
    bedrock_mantle/us-gov-west-1/<model> row."""

    prompt_cost, completion_cost = litellm.cost_per_token(
        model=f"bedrock_mantle/{model}",
        prompt_tokens=38,
        completion_tokens=20,
        custom_llm_provider="bedrock_mantle",
        region_name="us-gov-west-1",
    )
    gov = litellm.model_cost[f"bedrock_mantle/us-gov-west-1/{model}"]

    assert prompt_cost + completion_cost == pytest.approx(
        38 * gov["input_cost_per_token"] + 20 * gov["output_cost_per_token"]
    )


def test_completion_cost_legacy_mantle_route_prices_after_router_registration(local_model_cost_map):
    """The proxy registers every deployment under its provider-prefixed key at boot. A
    bedrock/mantle/<model> deployment must resolve to the bare Bedrock row there, otherwise the boot
    entry is a cost-less capability rule that shadows the priced row and every call on the deployment,
    /v1/chat/completions and /v1/messages alike, bills $0."""
    from litellm import Router

    Router(
        model_list=[
            {
                "model_name": "claude-sonnet-5",
                "litellm_params": {
                    "model": "bedrock/mantle/anthropic.claude-sonnet-5",
                    "aws_region_name": "us-east-1",
                },
            }
        ]
    )
    assert "bedrock/mantle/anthropic.claude-sonnet-5" not in litellm.model_cost

    response = litellm.ModelResponse(
        id="msg_x",
        choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}],
        model="claude-sonnet-5",
        usage={"prompt_tokens": 16, "completion_tokens": 4, "total_tokens": 20},
    )
    row = litellm.model_cost["anthropic.claude-sonnet-5"]
    expected = 16 * row["input_cost_per_token"] + 4 * row["output_cost_per_token"]
    assert expected > 0

    for call_type in ("completion", "anthropic_messages"):
        assert litellm.completion_cost(
            completion_response=response,
            model="mantle/anthropic.claude-sonnet-5",
            custom_llm_provider="bedrock",
            call_type=call_type,
        ) == pytest.approx(expected), call_type


def test_select_model_name_keeps_base_model_free_of_region(_local_model_cost_map):
    """An explicit base_model keeps pricing on that model's own key even when the request carries a
    region with different regional rates, so the private provider model never widens region pricing."""

    from litellm.cost_calculator import select_model_name_for_cost_calc

    selected = select_model_name_for_cost_calc(
        model="my-bedrock-deployment",
        completion_response=_bedrock_response_with_private_model("moonshotai.kimi-k2.5", "ap-northeast-1"),
        base_model="moonshotai.kimi-k2.5",
        custom_llm_provider="bedrock",
    )

    assert selected == "bedrock/moonshotai.kimi-k2.5"


def test_completion_cost_base_model_ignores_regional_row(_local_model_cost_map):
    """A deployment with base_model set is priced from that model's own row even when the response
    carries a region whose regional row charges different rates."""

    response = litellm.ModelResponse(
        id="x",
        choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}],
        model="my-bedrock-deployment",
        usage={"prompt_tokens": 1000, "completion_tokens": 0, "total_tokens": 1000},
    )
    response._hidden_params = {"custom_llm_provider": "bedrock", "region_name": "eu-central-1"}
    flat = litellm.model_cost["anthropic.claude-instant-v1"]
    regional = litellm.model_cost["bedrock/eu-central-1/anthropic.claude-instant-v1"]
    assert flat["input_cost_per_token"] != regional["input_cost_per_token"]

    assert litellm.completion_cost(
        completion_response=response,
        model="my-bedrock-deployment",
        custom_llm_provider="bedrock",
        base_model="anthropic.claude-instant-v1",
    ) == pytest.approx(1000 * flat["input_cost_per_token"])


def test_select_model_name_unresolvable_alias_unchanged(_local_model_cost_map):
    """An alias that resolves to no known cost key keeps the legacy double-prefixed name."""

    from litellm.cost_calculator import select_model_name_for_cost_calc

    response = litellm.ModelResponse(
        id="x",
        choices=[
            {
                "index": 0,
                "message": {"role": "assistant", "content": "hi"},
                "finish_reason": "stop",
            }
        ],
        model="team/nonsense-model",
    )
    response._hidden_params = {}

    selected = select_model_name_for_cost_calc(
        model=None,
        completion_response=response,
        custom_llm_provider="vertex_ai",
    )

    assert selected == "vertex_ai/team/nonsense-model"


def test_completion_cost_keeps_custom_priced_slash_router_id(_local_model_cost_map):
    """A custom-priced router id containing "/" keeps its custom pricing instead of being
    rewritten to the built-in key its suffix happens to match."""

    from litellm.cost_calculator import select_model_name_for_cost_calc

    litellm.register_model(
        model_cost={
            "vertex/claude-opus-5": {
                "input_cost_per_token": 7e-6,
                "output_cost_per_token": 8e-6,
                "litellm_provider": "vertex_ai",
            }
        }
    )

    selected = select_model_name_for_cost_calc(
        model="vertex_ai/claude-opus-5",
        completion_response=None,
        custom_pricing=True,
        custom_llm_provider="vertex_ai",
        router_model_id="vertex/claude-opus-5",
    )
    assert selected == "vertex_ai/vertex/claude-opus-5"

    response = litellm.ModelResponse(
        id="x",
        choices=[
            {
                "index": 0,
                "message": {"role": "assistant", "content": "hi"},
                "finish_reason": "stop",
            }
        ],
        model="vertex/claude-opus-5",
    )
    response._hidden_params = {"custom_llm_provider": "vertex_ai"}
    response.usage = litellm.Usage(prompt_tokens=100, completion_tokens=50)

    cost = litellm.completion_cost(
        completion_response=response,
        custom_llm_provider="vertex_ai",
        custom_pricing=True,
        router_model_id="vertex/claude-opus-5",
    )
    assert cost == pytest.approx(100 * 7e-6 + 50 * 8e-6, rel=1e-9)


@pytest.mark.parametrize(
    "priceless_entry",
    [
        {"litellm_provider": "vertex_ai", "mode": "realtime"},
        {
            "litellm_provider": "vertex_ai",
            "mode": "realtime",
            "input_cost_per_token": None,
            "output_cost_per_token": None,
            "input_cost_per_audio_token": None,
        },
    ],
    ids=["registered_without_price_fields", "registered_with_none_valued_price_fields"],
)
def test_realtime_priceless_deployment_entry_falls_through_to_priced_model(
    _local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch, priceless_entry: dict
) -> None:
    """Regression for https://github.com/BerriAI/litellm/issues/31087 (router-registered priceless entries)."""
    monkeypatch.setitem(
        litellm.model_cost,
        "vertex_ai/some-unmapped-live-model",
        priceless_entry,
    )
    priced_model = "vertex_ai/gemini-live-2.5-flash-preview-native-audio-09-2025"
    priced_entry = litellm.model_cost["gemini-live-2.5-flash-preview-native-audio-09-2025"]

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": "some-unmapped-live-model"}},
    ]
    combined_usage_object = Usage(prompt_tokens=8, completion_tokens=25, total_tokens=33)

    cost = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=combined_usage_object,
        custom_llm_provider="vertex_ai",
        litellm_model_name=priced_model,
    )

    expected_cost = 8 * priced_entry["input_cost_per_token"] + 25 * priced_entry["output_cost_per_token"]
    assert cost == pytest.approx(expected_cost, rel=1e-9)
    assert cost > 0


def test_realtime_explicitly_free_session_model_still_bills_zero(
    _local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch
) -> None:
    monkeypatch.setitem(
        litellm.model_cost,
        "vertex_ai/free-live-model",
        {
            "litellm_provider": "vertex_ai",
            "mode": "realtime",
            "input_cost_per_token": 0.0,
            "output_cost_per_token": 0.0,
        },
    )

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": "free-live-model"}},
    ]
    combined_usage_object = Usage(prompt_tokens=8, completion_tokens=25, total_tokens=33)

    cost = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=combined_usage_object,
        custom_llm_provider="vertex_ai",
        litellm_model_name="vertex_ai/gemini-live-2.5-flash-preview-native-audio-09-2025",
    )

    assert cost == 0.0


def test_completion_cost_prefers_private_provider_response_model(
    _local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch
) -> None:
    monkeypatch.setitem(
        litellm.model_cost,
        "openai/selected-cost-model",
        {
            "input_cost_per_token": 0.000002,
            "output_cost_per_token": 0.000004,
            "litellm_provider": "openai",
        },
    )
    response = litellm.ModelResponse(
        id="x",
        choices=[
            {
                "index": 0,
                "message": {"role": "assistant", "content": "hi"},
                "finish_reason": "stop",
            }
        ],
        model="requested-route",
    )
    response._hidden_params = {
        "custom_llm_provider": "openai",
        "provider_response_model": "selected-cost-model",
    }
    response.usage = litellm.Usage(prompt_tokens=100, completion_tokens=50)

    cost = litellm.completion_cost(
        completion_response=response,
        custom_llm_provider="openai",
    )

    assert response.model == "requested-route"
    assert cost == pytest.approx(100 * 0.000002 + 50 * 0.000004)


@pytest.mark.parametrize(
    ("base_model", "custom_pricing", "expected"),
    [
        ("openai/base-model", False, "openai/base-model"),
        (None, True, "openai/requested-route"),
    ],
)
def test_explicit_pricing_precedes_private_provider_response_model(
    base_model: str | None,
    custom_pricing: bool,
    expected: str,
) -> None:
    from litellm.cost_calculator import select_model_name_for_cost_calc

    response = litellm.ModelResponse(
        id="x",
        choices=[
            {
                "index": 0,
                "message": {"role": "assistant", "content": "hi"},
                "finish_reason": "stop",
            }
        ],
        model="requested-route",
    )
    response._hidden_params = {"provider_response_model": "selected-cost-model"}

    selected = select_model_name_for_cost_calc(
        model="requested-route",
        completion_response=response,
        base_model=base_model,
        custom_pricing=custom_pricing,
        custom_llm_provider="openai",
    )

    assert selected == expected


def test_collect_and_combine_realtime_usage_stores_partitioned_text_tokens() -> None:
    """The combined usage that lands in spend logs keeps reasoning out of text_tokens for every turn."""
    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": "gpt-realtime-2.1-mini"}},
        {
            "type": "response.done",
            "response": {
                "usage": {
                    "total_tokens": 307,
                    "input_tokens": 237,
                    "output_tokens": 70,
                    "input_token_details": {
                        "text_tokens": 43,
                        "audio_tokens": 0,
                        "image_tokens": 194,
                        "cached_tokens": 0,
                    },
                    "output_token_details": {"text_tokens": 70, "audio_tokens": 0, "reasoning_tokens": 52},
                }
            },
        },
        {
            "type": "response.done",
            "response": {
                "usage": {
                    "total_tokens": 363,
                    "input_tokens": 300,
                    "output_tokens": 63,
                    "input_token_details": {
                        "text_tokens": 106,
                        "audio_tokens": 0,
                        "image_tokens": 194,
                        "cached_tokens": 0,
                    },
                    "output_token_details": {"text_tokens": 63, "audio_tokens": 0, "reasoning_tokens": 43},
                }
            },
        },
    ]

    combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results)

    assert combined.completion_tokens == 133
    assert combined.completion_tokens_details is not None
    assert combined.completion_tokens_details.reasoning_tokens == 95
    assert combined.completion_tokens_details.text_tokens == 38
    assert combined.completion_tokens_details.audio_tokens == 0


def test_realtime_combine_sums_nested_cached_tokens_details():
    results: OpenAIRealtimeStreamList = [
        {
            "type": "response.done",
            "response": {
                "usage": {
                    "input_tokens": 283,
                    "output_tokens": 0,
                    "total_tokens": 283,
                    "input_token_details": {
                        "text_tokens": 116,
                        "audio_tokens": 167,
                        "cached_tokens": 192,
                        "cached_tokens_details": {"text_tokens": 64, "audio_tokens": 128},
                    },
                }
            },
        },
        {
            "type": "response.done",
            "response": {
                "usage": {
                    "input_tokens": 150,
                    "output_tokens": 0,
                    "total_tokens": 150,
                    "input_token_details": {
                        "text_tokens": 50,
                        "audio_tokens": 100,
                        "cached_tokens": 100,
                        "cached_tokens_details": {"audio_tokens": 100},
                    },
                }
            },
        },
    ]

    combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
        results=results,
    )

    assert combined.prompt_tokens_details is not None
    assert combined.prompt_tokens_details.cached_tokens == 292
    assert combined.prompt_tokens_details.cached_tokens_details is not None
    assert combined.prompt_tokens_details.cached_tokens_details.audio_tokens == 228
    assert combined.prompt_tokens_details.cached_tokens_details.text_tokens == 64
    assert combined.prompt_tokens_details.cached_tokens_details.image_tokens is None


@pytest.mark.parametrize("details_first", [True, False])
def test_realtime_combine_keeps_cached_split_when_only_one_usage_has_details(details_first: bool):
    with_details: Final = {
        "type": "response.done",
        "response": {
            "usage": {
                "input_tokens": 283,
                "output_tokens": 0,
                "total_tokens": 283,
                "input_token_details": {
                    "text_tokens": 116,
                    "audio_tokens": 167,
                    "cached_tokens": 192,
                    "cached_tokens_details": {"text_tokens": 64, "audio_tokens": 128},
                },
            }
        },
    }
    without_details: Final = {
        "type": "response.done",
        "response": {
            "usage": {
                "input_tokens": 150,
                "output_tokens": 0,
                "total_tokens": 150,
                "input_token_details": {"text_tokens": 50, "audio_tokens": 100, "cached_tokens": 100},
            }
        },
    }
    results: OpenAIRealtimeStreamList = (
        [with_details, without_details] if details_first else [without_details, with_details]
    )

    combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
        results=results,
    )

    assert combined.prompt_tokens_details is not None
    assert combined.prompt_tokens_details.cached_tokens == 292
    assert combined.prompt_tokens_details.cached_tokens_details == CachedTokensDetails(text_tokens=64, audio_tokens=128)


def test_usage_without_cached_tokens_details_omits_key():
    usage = Usage(
        prompt_tokens=10,
        completion_tokens=5,
        total_tokens=15,
        prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=10),
    )

    dumped = usage.prompt_tokens_details.model_dump()
    assert "cached_tokens_details" not in dumped
    assert "cached_tokens_details" not in usage.prompt_tokens_details.model_dump_json()


UNMAPPED_OCR_MODEL: Final = "azure_ai/some-unmapped-ocr-model-for-testing"
MAPPED_OCR_MODEL: Final = "mistral/mistral-ocr-4-0"


def _ocr_response(model: str, pages_processed: int, credits: float | None = None) -> OCRResponse:
    return OCRResponse(
        pages=[OCRPage(index=index, markdown=f"page {index}") for index in range(pages_processed)],
        model=model,
        usage_info=OCRUsageInfo(pages_processed=pages_processed, credits=credits),
    )


def _ocr_logging_obj(litellm_params: dict[str, object]) -> Logging:
    logging_obj: Final = Logging(
        model=UNMAPPED_OCR_MODEL,
        messages=[],
        stream=False,
        call_type="ocr",
        start_time=None,
        litellm_call_id="test-ocr-custom-pricing",
        function_id="1234",
    )
    logging_obj.update_environment_variables(litellm_params=litellm_params, optional_params={})
    return logging_obj


@pytest.mark.parametrize("pages_processed", [1, 3, 10])
def test_ocr_cost_uses_deployment_per_page_pricing_for_unmapped_model(pages_processed: int):
    from litellm.cost_calculator import ocr_cost

    assert UNMAPPED_OCR_MODEL not in litellm.model_cost
    cost, _ = ocr_cost(
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        response=_ocr_response(UNMAPPED_OCR_MODEL, pages_processed=pages_processed),
        model_info={"ocr_cost_per_page": 0.004},
    )
    assert cost == pytest.approx(0.004 * pages_processed)


def test_ocr_cost_uses_deployment_annotation_only_pricing_for_unmapped_model():
    from litellm.cost_calculator import ocr_cost

    assert UNMAPPED_OCR_MODEL not in litellm.model_cost
    response: Final = OCRResponse(
        pages=[OCRPage(index=index, markdown=f"page {index}") for index in range(3)],
        model=UNMAPPED_OCR_MODEL,
        usage_info=OCRUsageInfo(pages_processed=3, pages_processed_annotation=2),
    )
    cost, _ = ocr_cost(
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        response=response,
        model_info={"annotation_cost_per_page": 0.01},
    )
    assert cost == pytest.approx(0.01 * 2)


def test_ocr_cost_annotation_only_override_keeps_mapped_per_page_rate():
    from litellm.cost_calculator import ocr_cost

    map_price: Final = litellm.model_cost[MAPPED_OCR_MODEL]["ocr_cost_per_page"]
    response: Final = OCRResponse(
        pages=[OCRPage(index=index, markdown=f"page {index}") for index in range(3)],
        model=MAPPED_OCR_MODEL,
        usage_info=OCRUsageInfo(pages_processed=3, pages_processed_annotation=2),
    )
    cost, _ = ocr_cost(
        model=MAPPED_OCR_MODEL,
        custom_llm_provider="mistral",
        response=response,
        model_info={"annotation_cost_per_page": 0.01},
    )
    assert cost == pytest.approx(map_price * 3 + 0.01 * 2)


def test_ocr_cost_uses_deployment_per_credit_pricing_for_unmapped_model():
    from litellm.cost_calculator import ocr_cost

    cost, _ = ocr_cost(
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        response=_ocr_response(UNMAPPED_OCR_MODEL, pages_processed=2, credits=4),
        model_info={"ocr_cost_per_credit": 0.25},
    )
    assert cost == pytest.approx(0.25 * 4)


def test_ocr_cost_unmapped_model_without_deployment_pricing_bills_zero():
    from litellm.cost_calculator import ocr_cost

    cost, _ = ocr_cost(
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        response=_ocr_response(UNMAPPED_OCR_MODEL, pages_processed=5),
        model_info={"id": "some-deployment-id"},
    )
    assert cost == 0.0


@pytest.mark.usefixtures("_local_model_cost_map")
def test_ocr_cost_deployment_pricing_overrides_cost_map_for_mapped_model():
    from litellm.cost_calculator import ocr_cost

    map_price: Final = litellm.get_model_info(MAPPED_OCR_MODEL)["ocr_cost_per_page"]
    assert map_price is not None
    override_price: Final = map_price * 10

    cost, _ = ocr_cost(
        model=MAPPED_OCR_MODEL,
        custom_llm_provider="mistral",
        response=_ocr_response(MAPPED_OCR_MODEL, pages_processed=2),
        model_info={"ocr_cost_per_page": override_price},
    )
    assert cost == pytest.approx(override_price * 2)


@pytest.mark.usefixtures("_local_model_cost_map")
def test_ocr_cost_falls_through_to_cost_map_when_deployment_has_no_ocr_pricing():
    from litellm.cost_calculator import ocr_cost

    map_price: Final = litellm.get_model_info(MAPPED_OCR_MODEL)["ocr_cost_per_page"]
    assert map_price is not None

    cost, _ = ocr_cost(
        model=MAPPED_OCR_MODEL,
        custom_llm_provider="mistral",
        response=_ocr_response(MAPPED_OCR_MODEL, pages_processed=2),
        model_info={"id": "some-deployment-id"},
    )
    assert cost == pytest.approx(map_price * 2)


@pytest.mark.usefixtures("_local_model_cost_map")
def test_ocr_cost_ignores_deployment_credit_pricing_when_response_reports_no_credits():
    from litellm.cost_calculator import ocr_cost

    map_price: Final = litellm.get_model_info(MAPPED_OCR_MODEL)["ocr_cost_per_page"]
    assert map_price is not None

    cost, _ = ocr_cost(
        model=MAPPED_OCR_MODEL,
        custom_llm_provider="mistral",
        response=_ocr_response(MAPPED_OCR_MODEL, pages_processed=2),
        model_info={"ocr_cost_per_credit": 0.5},
    )
    assert cost == pytest.approx(map_price * 2)


@pytest.mark.parametrize("metadata_key", ["metadata", "litellm_metadata"])
def test_completion_cost_ocr_reads_deployment_pricing_from_logging_metadata(metadata_key: str):
    logging_obj = _ocr_logging_obj({metadata_key: {"model_info": {"ocr_cost_per_page": 0.004}}})

    cost = completion_cost(
        completion_response=_ocr_response(UNMAPPED_OCR_MODEL, pages_processed=3),
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        call_type="ocr",
        custom_pricing=True,
        litellm_logging_obj=logging_obj,
    )
    assert cost == pytest.approx(0.004 * 3)


def test_completion_cost_ocr_prefers_pricing_registered_under_router_model_id(monkeypatch: pytest.MonkeyPatch):
    deployment_id: Final = "ocr-deployment-priced-through-litellm-params"
    monkeypatch.setitem(
        litellm.model_cost, deployment_id, {"mode": "ocr", "litellm_provider": "azure_ai", "ocr_cost_per_page": 0.05}
    )
    logging_obj = _ocr_logging_obj({"metadata": {"model_info": {"mode": "ocr"}}})

    cost = completion_cost(
        completion_response=_ocr_response(UNMAPPED_OCR_MODEL, pages_processed=3),
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        call_type="ocr",
        custom_pricing=True,
        router_model_id=deployment_id,
        litellm_logging_obj=logging_obj,
    )
    assert cost == pytest.approx(0.05 * 3)


def test_completion_cost_ocr_bills_request_level_pricing_for_direct_sdk_call():
    logging_obj = _ocr_logging_obj({"ocr_cost_per_page": 0.05})

    cost = completion_cost(
        completion_response=_ocr_response(UNMAPPED_OCR_MODEL, pages_processed=3),
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        call_type="ocr",
        custom_pricing=True,
        litellm_logging_obj=logging_obj,
    )
    assert cost == pytest.approx(0.05 * 3)


def test_completion_cost_ocr_request_level_pricing_fills_in_deployment_model_info_without_ocr_pricing():
    logging_obj = _ocr_logging_obj({"ocr_cost_per_page": 0.05, "metadata": {"model_info": {"mode": "ocr"}}})

    cost = completion_cost(
        completion_response=_ocr_response(UNMAPPED_OCR_MODEL, pages_processed=3),
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        call_type="ocr",
        custom_pricing=True,
        litellm_logging_obj=logging_obj,
    )
    assert cost == pytest.approx(0.05 * 3)


def test_completion_cost_ocr_ignores_deployment_pricing_without_custom_pricing_flag():
    logging_obj = _ocr_logging_obj({"metadata": {"model_info": {"ocr_cost_per_page": 0.004}}})

    cost = completion_cost(
        completion_response=_ocr_response(UNMAPPED_OCR_MODEL, pages_processed=3),
        model=UNMAPPED_OCR_MODEL,
        custom_llm_provider="azure_ai",
        call_type="ocr",
        custom_pricing=False,
        litellm_logging_obj=logging_obj,
    )
    assert cost == 0.0


def test_completion_cost_prices_responses_websocket_turns_per_service_tier():
    """Issue #41299: a session mixing default and priority turns must price each turn at
    its own returned service_tier, not the summed usage at a single tier."""
    events = [
        {"type": "response.created", "response": {}},
        {
            "type": "response.completed",
            "response": {
                "service_tier": "default",
                "usage": {"input_tokens": 100, "output_tokens": 40, "total_tokens": 140},
            },
        },
        {"type": "rate_limits.updated", "rate_limits": {}},
        {
            "type": "response.completed",
            "response": {
                "service_tier": "priority",
                "usage": {"input_tokens": 60, "output_tokens": 10, "total_tokens": 70},
            },
        },
        {"type": "response.failed", "response": {"usage": None}},
    ]

    partition = ResponsesWebSocketTokenUsageProcessor.partition_results_by_service_tier(events)
    assert tuple(partition.keys()) == ("default", "priority")
    assert len(partition["default"]) == 1
    assert len(partition["priority"]) == 1

    logging_obj = Logging(
        model="gpt-5.4",
        messages=[],
        stream=False,
        call_type=CallTypes.aresponses_websocket.value,
        start_time=time.time(),
        litellm_call_id="responses-ws-tier-test",
        function_id="responses-ws-tier-test",
    )
    normalized = logging_obj.normalize_logging_result(result=events)
    assert isinstance(normalized, LiteLLMRealtimeStreamLoggingObject)
    assert normalized.service_tier is None

    def _http_cost(input_tokens: int, output_tokens: int, service_tier: str) -> float:
        return completion_cost(
            completion_response=ResponsesAPIResponse(
                id=f"resp-{service_tier}",
                created_at=1700000000,
                output=[],
                service_tier=service_tier,
                usage=ResponseAPIUsage(
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    total_tokens=input_tokens + output_tokens,
                ),
            ),
            model="gpt-5.4",
            call_type=CallTypes.aresponses.value,
            custom_llm_provider="openai",
        )

    ws_cost = completion_cost(
        completion_response=normalized,
        model="gpt-5.4",
        call_type=CallTypes.aresponses_websocket.value,
        custom_llm_provider="openai",
    )

    assert ws_cost == pytest.approx(_http_cost(100, 40, "default") + _http_cost(60, 10, "priority"))
    assert ws_cost != pytest.approx(_http_cost(160, 50, "default"))
    assert ws_cost != pytest.approx(_http_cost(160, 50, "priority"))


_TIERED_BATCH_MODEL: Final = "lit-tiered-batch-model"
_FLAT_CACHE_BATCH_MODEL: Final = "lit-tiered-batch-model-without-cache-batch-rates"
_TIERED_BATCH_ENTRY: Final = MappingProxyType(
    {
        "litellm_provider": "openai",
        "mode": "chat",
        "input_cost_per_token": 2e-6,
        "output_cost_per_token": 8e-6,
        "cache_read_input_token_cost": 2e-7,
        "cache_creation_input_token_cost": 2.5e-6,
        "input_cost_per_token_above_272k_tokens": 4e-6,
        "output_cost_per_token_above_272k_tokens": 1.2e-5,
        "input_cost_per_token_batches": 1e-6,
        "output_cost_per_token_batches": 4e-6,
        "cache_read_input_token_cost_batches": 1e-7,
        "cache_creation_input_token_cost_batches": 1.25e-6,
        "input_cost_per_token_above_272k_tokens_batches": 3e-6,
        "output_cost_per_token_above_272k_tokens_batches": 7e-6,
        "cache_read_input_token_cost_above_272k_tokens_batches": 3e-7,
        "cache_creation_input_token_cost_above_272k_tokens_batches": 3.75e-6,
    }
)
_BATCH_RATE_PREFIXES: Final = (
    "input_cost_per_token",
    "output_cost_per_token",
    "cache_read_input_token_cost",
    "cache_creation_input_token_cost",
)


@pytest.fixture
def _tiered_batch_models(_local_model_cost_map: None) -> None:
    litellm.register_model(
        model_cost={
            _TIERED_BATCH_MODEL: {**_TIERED_BATCH_ENTRY},
            _FLAT_CACHE_BATCH_MODEL: {
                key: rate
                for key, rate in _TIERED_BATCH_ENTRY.items()
                if not (key.startswith("cache_") and key.endswith("_batches"))
            },
        },
        persist_across_reloads=False,
    )


def test_batch_cost_calculator_bills_the_long_context_batch_tier_above_272k(_tiered_batch_models: None) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    usage: Final = Usage(prompt_tokens=300_035, completion_tokens=64, total_tokens=300_099)

    prompt_cost, completion_cost_value = batch_cost_calculator(
        usage=usage, model=_TIERED_BATCH_MODEL, custom_llm_provider="openai"
    )

    assert prompt_cost == pytest.approx(300_035 * 3e-6)
    assert completion_cost_value == pytest.approx(64 * 7e-6)


@pytest.mark.parametrize("prompt_tokens", [272_000, 1_000])
def test_batch_cost_calculator_bills_the_flat_batch_rate_at_or_below_272k(
    _tiered_batch_models: None, prompt_tokens: int
) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    usage: Final = Usage(prompt_tokens=prompt_tokens, completion_tokens=64, total_tokens=prompt_tokens + 64)

    prompt_cost, completion_cost_value = batch_cost_calculator(
        usage=usage, model=_TIERED_BATCH_MODEL, custom_llm_provider="openai"
    )

    assert prompt_cost == pytest.approx(prompt_tokens * 1e-6)
    assert completion_cost_value == pytest.approx(64 * 4e-6)


def test_get_model_info_exposes_every_registered_batch_rate(_tiered_batch_models: None) -> None:
    info: Final = litellm.get_model_info(_TIERED_BATCH_MODEL, custom_llm_provider="openai")
    batch_keys: Final = tuple(key for key in _TIERED_BATCH_ENTRY if key.endswith("_batches"))

    assert len(batch_keys) == 8
    assert {key: info[key] for key in batch_keys} == {key: _TIERED_BATCH_ENTRY[key] for key in batch_keys}


def test_regular_path_never_bills_the_batch_tier_keys(_local_model_cost_map, monkeypatch):
    monkeypatch.setitem(
        litellm.model_cost,
        "lit-batch-tier-guard",
        {
            "litellm_provider": "openai",
            "mode": "chat",
            "input_cost_per_token": 2e-6,
            "output_cost_per_token": 8e-6,
            "input_cost_per_token_batches": 1e-6,
            "output_cost_per_token_batches": 4e-6,
            "input_cost_per_token_above_272k_tokens_batches": 5e-6,
            "output_cost_per_token_above_272k_tokens_batches": 9e-6,
        },
    )

    prompt_cost, completion_cost = litellm.cost_per_token(
        model="lit-batch-tier-guard", custom_llm_provider="openai", prompt_tokens=300_035, completion_tokens=64
    )

    assert prompt_cost == pytest.approx(300_035 * 2e-6)
    assert completion_cost == pytest.approx(64 * 8e-6)


@pytest.mark.parametrize("prefix", _BATCH_RATE_PREFIXES)
def test_every_openai_entry_with_a_long_context_rate_and_a_batch_rate_declares_the_batch_tier(
    _local_model_cost_map: None, prefix: str
) -> None:
    undeclared: Final = [
        name
        for name, entry in litellm.model_cost.items()
        if isinstance(entry, dict)
        and entry.get("litellm_provider") == "openai"
        and entry.get(f"{prefix}_above_272k_tokens") is not None
        and entry.get(f"{prefix}_batches") is not None
        and entry.get(f"{prefix}_above_272k_tokens_batches") is None
    ]

    assert undeclared == []


@pytest.mark.parametrize("prefix", _BATCH_RATE_PREFIXES)
def test_every_xai_entry_with_a_long_context_rate_and_a_batch_rate_declares_the_batch_tier(
    _local_model_cost_map: None, prefix: str
) -> None:
    undeclared: Final = [
        name
        for name, entry in litellm.model_cost.items()
        if isinstance(entry, dict)
        and entry.get("litellm_provider") == "xai"
        and entry.get(f"{prefix}_above_200k_tokens") is not None
        and entry.get(f"{prefix}_batches") is not None
        and entry.get(f"{prefix}_above_200k_tokens_batches") is None
    ]

    assert undeclared == []


_XAI_TIERED_BATCH_MODEL: Final = "xai/grok-4.3"


def test_xai_batch_tier_discounts_the_long_context_rate_like_the_flat_batch_rate(_local_model_cost_map: None) -> None:
    info: Final = litellm.get_model_info(_XAI_TIERED_BATCH_MODEL, custom_llm_provider="xai")
    flat_discount: Final = info["input_cost_per_token_batches"] / info["input_cost_per_token"]

    for prefix in ("input_cost_per_token", "output_cost_per_token", "cache_read_input_token_cost"):
        tier_discount = info[f"{prefix}_above_200k_tokens_batches"] / info[f"{prefix}_above_200k_tokens"]
        assert tier_discount == pytest.approx(flat_discount)
        assert info[f"{prefix}_above_200k_tokens_batches"] < info[f"{prefix}_above_200k_tokens"]


@pytest.mark.parametrize(("prompt_tokens", "tier"), [(200_000, "_above_200k_tokens_batches"), (199_999, "_batches")])
def test_xai_batch_cost_calculator_bills_the_200k_batch_tier_inclusively(
    _local_model_cost_map: None, prompt_tokens: int, tier: str
) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    info: Final = litellm.get_model_info(_XAI_TIERED_BATCH_MODEL, custom_llm_provider="xai")
    usage: Final = Usage(prompt_tokens=prompt_tokens, completion_tokens=64, total_tokens=prompt_tokens + 64)

    prompt_cost, completion_cost_value = batch_cost_calculator(
        usage=usage, model=_XAI_TIERED_BATCH_MODEL, custom_llm_provider="xai"
    )

    assert prompt_cost == pytest.approx(prompt_tokens * info[f"input_cost_per_token{tier}"])
    assert completion_cost_value == pytest.approx(64 * info[f"output_cost_per_token{tier}"])


def test_batch_cost_calculator_ignores_malformed_batch_tier_keys():
    from litellm.cost_calculator import batch_cost_calculator

    usage = Usage(prompt_tokens=300_035, completion_tokens=64, total_tokens=300_099)
    model_info = cast(
        ModelInfo,
        {
            "input_cost_per_token": 2e-6,
            "output_cost_per_token": 8e-6,
            "input_cost_per_token_batches": 1e-6,
            "output_cost_per_token_batches": 4e-6,
            "input_cost_per_token_above_272k_tokens_batches": 2e-6,
            "output_cost_per_token_above_272k_tokens_batches": 6e-6,
            "input_cost_per_token_above_lots_tokens_batches": 1.0,
        },
    )

    prompt_cost, completion_cost = batch_cost_calculator(
        usage=usage, model=_TIERED_BATCH_MODEL, custom_llm_provider="openai", model_info=model_info
    )

    assert prompt_cost == pytest.approx(300_035 * 2e-6)
    assert completion_cost == pytest.approx(64 * 6e-6)


def _cached_usage(prompt_tokens: int, cached_tokens: int, completion_tokens: int) -> Usage:
    return Usage(
        prompt_tokens=prompt_tokens,
        completion_tokens=completion_tokens,
        total_tokens=prompt_tokens + completion_tokens,
        prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens),
    )


def test_batch_cost_calculator_bills_cached_tokens_at_the_long_context_batch_cached_rate(
    _tiered_batch_models: None,
) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    prompt_cost, completion_cost_value = batch_cost_calculator(
        usage=_cached_usage(300_048, 300_045, 11), model=_TIERED_BATCH_MODEL, custom_llm_provider="openai"
    )

    assert prompt_cost == pytest.approx(3 * 3e-6 + 300_045 * 3e-7)
    assert completion_cost_value == pytest.approx(11 * 7e-6)


def test_batch_cost_calculator_bills_cached_tokens_at_the_flat_batch_cached_rate_at_or_below_272k(
    _tiered_batch_models: None,
) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    prompt_cost, _ = batch_cost_calculator(
        usage=_cached_usage(1_000, 900, 4), model=_TIERED_BATCH_MODEL, custom_llm_provider="openai"
    )

    assert prompt_cost == pytest.approx(100 * 1e-6 + 900 * 1e-7)


def test_batch_cost_calculator_bills_cached_tokens_at_the_batch_input_rate_without_a_cached_batch_rate(
    _tiered_batch_models: None,
) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    prompt_cost, _ = batch_cost_calculator(
        usage=_cached_usage(300_048, 300_045, 11), model=_FLAT_CACHE_BATCH_MODEL, custom_llm_provider="openai"
    )

    assert prompt_cost == pytest.approx(300_048 * 3e-6)


def _cache_write_usage(prompt_tokens: int, cache_write_tokens: int, completion_tokens: int) -> Usage:
    return Usage(
        prompt_tokens=prompt_tokens,
        completion_tokens=completion_tokens,
        total_tokens=prompt_tokens + completion_tokens,
        prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=0, cache_write_tokens=cache_write_tokens),
    )


def test_batch_cost_calculator_bills_cache_write_tokens_at_the_long_context_batch_cache_write_rate(
    _tiered_batch_models: None,
) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    prompt_cost, completion_cost_value = batch_cost_calculator(
        usage=_cache_write_usage(300_048, 300_045, 4), model=_TIERED_BATCH_MODEL, custom_llm_provider="openai"
    )

    assert prompt_cost == pytest.approx(3 * 3e-6 + 300_045 * 3.75e-6)
    assert completion_cost_value == pytest.approx(4 * 7e-6)


def test_batch_cost_calculator_bills_cache_write_tokens_at_the_flat_batch_cache_write_rate_at_or_below_272k(
    _tiered_batch_models: None,
) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    prompt_cost, _ = batch_cost_calculator(
        usage=_cache_write_usage(1_000, 900, 4), model=_TIERED_BATCH_MODEL, custom_llm_provider="openai"
    )

    assert prompt_cost == pytest.approx(100 * 1e-6 + 900 * 1.25e-6)


def test_batch_cost_calculator_bills_cache_write_tokens_at_the_batch_input_rate_without_a_cache_write_batch_rate(
    _tiered_batch_models: None,
) -> None:
    from litellm.cost_calculator import batch_cost_calculator

    prompt_cost, _ = batch_cost_calculator(
        usage=_cache_write_usage(300_048, 300_045, 4), model=_FLAT_CACHE_BATCH_MODEL, custom_llm_provider="openai"
    )

    assert prompt_cost == pytest.approx(300_048 * 3e-6)


def test_batch_cost_calculator_prices_modalities_and_cached_tokens_together_in_the_crossed_tier() -> None:
    from litellm.cost_calculator import batch_cost_calculator

    model_info: Final = cast(
        ModelInfo,
        {
            "input_cost_per_token_batches": 1e-6,
            "input_cost_per_token_above_272k_tokens_batches": 3e-6,
            "input_cost_per_audio_token_batches": 5e-6,
            "cache_read_input_token_cost_batches": 1e-7,
            "cache_read_input_token_cost_above_272k_tokens_batches": 3e-7,
        },
    )
    usage: Final = Usage(
        prompt_tokens=300_000,
        completion_tokens=0,
        total_tokens=300_000,
        prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=64, image_tokens=10, cached_tokens=1_000),
    )

    prompt_cost, _ = batch_cost_calculator(
        usage=usage, model=_TIERED_BATCH_MODEL, custom_llm_provider="openai", model_info=model_info
    )

    assert prompt_cost == pytest.approx(298_926 * 3e-6 + 64 * 5e-6 + 10 * 3e-6 + 1_000 * 3e-7)


QWEN3_NEXT_REGIONS: Final = ("ap-northeast-1", "ap-south-1", "ap-southeast-2", "eu-west-1", "eu-west-2", "sa-east-1")


@pytest.mark.parametrize("region", QWEN3_NEXT_REGIONS)
def test_cost_per_token_bedrock_qwen3_next_uses_regional_entry_not_us_rate(
    monkeypatch: pytest.MonkeyPatch, region: str
) -> None:
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    regional: Final = litellm.model_cost[f"bedrock/{region}/qwen.qwen3-next-80b-a3b"]
    us: Final = litellm.model_cost["qwen.qwen3-next-80b-a3b"]
    assert regional["input_cost_per_token"] != us["input_cost_per_token"]
    assert regional["output_cost_per_token"] != us["output_cost_per_token"]

    prompt_tokens, completion_tokens = 1000, 500
    prompt_usd, completion_usd = cost_per_token(
        model=f"bedrock/{region}/qwen.qwen3-next-80b-a3b",
        prompt_tokens=prompt_tokens,
        completion_tokens=completion_tokens,
        custom_llm_provider="bedrock",
    )

    assert prompt_usd == pytest.approx(prompt_tokens * regional["input_cost_per_token"])
    assert completion_usd == pytest.approx(completion_tokens * regional["output_cost_per_token"])


def test_cost_per_token_bedrock_nemotron_super_3_uses_eu_west_2_entry_not_us_rate(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    regional_key: Final = "bedrock/eu-west-2/nvidia.nemotron-super-3-120b"
    regional: Final = litellm.model_cost[regional_key]
    us: Final = litellm.model_cost["nvidia.nemotron-super-3-120b"]
    assert regional["input_cost_per_token"] != us["input_cost_per_token"]
    assert regional["output_cost_per_token"] != us["output_cost_per_token"]

    prompt_tokens, completion_tokens = 1000, 500
    prompt_usd, completion_usd = cost_per_token(
        model=regional_key,
        prompt_tokens=prompt_tokens,
        completion_tokens=completion_tokens,
        custom_llm_provider="bedrock",
    )

    assert prompt_usd == pytest.approx(prompt_tokens * regional["input_cost_per_token"])
    assert completion_usd == pytest.approx(completion_tokens * regional["output_cost_per_token"])


GPT_REALTIME_2_FAMILY: Final = (
    "azure/gpt-realtime-2.1",
    "azure/gpt-realtime-2.1-mini",
    "gpt-realtime-2",
    "gpt-realtime-2.1",
    "gpt-realtime-2.1-mini",
)


def test_gpt_realtime_2_family_prices_audio_cache_writes_and_reads_alike(_local_model_cost_map: None) -> None:
    audio_cache_rates: Final = {
        model: (
            litellm.model_cost[model].get("cache_read_input_audio_token_cost"),
            litellm.model_cost[model].get("cache_creation_input_audio_token_cost"),
        )
        for model in GPT_REALTIME_2_FAMILY
    }

    # Azure publishes one cached-audio meter per gpt-realtime-2 deployment,
    # https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/, checked 2026-09-23
    assert all(read is not None and write == read for read, write in audio_cache_rates.values()), audio_cache_rates
    assert len(audio_cache_rates) == len(GPT_REALTIME_2_FAMILY)


GEMINI_LIVE_NATIVE_AUDIO_CASES: Final = (
    ("gemini-live-2.5-flash-native-audio", "vertex_ai"),
    ("gemini-live-2.5-flash-preview-native-audio-09-2025", "vertex_ai"),
    ("gemini/gemini-live-2.5-flash-preview-native-audio-09-2025", "gemini"),
)


@pytest.mark.parametrize(("model", "provider"), GEMINI_LIVE_NATIVE_AUDIO_CASES)
def test_gemini_live_native_audio_carries_no_cached_input_rate(
    _local_model_cost_map: None, model: str, provider: str
) -> None:
    # the Vertex pricing table prints N/A for cached input on every Live row,
    # https://cloud.google.com/vertex-ai/generative-ai/pricing, checked 2026-09-23
    assert litellm.get_model_info(model, custom_llm_provider=provider)["cache_read_input_token_cost"] is None

    prompt_usd, _ = cost_per_token(
        model=model,
        prompt_tokens=101_000,
        completion_tokens=0,
        custom_llm_provider=provider,
        usage_object=Usage(
            prompt_tokens=101_000,
            completion_tokens=0,
            prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=100_000),
        ),
    )
    fresh_usd, _ = cost_per_token(
        model=model,
        prompt_tokens=101_000,
        completion_tokens=0,
        custom_llm_provider=provider,
        usage_object=Usage(prompt_tokens=101_000, completion_tokens=0),
    )

    assert prompt_usd == pytest.approx(fresh_usd), (
        "with no cached rate the cached tokens bill at the input rate, so a phantom discount cannot appear"
    )
    assert prompt_usd > 0


@pytest.mark.parametrize(("model", "provider"), GEMINI_LIVE_NATIVE_AUDIO_CASES)
def test_gemini_live_native_audio_declares_prompt_caching_unsupported(
    _local_model_cost_map: None, model: str, provider: str
) -> None:
    # the Vertex context-caching supported-model lists contain no Live model while 2.5 Flash is listed,
    # https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-overview, checked 2026-09-23
    assert litellm.get_model_info(model, custom_llm_provider=provider)["supports_prompt_caching"] is False
    assert supports_prompt_caching(model=model, custom_llm_provider=provider) is False
    assert supports_prompt_caching(model="gemini-2.5-flash", custom_llm_provider="vertex_ai") is True, (
        "control: the helper swallows a lookup error into False, so without this a broken lookup reads as a pass"
    )


@pytest.mark.parametrize(
    "model",
    ["gemini-live-2.5-flash-native-audio", "vertex_ai/gemini-live-2.5-flash-native-audio"],
)
def test_gemini_live_native_audio_limits_and_capabilities_match_vendor_model_card(
    _local_model_cost_map: None, model: str
) -> None:
    info = litellm.get_model_info(model)

    # the Vertex model card for gemini-live-2.5-flash-native-audio publishes these limits and flags,
    # https://cloud.google.com/vertex-ai/generative-ai/docs/models, checked 2026-09-23
    assert info["max_input_tokens"] == 131072
    assert info["max_output_tokens"] == 65536
    assert info["max_tokens"] == 65536
    assert info["supports_response_schema"] is False
    assert info["supports_url_context"] is False
    assert info["supports_pdf_input"] is False


def test_realtime_honours_deployment_custom_pricing(monkeypatch: pytest.MonkeyPatch) -> None:
    """Regression: a deployment's pricing override never reached realtime costing.

    `model_info` overrides are registered under the deployment's own model_id, and
    only `_select_model_name_for_cost_calc` knows to look there. The realtime branch
    discarded that result and priced by the model the session reported, so a config
    that zeroes a realtime deployment was billed at the public rate anyway. Audio is
    the bulk of a voice call, so the gap was most of the cost.
    """
    from litellm.types.utils import CompletionTokensDetailsWrapper

    model = "gemini-3.1-flash-live-preview"
    deployment_key = "deployment-id-for-a-zero-rated-realtime-group"
    paid = litellm.model_cost[model]
    monkeypatch.setitem(
        litellm.model_cost,
        deployment_key,
        {
            **paid,
            "input_cost_per_token": 0.0,
            "output_cost_per_token": 0.0,
            "input_cost_per_audio_token": 0.0,
            "output_cost_per_audio_token": 0.0,
            "cache_read_input_token_cost": 0.0,
        },
    )

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": model}},
        {
            "type": "response.done",
            "response": {"usage": {"input_tokens": 10, "output_tokens": 200, "total_tokens": 210}},
        },
    ]
    usage = Usage(
        prompt_tokens=10,
        completion_tokens=200,
        total_tokens=210,
        prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=10, cached_tokens=0),
        completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=20, audio_tokens=180),
    )

    paid_cost = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=usage,
        custom_llm_provider="gemini",
        litellm_model_name=model,
    )
    expected_paid = (
        10 * paid["input_cost_per_token"]
        + 20 * paid["output_cost_per_token"]
        + 180 * paid["output_cost_per_audio_token"]
    )
    assert paid_cost == pytest.approx(expected_paid, rel=1e-9)
    assert paid_cost > 0

    zero_rated_cost = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=usage,
        custom_llm_provider="gemini",
        litellm_model_name=model,
        custom_pricing_model=deployment_key,
    )
    assert zero_rated_cost == 0.0


def test_realtime_honours_a_provider_prefixed_zero_rated_deployment(monkeypatch: pytest.MonkeyPatch) -> None:
    """Regression: the override arrived provider-prefixed and was read as pricing nothing.

    `_select_model_name_for_cost_calc` hands back `<provider>/<model_id>`, so the name reaching
    the pricing guard carries a prefix the raw cost-map lookups cannot strip. The rates resolved
    correctly through `get_model_info`, then the guard rejected them as undeclared and the session
    billed the public rates. A zero-rated deployment must stay at zero however its name arrives.
    """
    from litellm.types.utils import CompletionTokensDetailsWrapper

    model = "gemini-live-2.5-flash-native-audio"
    deployment_key = "deployment-id-for-a-prefixed-zero-rated-realtime-group"
    paid = litellm.model_cost[model]
    monkeypatch.setitem(
        litellm.model_cost,
        deployment_key,
        {
            **paid,
            "input_cost_per_token": 0.0,
            "output_cost_per_token": 0.0,
            "input_cost_per_audio_token": 0.0,
            "output_cost_per_audio_token": 0.0,
        },
    )

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": model}},
        {
            "type": "response.done",
            "response": {"usage": {"input_tokens": 219, "output_tokens": 81, "total_tokens": 300}},
        },
    ]
    usage = Usage(
        prompt_tokens=219,
        completion_tokens=81,
        total_tokens=300,
        prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=16, audio_tokens=203),
        completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=23, audio_tokens=58),
    )

    paid_cost = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=usage,
        custom_llm_provider="vertex_ai",
        litellm_model_name=model,
    )
    assert paid_cost > 0

    zero_rated_cost = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=usage,
        custom_llm_provider="vertex_ai",
        litellm_model_name=model,
        custom_pricing_model=f"vertex_ai/{deployment_key}",
    )
    assert zero_rated_cost == 0.0


def test_unpriced_deployment_entry_still_falls_through_to_the_session_model(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    """The guard's own purpose must survive: an entry that prices nothing is not an override.

    Deployments are auto-registered under their model_id with no rates at all, and those must
    keep billing at the session model's public rates rather than silently costing nothing.
    """
    from litellm.types.utils import CompletionTokensDetailsWrapper

    model = "gemini-live-2.5-flash-native-audio"
    deployment_key = "deployment-id-with-no-declared-rates"
    monkeypatch.setitem(
        litellm.model_cost,
        deployment_key,
        {key: value for key, value in litellm.model_cost[model].items() if "cost_per" not in key},
    )

    results: OpenAIRealtimeStreamList = [
        {"type": "session.created", "session": {"model": model}},
        {
            "type": "response.done",
            "response": {"usage": {"input_tokens": 219, "output_tokens": 81, "total_tokens": 300}},
        },
    ]
    usage = Usage(
        prompt_tokens=219,
        completion_tokens=81,
        total_tokens=300,
        prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=16, audio_tokens=203),
        completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=23, audio_tokens=58),
    )

    with_unpriced_override = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=usage,
        custom_llm_provider="vertex_ai",
        litellm_model_name=model,
        custom_pricing_model=f"vertex_ai/{deployment_key}",
    )
    without_override = handle_realtime_stream_cost_calculation(
        results=results,
        combined_usage_object=usage,
        custom_llm_provider="vertex_ai",
        litellm_model_name=model,
    )
    assert with_unpriced_override == pytest.approx(without_override, rel=1e-9)
    assert with_unpriced_override > 0


def test_realtime_audio_only_override_bills_audio_at_the_deployment_rate(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    """Regression: an audio-only pricing override was never selected as the pricing key.

    The deployment-selection guard recognised only text, per-second, per-query and
    tiered rates, so a deployment that priced just the audio meters was passed over
    and the session kept billing the public rates for the exact tokens it priced.
    """
    from litellm.types.utils import CompletionTokensDetailsWrapper

    model = "gemini-live-2.5-flash-native-audio"
    deployment_key = "deployment-id-for-an-audio-only-realtime-group"
    monkeypatch.setitem(
        litellm.model_cost,
        deployment_key,
        {
            "litellm_provider": "vertex_ai",
            "mode": "realtime",
            "input_cost_per_audio_token": 0.0,
            "output_cost_per_audio_token": 0.0,
        },
    )

    logging_object = LiteLLMRealtimeStreamLoggingObject(
        usage=Usage(
            prompt_tokens=203,
            completion_tokens=58,
            total_tokens=261,
            prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=203),
            completion_tokens_details=CompletionTokensDetailsWrapper(audio_tokens=58),
        ),
        results=[
            {"type": "session.created", "session": {"model": model}},
            {
                "type": "response.done",
                "response": {"usage": {"input_tokens": 203, "output_tokens": 58, "total_tokens": 261}},
            },
        ],
    )

    public_cost = completion_cost(
        completion_response=logging_object,
        model=model,
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="vertex_ai",
    )
    assert public_cost > 0

    overridden_cost = completion_cost(
        completion_response=logging_object,
        model=model,
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="vertex_ai",
        custom_pricing=True,
        router_model_id=deployment_key,
    )
    assert overridden_cost == pytest.approx(0.0)


def test_realtime_session_falls_back_to_base_model_pricing(monkeypatch: pytest.MonkeyPatch) -> None:
    """Regression: a priced base_model was discarded for realtime sessions.

    The resolved base model only reached the realtime cost path when custom pricing
    was on, so a session reporting an alias unmapped in the cost map recorded zero
    instead of the base model's published price.
    """
    from litellm.types.utils import CompletionTokensDetailsWrapper

    base_model = "gemini-live-2.5-flash-native-audio"
    logging_object = LiteLLMRealtimeStreamLoggingObject(
        usage=Usage(
            prompt_tokens=219,
            completion_tokens=81,
            total_tokens=300,
            prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=16, audio_tokens=203),
            completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=23, audio_tokens=58),
        ),
        results=[
            {
                "type": "session.created",
                "session": {"model": "my-voice-alias"},
            },
            {
                "type": "response.done",
                "response": {"usage": {"input_tokens": 219, "output_tokens": 81, "total_tokens": 300}},
            },
        ],
    )

    aliased_cost = completion_cost(
        completion_response=logging_object,
        model="my-voice-alias",
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="vertex_ai",
        base_model=base_model,
    )
    base_cost = completion_cost(
        completion_response=logging_object,
        model=base_model,
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="vertex_ai",
    )
    assert aliased_cost == pytest.approx(base_cost, rel=1e-9)
    assert aliased_cost > 0


def test_base_model_does_not_override_transcription_rates(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    base_model = "gpt-realtime-2"
    asr_model = "gpt-4o-transcribe"
    logging_object = LiteLLMRealtimeStreamLoggingObject(
        usage=Usage(),
        results=[
            {
                "type": "session.created",
                "session": {
                    "model": "my-voice-alias",
                    "audio": {"input": {"transcription": {"model": asr_model}}},
                },
            },
            {
                "type": "conversation.item.input_audio_transcription.completed",
                "usage": {
                    "type": "tokens",
                    "input_token_details": {"audio_tokens": 400, "text_tokens": 12},
                    "output_tokens": 30,
                },
            },
        ],
    )

    with_base_model = completion_cost(
        completion_response=logging_object,
        model="my-voice-alias",
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="openai",
        base_model=base_model,
    )
    asr_priced = completion_cost(
        completion_response=logging_object,
        model="my-voice-alias",
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="openai",
    )
    realtime_card = litellm.model_cost[base_model]
    billed_at_realtime = (
        400 * realtime_card["input_cost_per_audio_token"]
        + 12 * realtime_card["input_cost_per_token"]
        + 30 * realtime_card["output_cost_per_audio_token"]
    )
    assert billed_at_realtime != pytest.approx(asr_priced, rel=1e-9)
    assert with_base_model == pytest.approx(asr_priced, rel=1e-9)
    assert with_base_model > 0


def test_realtime_base_model_outranks_the_session_reported_model(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))

    from litellm.types.utils import CompletionTokensDetailsWrapper

    session_model = "gpt-realtime-mini"
    base_model = "gpt-realtime-2"

    def logging_object_for(session: str) -> LiteLLMRealtimeStreamLoggingObject:
        return LiteLLMRealtimeStreamLoggingObject(
            usage=Usage(
                prompt_tokens=120,
                completion_tokens=60,
                total_tokens=180,
                prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=20, audio_tokens=100),
                completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=10, audio_tokens=50),
            ),
            results=[
                {
                    "type": "session.created",
                    "session": {"model": session},
                },
                {
                    "type": "response.done",
                    "response": {"usage": {"input_tokens": 120, "output_tokens": 60, "total_tokens": 180}},
                },
            ],
        )

    with_base_model = completion_cost(
        completion_response=logging_object_for(session_model),
        model=session_model,
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="openai",
        base_model=base_model,
    )
    base_priced = completion_cost(
        completion_response=logging_object_for(base_model),
        model=base_model,
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="openai",
    )
    session_priced = completion_cost(
        completion_response=logging_object_for(session_model),
        model=session_model,
        call_type=CallTypes.arealtime.value,
        custom_llm_provider="openai",
    )
    assert base_priced != pytest.approx(session_priced, rel=1e-9)
    assert with_base_model == pytest.approx(base_priced, rel=1e-9)


def test_baseten_glm_5_3_fast_is_priced_from_registry(_local_model_cost_map: None) -> None:
    model: Final = "baseten/zai-org/GLM-5.3-Fast"
    prompt_tokens: Final = 1000
    completion_tokens: Final = 500

    prompt_usd, completion_usd = litellm.cost_per_token(
        model=model,
        prompt_tokens=prompt_tokens,
        completion_tokens=completion_tokens,
    )

    entry: Final = litellm.model_cost[model]
    assert prompt_usd == pytest.approx(prompt_tokens * entry["input_cost_per_token"])
    assert completion_usd == pytest.approx(completion_tokens * entry["output_cost_per_token"])
    assert prompt_usd > 0
    assert completion_usd > 0


def test_completion_cost_charges_explicit_per_token_rates_over_registered_ones(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    monkeypatch.setitem(
        litellm.model_cost,
        "smoke-priced-model",
        {"input_cost_per_token": 0.01, "output_cost_per_token": 0.02, "litellm_provider": "openai", "mode": "chat"},
    )
    response: Final = ModelResponse(
        model="smoke-priced-model",
        choices=[],
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    cost: Final = completion_cost(
        completion_response=response,
        model="smoke-priced-model",
        custom_llm_provider="openai",
        custom_cost_per_token={"input_cost_per_token": 0.001, "output_cost_per_token": 0.002},
    )

    assert cost == pytest.approx(100 * 0.001 + 50 * 0.002)


def test_completion_cost_is_zero_when_explicit_rates_are_zero(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setitem(
        litellm.model_cost,
        "smoke-priced-model",
        {"input_cost_per_token": 0.01, "output_cost_per_token": 0.02, "litellm_provider": "openai", "mode": "chat"},
    )
    response: Final = ModelResponse(
        model="smoke-priced-model",
        choices=[],
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )

    cost: Final = completion_cost(
        completion_response=response,
        model="smoke-priced-model",
        custom_llm_provider="openai",
        custom_cost_per_token={"input_cost_per_token": 0.0, "output_cost_per_token": 0.0},
    )

    assert cost == 0.0


@pytest.mark.parametrize(
    ("requested", "served", "expected"),
    [
        (None, "priority", "priority"),
        ("priority", "flex", "flex"),
        ("priority", "default", None),
        ("priority", "standard", None),
        ("priority", "auto", "priority"),
        ("priority", "scale", "priority"),
        ("priority", None, "priority"),
        ("auto", None, None),
        (None, "Priority", "priority"),
        ("flex", "on_demand", "flex"),
    ],
)
def test_resolve_billable_service_tier(requested: object, served: object, expected: str | None) -> None:
    from litellm.cost_calculator import _resolve_billable_service_tier

    assert _resolve_billable_service_tier(requested=requested, served=served) == expected


def _served_tier_cost_model(monkeypatch: pytest.MonkeyPatch) -> str:
    model: Final = "served-tier-cost-model"
    monkeypatch.setitem(
        litellm.model_cost,
        model,
        {
            "input_cost_per_token": 0.001,
            "output_cost_per_token": 0.002,
            "input_cost_per_token_priority": 0.01,
            "output_cost_per_token_priority": 0.02,
            "litellm_provider": "openai",
            "mode": "chat",
        },
    )
    return model


def test_completion_cost_bills_base_when_served_default_overrides_requested_priority(
    _local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch
) -> None:
    model: Final = _served_tier_cost_model(monkeypatch)
    response: Final = ModelResponse(
        model=model,
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )
    setattr(response, "service_tier", "default")

    cost: Final = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="openai",
        optional_params={"service_tier": "priority"},
    )

    assert cost == pytest.approx(100 * 0.001 + 50 * 0.002)


def test_completion_cost_bills_priority_when_served_tier_overrides_missing_request(
    _local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch
) -> None:
    model: Final = _served_tier_cost_model(monkeypatch)
    response: Final = ModelResponse(
        model=model,
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )
    setattr(response, "service_tier", "priority")

    cost: Final = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="openai",
    )

    assert cost == pytest.approx(100 * 0.01 + 50 * 0.02)


def test_completion_cost_bills_base_when_gemini_serves_on_demand(
    _local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch
) -> None:
    model: Final = _served_tier_cost_model(monkeypatch)
    response: Final = ModelResponse(
        model=model,
        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
    )
    response._hidden_params["provider_specific_fields"] = {"traffic_type": "ON_DEMAND"}

    cost: Final = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider="openai",
        optional_params={"service_tier": "priority"},
    )

    assert cost == pytest.approx(100 * 0.001 + 50 * 0.002)


def test_custom_pricing_as_completion_cost_param():
    from litellm import Choices, Message, ModelResponse
    from litellm.utils import Usage

    resp = ModelResponse(
        id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
        choices=[
            Choices(
                finish_reason=None,
                index=0,
                message=Message(
                    content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a",
                    role="assistant",
                ),
            )
        ],
        created=1700775391,
        model="ft:gpt-3.5-turbo:my-org:custom_suffix:id",
        object="chat.completion",
        system_fingerprint=None,
        usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
    )

    cost = litellm.completion_cost(
        completion_response=resp,
        custom_cost_per_token={
            "input_cost_per_token": 1000,
            "output_cost_per_token": 20,
        },
    )

    expected_cost = 1000 * 21 + 17 * 20

    assert round(cost, 5) == round(expected_cost, 5)


def test_cost_ft_gpt_35():
    try:
        # this tests if litellm.completion_cost can calculate cost for ft:gpt-3.5-turbo:my-org:custom_suffix:id
        # it needs to lookup  ft:gpt-3.5-turbo in the litellm model_cost map to get the correct cost
        from litellm import Choices, Message, ModelResponse
        from litellm.utils import Usage

        litellm.set_verbose = True

        resp = ModelResponse(
            id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
            choices=[
                Choices(
                    finish_reason=None,
                    index=0,
                    message=Message(
                        content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a",
                        role="assistant",
                    ),
                )
            ],
            created=1700775391,
            model="ft:gpt-3.5-turbo:my-org:custom_suffix:id",
            object="chat.completion",
            system_fingerprint=None,
            usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
        )

        cost = litellm.completion_cost(
            completion_response=resp, custom_llm_provider="openai"
        )
        print("\n Calculated Cost for ft:gpt-3.5", cost)
        input_cost = model_cost["ft:gpt-3.5-turbo"]["input_cost_per_token"]
        output_cost = model_cost["ft:gpt-3.5-turbo"]["output_cost_per_token"]
        print(input_cost, output_cost)
        expected_cost = (input_cost * resp.usage.prompt_tokens) + (
            output_cost * resp.usage.completion_tokens
        )
        print("\n Excpected cost", expected_cost)
        assert cost == expected_cost
    except Exception as e:
        print(f"Error: {e}")
        pytest.fail(
            f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}"
        )


def test_cost_azure_gpt_35():
    try:
        # this tests if litellm.completion_cost can calculate cost for azure/chatgpt-deployment-2 which maps to azure/gpt-3.5-turbo
        # for this test we check if passing `model` to completion_cost overrides the completion cost
        from litellm import Choices, Message, ModelResponse
        from litellm.utils import Usage

        resp = ModelResponse(
            id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
            choices=[
                Choices(
                    finish_reason=None,
                    index=0,
                    message=Message(
                        content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a",
                        role="assistant",
                    ),
                )
            ],
            model="azure/gpt-35-turbo",  # azure always has model written like this
            usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
        )

        cost = litellm.completion_cost(
            completion_response=resp, model="azure/chatgpt-deployment-2"
        )
        print("\n Calculated Cost for azure/gpt-3.5-turbo", cost)
        input_cost = model_cost["azure/gpt-35-turbo"]["input_cost_per_token"]
        output_cost = model_cost["azure/gpt-35-turbo"]["output_cost_per_token"]
        expected_cost = (input_cost * resp.usage.prompt_tokens) + (
            output_cost * resp.usage.completion_tokens
        )
        print("\n Excpected cost", expected_cost)
        assert cost == expected_cost
    except Exception as e:
        pytest.fail(f"Cost Calc failed for azure/gpt-3.5-turbo. {str(e)}")


def test_cost_bedrock_pricing_actual_calls():
    litellm.set_verbose = True
    model = "anthropic.claude-3-5-sonnet-20240620-v1:0"
    messages = [{"role": "user", "content": "Hey, how's it going?"}]
    response = litellm.completion(
        model=model, messages=messages, mock_response="hello cool one"
    )

    print("response", response)
    cost = litellm.completion_cost(
        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
        completion_response=response,
        messages=[{"role": "user", "content": "Hey, how's it going?"}],
    )
    assert cost > 0


def test_whisper_openai():
    litellm.set_verbose = True
    transcription = TranscriptionResponse(
        text="Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure."
    )

    setattr(transcription, "duration", 3)
    transcription._hidden_params = {
        "model": "whisper-1",
        "custom_llm_provider": "openai",
        "optional_params": {},
        "model_id": None,
    }
    _total_time_in_seconds = 3

    cost = litellm.completion_cost(model="whisper-1", completion_response=transcription)

    print(f"cost: {cost}")
    print(f"whisper dict: {litellm.model_cost['whisper-1']}")
    expected_cost = round(
        litellm.model_cost["whisper-1"]["output_cost_per_second"]
        * _total_time_in_seconds,
        5,
    )
    assert round(cost, 5) == round(expected_cost, 5)


def test_whisper_azure():
    litellm.set_verbose = True
    transcription = TranscriptionResponse(
        text="Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure."
    )
    transcription._hidden_params = {
        "model": "whisper-1",
        "custom_llm_provider": "azure",
        "optional_params": {},
        "model_id": None,
    }
    _total_time_in_seconds = 3
    setattr(transcription, "duration", _total_time_in_seconds)

    cost = litellm.completion_cost(
        model="azure/azure-whisper", completion_response=transcription
    )

    print(f"cost: {cost}")
    print(f"whisper dict: {litellm.model_cost['whisper-1']}")
    expected_cost = round(
        litellm.model_cost["whisper-1"]["output_cost_per_second"]
        * _total_time_in_seconds,
        5,
    )
    assert round(cost, 5) == round(expected_cost, 5)


def test_gpt_image_2_azure_cost_tracking():
    azure_image_generation_response: Final = {
        "created": 1758585600,
        "data": [{"b64_json": "iVBORw0KGgo=", "revised_prompt": None, "url": None}],
        "output_format": "png",
        "quality": "low",
        "size": "1024x1024",
        "usage": {
            "input_tokens": 12,
            "input_tokens_details": {"image_tokens": 0, "text_tokens": 12},
            "output_tokens": 196,
            "output_tokens_details": {"image_tokens": 196, "text_tokens": 0},
            "total_tokens": 208,
        },
    }
    response: Final = convert_to_model_response_object(
        response_object=azure_image_generation_response,
        model_response_object=litellm.ImageResponse(),
        response_type="image_generation",
        hidden_params={"model": "gpt-image-2", "custom_llm_provider": "azure"},
    )

    cost: Final = litellm.completion_cost(
        completion_response=response,
        model="azure/my-gpt-image-2-deployment",
        custom_llm_provider="azure",
        base_model="gpt-image-2",
        call_type="image_generation",
    )

    pricing: Final = litellm.model_cost["azure/gpt-image-2"]
    expected_cost: Final = pricing["input_cost_per_token"] * 12 + pricing["output_cost_per_image_token"] * 196
    assert round(cost, 8) == round(expected_cost, 8)


def test_replicate_llama3_cost_tracking():
    litellm.set_verbose = True
    model = "replicate/meta/meta-llama-3-8b-instruct"
    litellm.register_model(
        {
            "replicate/meta/meta-llama-3-8b-instruct": {
                "input_cost_per_token": 0.00000005,
                "output_cost_per_token": 0.00000025,
                "litellm_provider": "replicate",
            }
        }
    )
    response = litellm.ModelResponse(
        id="chatcmpl-cad7282f-7f68-41e7-a5ab-9eb33ae301dc",
        choices=[
            litellm.utils.Choices(
                finish_reason="stop",
                index=0,
                message=litellm.utils.Message(
                    content="I'm doing well, thanks for asking! I'm here to help you with any questions or tasks you may have. How can I assist you today?",
                    role="assistant",
                ),
            )
        ],
        created=1714401369,
        model="replicate/meta/meta-llama-3-8b-instruct",
        object="chat.completion",
        system_fingerprint=None,
        usage=litellm.utils.Usage(
            prompt_tokens=48, completion_tokens=31, total_tokens=79
        ),
    )
    cost = litellm.completion_cost(
        completion_response=response,
        messages=[{"role": "user", "content": "Hey, how's it going?"}],
    )

    print(f"cost: {cost}")
    cost = round(cost, 5)
    expected_cost = round(
        litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"][
            "input_cost_per_token"
        ]
        * 48
        + litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"][
            "output_cost_per_token"
        ]
        * 31,
        5,
    )
    assert cost == expected_cost


@pytest.mark.parametrize("is_streaming", [True, False])  #
def test_groq_response_cost_tracking(is_streaming):
    from litellm.utils import (
        CallTypes,
        Choices,
        Message,
        ModelResponse,
        Usage,
    )

    response = ModelResponse(
        id="chatcmpl-876cce24-e520-4cf8-8649-562a9be11c02",
        choices=[
            Choices(
                finish_reason="stop",
                index=0,
                message=Message(
                    content="Hi! I'm an AI, so I don't have emotions or feelings like humans do, but I'm functioning properly and ready to help with any questions or topics you'd like to discuss! How can I assist you today?",
                    role="assistant",
                ),
            )
        ],
        created=1717519830,
        model="llama3-70b-8192",
        object="chat.completion",
        system_fingerprint="fp_c1a4bcec29",
        usage=Usage(completion_tokens=46, prompt_tokens=17, total_tokens=63),
    )
    response._hidden_params["custom_llm_provider"] = "groq"
    print(response)

    response_cost = litellm.response_cost_calculator(
        response_object=response,
        model="groq/openai/gpt-oss-120b",
        custom_llm_provider="groq",
        call_type=CallTypes.acompletion.value,
        optional_params={},
    )

    assert isinstance(response_cost, float)
    assert response_cost > 0.0

    print(f"response_cost: {response_cost}")


def test_together_ai_qwen_completion_cost():
    input_kwargs = {
        "completion_response": litellm.ModelResponse(
            **{
                "id": "890db0c33c4ef94b-SJC",
                "choices": [
                    {
                        "finish_reason": "eos",
                        "index": 0,
                        "message": {
                            "content": "I am Qwen, a large language model created by Alibaba Cloud.",
                            "role": "assistant",
                        },
                    }
                ],
                "created": 1717900130,
                "model": "together_ai/qwen/Qwen2-72B-Instruct",
                "object": "chat.completion",
                "system_fingerprint": None,
                "usage": {
                    "completion_tokens": 15,
                    "prompt_tokens": 23,
                    "total_tokens": 38,
                },
            }
        ),
        "model": "qwen/Qwen2-72B-Instruct",
        "prompt": "",
        "messages": [],
        "completion": "",
        "total_time": 0.0,
        "call_type": "completion",
        "custom_llm_provider": "together_ai",
        "region_name": None,
        "size": None,
        "quality": None,
        "n": None,
        "custom_cost_per_token": None,
        "custom_cost_per_second": None,
    }

    response = litellm.cost_calculator.get_model_params_and_category(
        model_name="qwen/Qwen2-72B-Instruct", call_type=CallTypes.completion
    )

    assert response == "together-ai-41.1b-80b"


@pytest.mark.parametrize("provider", ["gemini"])
def test_gemini_completion_cost(provider):
    """
    Check if cost correctly calculated for gemini models based on context window
    """
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")
    model_name = "gemini-3.8-flash"
    prompt_tokens = 128.0
    output_tokens = 228.0
    ## GET MODEL FROM LITELLM.MODEL_INFO
    model_info = litellm.get_model_info(model=model_name, custom_llm_provider=provider)

    ## EXPECTED COST
    input_cost = prompt_tokens * model_info["input_cost_per_token"]
    output_cost = output_tokens * model_info["output_cost_per_token"]

    ## CALCULATED COST
    calculated_input_cost, calculated_output_cost = cost_per_token(
        model=model_name,
        prompt_tokens=prompt_tokens,
        completion_tokens=output_tokens,
        custom_llm_provider=provider,
    )

    assert calculated_input_cost == input_cost
    assert calculated_output_cost == output_cost


def test_vertex_ai_completion_cost():
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    prompt_tokens = 100

    model_info = litellm.get_model_info(model="gemini-3.8-flash")

    print("\nExpected model info:\n{}\n\n".format(model_info))

    expected_input_cost = prompt_tokens * model_info["input_cost_per_token"]

    ## CALCULATED COST
    calculated_input_cost, calculated_output_cost = cost_per_token(
        model="gemini-3.8-flash",
        custom_llm_provider="vertex_ai",
        prompt_tokens=prompt_tokens,
        completion_tokens=0,
    )

    assert round(expected_input_cost, 6) == round(calculated_input_cost, 6)
    print("expected_input_cost: {}".format(expected_input_cost))
    print("calculated_input_cost: {}".format(calculated_input_cost))


def test_vertex_ai_medlm_completion_cost():
    """Test for medlm completion cost ."""

    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    model = "vertex_ai/medlm-medium"
    messages = [{"role": "user", "content": "Test MedLM completion cost."}]
    predictive_cost = completion_cost(
        model=model, messages=messages, custom_llm_provider="vertex_ai"
    )
    assert predictive_cost > 0

    model = "vertex_ai/medlm-large"
    messages = [{"role": "user", "content": "Test MedLM completion cost."}]
    predictive_cost = completion_cost(model=model, messages=messages)
    assert predictive_cost > 0


def test_vertex_ai_embedding_completion_cost(caplog):
    """
    Relevant issue - https://github.com/BerriAI/litellm/issues/4630
    """
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    text = "The quick brown fox jumps over the lazy dog."
    input_tokens = litellm.token_counter(
        model="vertex_ai/text-embedding-004", text=text
    )

    model_info = litellm.get_model_info(model="vertex_ai/text-embedding-004")

    print("\nExpected model info:\n{}\n\n".format(model_info))

    expected_input_cost = input_tokens * model_info["input_cost_per_token"]

    ## CALCULATED COST
    calculated_input_cost, calculated_output_cost = cost_per_token(
        model="text-embedding-004",
        custom_llm_provider="vertex_ai",
        prompt_tokens=input_tokens,
        call_type="aembedding",
    )

    assert round(expected_input_cost, 6) == round(calculated_input_cost, 6)
    print("expected_input_cost: {}".format(expected_input_cost))
    print("calculated_input_cost: {}".format(calculated_input_cost))

    captured_logs = [rec.message for rec in caplog.records]
    for item in captured_logs:
        print("\nitem:{}\n".format(item))
        if (
            "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured "
            in item
        ):
            raise Exception("Error log raised for calculating embedding cost")


@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_completion_cost_hidden_params(sync_mode):
    litellm.return_response_headers = True
    if sync_mode:
        response = litellm.completion(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": "Hey, how's it going?"}],
            mock_response="Hello world",
        )
    else:
        response = await litellm.acompletion(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": "Hey, how's it going?"}],
            mock_response="Hello world",
        )

    assert "response_cost" in response._hidden_params
    assert isinstance(response._hidden_params["response_cost"], float)


def test_vertex_ai_gemini_predict_cost():
    model = "gemini-3.8-flash"
    messages = [{"role": "user", "content": "Hey, hows it going???"}]
    predictive_cost = completion_cost(model=model, messages=messages)

    assert predictive_cost > 0


@pytest.mark.parametrize("usage", ["litellm_usage", "openai_usage"])
def test_vertex_ai_mistral_predict_cost(usage):
    from litellm.types.utils import Choices, Message, ModelResponse, Usage

    if usage == "litellm_usage":
        response_usage = Usage(prompt_tokens=32, completion_tokens=55, total_tokens=87)
    else:
        from openai.types.completion_usage import CompletionUsage

        response_usage = CompletionUsage(
            prompt_tokens=32, completion_tokens=55, total_tokens=87
        )
    response_object = ModelResponse(
        id="26c0ef045020429d9c5c9b078c01e564",
        choices=[
            Choices(
                finish_reason="stop",
                index=0,
                message=Message(
                    content="Hello! I'm Litellm Bot, your helpful assistant. While I can't provide real-time weather updates, I can help you find a reliable weather service or guide you on how to check the weather on your device. Would you like assistance with that?",
                    role="assistant",
                    tool_calls=None,
                    function_call=None,
                ),
            )
        ],
        created=1722124652,
        model="vertex_ai/mistral-large",
        object="chat.completion",
        system_fingerprint=None,
        usage=response_usage,
    )
    model = "mistral-large@2407"
    messages = [{"role": "user", "content": "Hey, hows it going???"}]
    custom_llm_provider = "vertex_ai"
    predictive_cost = completion_cost(
        completion_response=response_object,
        model=model,
        messages=messages,
        custom_llm_provider=custom_llm_provider,
    )

    assert predictive_cost > 0


@pytest.mark.parametrize(
    "model", ["openai/tts-1", "azure/tts-1", "openai/gpt-4o-mini-tts"]
)
def test_completion_cost_tts(model):
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    cost = completion_cost(
        model=model,
        prompt="the quick brown fox jumped over the lazy dogs",
        call_type="speech",
    )

    assert cost > 0


def test_completion_cost_anthropic():
    """
    model_name: claude-haiku-4-5
    litellm_params:
      model: anthropic/claude-haiku-4-5
      max_tokens: 4096
    """
    router = litellm.Router(
        model_list=[
            {
                "model_name": "claude-haiku-4-5",
                "litellm_params": {
                    "model": "anthropic/claude-haiku-4-5",
                    "max_tokens": 4096,
                },
            }
        ]
    )
    data = {
        "model": "claude-haiku-4-5",
        "prompt_tokens": 21,
        "completion_tokens": 20,
        "response_time_ms": 871.7040000000001,
        "custom_llm_provider": "anthropic",
        "region_name": None,
        "prompt_characters": 0,
        "completion_characters": 0,
        "custom_cost_per_token": None,
        "custom_cost_per_second": None,
        "call_type": "acompletion",
    }

    input_cost, output_cost = cost_per_token(**data)

    assert input_cost > 0
    assert output_cost > 0

    print(input_cost)
    print(output_cost)


@pytest.mark.parametrize(
    "model, custom_llm_provider",
    [
        ("claude-sonnet-4-6", "anthropic"),
        ("claude-haiku-4-5", "anthropic"),
    ],
)
def test_completion_cost_prompt_caching(model, custom_llm_provider):
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    from litellm.utils import Choices, Message, ModelResponse, Usage

    ## WRITE TO CACHE ## (MORE EXPENSIVE)
    response_1 = ModelResponse(
        id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
        choices=[
            Choices(
                finish_reason="length",
                index=0,
                message=Message(
                    content="Hello! I'm doing well, thank you for",
                    role="assistant",
                    tool_calls=None,
                    function_call=None,
                ),
            )
        ],
        created=1725036547,
        model=model,
        object="chat.completion",
        system_fingerprint=None,
        usage=Usage(
            completion_tokens=10,
            prompt_tokens=114,
            total_tokens=124,
            prompt_tokens_details=PromptTokensDetails(cached_tokens=0),
            cache_creation_input_tokens=100,
            cache_read_input_tokens=0,
        ),
    )

    cost_1 = completion_cost(model=model, completion_response=response_1)

    _model_info = litellm.get_model_info(
        model=model, custom_llm_provider=custom_llm_provider
    )
    expected_cost = (
        (
            response_1.usage.prompt_tokens
            - response_1.usage.prompt_tokens_details.cached_tokens
            - response_1.usage.prompt_tokens_details.cache_creation_tokens
        )
        * _model_info["input_cost_per_token"]
        + (response_1.usage.prompt_tokens_details.cached_tokens or 0)
        * _model_info["cache_read_input_token_cost"]
        + (response_1.usage.cache_creation_input_tokens or 0)
        * _model_info["cache_creation_input_token_cost"]
        + (response_1.usage.completion_tokens or 0)
        * _model_info["output_cost_per_token"]
    )  # Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing)

    assert round(expected_cost, 5) == round(cost_1, 5)

    print(f"expected_cost: {expected_cost}, cost_1: {cost_1}")

    ## READ FROM CACHE ## (LESS EXPENSIVE)
    response_2 = ModelResponse(
        id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
        choices=[
            Choices(
                finish_reason="length",
                index=0,
                message=Message(
                    content="Hello! I'm doing well, thank you for",
                    role="assistant",
                    tool_calls=None,
                    function_call=None,
                ),
            )
        ],
        created=1725036547,
        model=model,
        object="chat.completion",
        system_fingerprint=None,
        usage=Usage(
            completion_tokens=10,
            prompt_tokens=114,
            total_tokens=134,
            prompt_tokens_details=PromptTokensDetails(cached_tokens=100),
            cache_creation_input_tokens=0,
            cache_read_input_tokens=100,
        ),
    )

    cost_2 = completion_cost(model=model, completion_response=response_2)

    assert cost_1 > cost_2


@pytest.mark.parametrize(
    "model",
    [
        "databricks/databricks-bge-large-en",
        "databricks/databricks-gte-large-en",
    ],
)
def test_completion_cost_databricks_embedding(model, monkeypatch):
    """
    Test completion cost calculation for Databricks embedding models using mocked HTTP responses.
    """
    base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
    api_key = "dapimykey"
    monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
    monkeypatch.setenv("DATABRICKS_API_KEY", api_key)

    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    mock_response_data = {
        "object": "list",
        "model": model.split("/")[1],
        "data": [
            {
                "index": 0,
                "object": "embedding",
                "embedding": [
                    0.06768798828125,
                    -0.01291656494140625,
                    -0.0501708984375,
                    0.0245361328125,
                    -0.030364990234375,
                ],
            }
        ],
        "usage": {
            "prompt_tokens": 8,
            "total_tokens": 8,
            "completion_tokens": 0,
            "completion_tokens_details": None,
            "prompt_tokens_details": None,
        },
    }

    mock_response = MagicMock(spec=httpx.Response)
    mock_response.status_code = 200
    mock_response.json.return_value = mock_response_data

    sync_handler = HTTPHandler()

    with patch.object(HTTPHandler, "post", return_value=mock_response):
        resp = litellm.embedding(
            model=model, input=["hey, how's it going?"], client=sync_handler
        )

        print(resp)
        cost = completion_cost(completion_response=resp)
        assert cost == resp.usage.prompt_tokens * litellm.get_model_info(model)["input_cost_per_token"]


@pytest.mark.parametrize(
    "model, base_model",
    [
        ("fireworks_ai/llama-v3p1-70b-instruct", "fireworks-ai-above-16b"),
    ],
)
def test_get_model_params_fireworks_ai(model, base_model):
    pricing_model = get_base_model_for_pricing(model_name=model)
    assert base_model == pricing_model


def test_cost_openai_prompt_caching():
    from litellm import get_model_info
    from litellm.utils import Choices, Message, ModelResponse, Usage

    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    model = "gpt-4o-mini-2024-07-18"

    ## LLM API CALL ## (MORE EXPENSIVE)
    response_1 = ModelResponse(
        id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
        choices=[
            Choices(
                finish_reason="length",
                index=0,
                message=Message(
                    content="Hello! I'm doing well, thank you for",
                    role="assistant",
                    tool_calls=None,
                    function_call=None,
                ),
            )
        ],
        created=1725036547,
        model=model,
        object="chat.completion",
        system_fingerprint=None,
        usage=Usage(
            completion_tokens=10,
            prompt_tokens=14,
            total_tokens=24,
        ),
    )

    ## PROMPT CACHE HIT ## (LESS EXPENSIVE)
    response_2 = ModelResponse(
        id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
        choices=[
            Choices(
                finish_reason="length",
                index=0,
                message=Message(
                    content="Hello! I'm doing well, thank you for",
                    role="assistant",
                    tool_calls=None,
                    function_call=None,
                ),
            )
        ],
        created=1725036547,
        model=model,
        object="chat.completion",
        system_fingerprint=None,
        usage=Usage(
            completion_tokens=10,
            prompt_tokens=14,
            total_tokens=10,
            prompt_tokens_details=PromptTokensDetails(
                cached_tokens=14,
            ),
        ),
    )

    cost_1 = completion_cost(model=model, completion_response=response_1)
    cost_2 = completion_cost(model=model, completion_response=response_2)
    assert cost_1 > cost_2

    model_info = get_model_info(model=model, custom_llm_provider="openai")
    usage = response_2.usage

    _expected_cost2 = (
        (usage.prompt_tokens - usage.prompt_tokens_details.cached_tokens)
        * model_info["input_cost_per_token"]
        + usage.completion_tokens * model_info["output_cost_per_token"]
        + usage.prompt_tokens_details.cached_tokens
        * model_info["cache_read_input_token_cost"]
    )

    print("_expected_cost2", _expected_cost2)
    print("cost_2", cost_2)

    assert cost_2 == _expected_cost2


@pytest.mark.parametrize(
    "model",
    [
        "cohere/rerank-english-v3.0",
        "azure_ai/cohere-rerank-v3-english",
    ],
)
def test_completion_cost_azure_ai_rerank(model):
    from litellm import RerankResponse

    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    response = RerankResponse(
        id="b01dbf2e-63c8-4981-9e69-32241da559ed",
        results=[
            {
                "document": {
                    "id": "1",
                    "text": "Paris is the capital of France.",
                },
                "index": 0,
                "relevance_score": 0.990732,
            },
        ],
        meta={
            "billed_units": {
                "search_units": 1,
            }
        },
    )
    print("response", response)
    cost = completion_cost(
        model=model, completion_response=response, call_type="arerank"
    )
    assert cost > 0


def test_together_ai_embedding_completion_cost():
    from litellm.utils import EmbeddingResponse, Usage

    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")
    response = EmbeddingResponse(
        model="togethercomputer/m2-bert-80M-8k-retrieval",
        data=[
            {
                "embedding": [
                    -0.18039076,
                    0.11614138,
                    0.37174946,
                    0.27238843,
                    -0.21933095,
                    -0.15207036,
                    0.17764972,
                    -0.08700938,
                    -0.23863377,
                    -0.24203257,
                    0.20441775,
                    0.04630023,
                    -0.07832973,
                    -0.193581,
                    0.2009999,
                    -0.30106494,
                    0.21179546,
                    -0.23836501,
                    -0.14919636,
                    -0.045276586,
                    0.08645845,
                    -0.027714893,
                    -0.009854938,
                    0.25298217,
                    -0.1081501,
                    -0.2383125,
                    0.23080236,
                    0.011114239,
                    0.06954927,
                    -0.21081704,
                    0.06937218,
                    -0.16756944,
                    -0.2030545,
                    -0.19809915,
                    -0.031914014,
                    -0.15959585,
                    0.17361341,
                    0.30239972,
                    -0.09923253,
                    0.12680714,
                    -0.13018028,
                    0.1302273,
                    0.19179879,
                    0.17068875,
                    0.065124996,
                    -0.15515316,
                    0.08250379,
                    0.07309733,
                    -0.07283606,
                    0.21411736,
                    0.15457751,
                    -0.08725933,
                    0.07227311,
                    0.056812778,
                    -0.077683985,
                    0.06833304,
                    0.0328722,
                    0.2719641,
                    -0.06989647,
                    0.22805125,
                    0.14953858,
                    0.0792393,
                    0.07793462,
                    0.16176109,
                    -0.15616545,
                    -0.25149494,
                    -0.065352336,
                    -0.38410214,
                    -0.27288514,
                    0.13946335,
                    -0.21873806,
                    0.1365704,
                    0.11738016,
                    -0.1141173,
                    0.022973377,
                    -0.16935326,
                    0.026940947,
                    -0.09990286,
                    -0.05157219,
                    0.21006724,
                    0.15897459,
                    0.011987913,
                    0.02576497,
                    -0.11819022,
                    -0.09184997,
                    -0.31881434,
                    -0.17055357,
                    -0.09523704,
                    0.008458802,
                    -0.015483258,
                    0.038404867,
                    0.014673892,
                    -0.041162584,
                    0.002691519,
                    0.04601874,
                    0.059108324,
                    0.007177156,
                    0.066804245,
                    0.038554087,
                    -0.038720075,
                    -0.2145991,
                    -0.15713418,
                    -0.03712905,
                    -0.066650696,
                    0.04227769,
                    0.018708894,
                    -0.26332214,
                    0.0012769096,
                    -0.13878848,
                    -0.33141217,
                    0.118736655,
                    0.03026654,
                    0.1017467,
                    -0.08000539,
                    0.00092649367,
                    0.13062756,
                    -0.03785864,
                    -0.2038575,
                    0.07655428,
                    -0.24818295,
                    -0.0600955,
                    0.114760056,
                    0.027571939,
                    -0.047068622,
                    -0.19806816,
                    0.0774084,
                    -0.05213658,
                    -0.042000014,
                    0.051924672,
                    -0.14131106,
                    -0.2309609,
                    0.20305444,
                    0.0700591,
                    0.13863273,
                    -0.06145084,
                    -0.039423797,
                    -0.055951696,
                    0.04732105,
                    0.078736484,
                    0.2566198,
                    0.054494765,
                    0.017602794,
                    -0.107575715,
                    -0.017887019,
                    -0.26046592,
                    -0.077659994,
                    -0.08430523,
                    0.18806657,
                    -0.12292346,
                    0.06288608,
                    -0.106739804,
                    -0.06600645,
                    -0.14719339,
                    -0.05070389,
                    0.23234129,
                    -0.034023043,
                    0.056019265,
                    -0.03627352,
                    0.11740493,
                    0.060294818,
                    -0.21726903,
                    -0.09775424,
                    0.27007395,
                    0.28328258,
                    0.022495652,
                    0.13218465,
                    0.07199022,
                    -0.15933248,
                    0.02381037,
                    -0.08288268,
                    0.020621575,
                    0.17395815,
                    0.06978612,
                    0.18418784,
                    -0.12663148,
                    -0.21287888,
                    0.21239495,
                    0.10222956,
                    0.03952703,
                    -0.066957936,
                    -0.035802357,
                    0.03683884,
                    0.22524163,
                    -0.029355489,
                    -0.11534147,
                    -0.041979663,
                    -0.012147716,
                    -0.07279564,
                    0.17417553,
                    0.05546745,
                    -0.1773277,
                    -0.26984993,
                    0.31703642,
                    0.05958132,
                    -0.14933203,
                    -0.084655434,
                    0.074604444,
                    -0.077568695,
                    0.25167143,
                    -0.17753932,
                    -0.006415411,
                    0.068613894,
                    -0.0031754146,
                    -0.0039771493,
                    0.015294107,
                    0.11839045,
                    -0.04570732,
                    0.103238374,
                    -0.09678329,
                    -0.21713412,
                    0.047976546,
                    -0.14346297,
                    0.17429878,
                    -0.31257913,
                    0.15445377,
                    -0.10576352,
                    -0.16792995,
                    -0.17988597,
                    -0.14238739,
                    -0.088244036,
                    0.2760547,
                    0.088823885,
                    -0.08074319,
                    -0.028918687,
                    0.107819095,
                    0.12004892,
                    0.13343112,
                    -0.1332874,
                    -0.0946055,
                    -0.20433402,
                    0.17760132,
                    0.11774745,
                    0.16756779,
                    -0.0937686,
                    0.23887308,
                    0.27315456,
                    0.08657822,
                    0.027402503,
                    -0.06605757,
                    0.29859266,
                    -0.21552202,
                    0.026192812,
                    0.1328459,
                    0.13072926,
                    0.19236198,
                    0.01760772,
                    -0.042355467,
                    0.08815041,
                    -0.013158761,
                    -0.23350924,
                    -0.043668386,
                    -0.15479062,
                    -0.024266671,
                    0.08113482,
                    0.14451654,
                    -0.29152337,
                    -0.028919466,
                    0.15022752,
                    -0.26923147,
                    0.23846954,
                    0.03292609,
                    -0.23572414,
                    -0.14883325,
                    -0.12743121,
                    -0.052229587,
                    -0.14230779,
                    0.284658,
                    0.36885592,
                    -0.13176951,
                    -0.16442224,
                    -0.20283924,
                    0.048434418,
                    -0.16231743,
                    -0.0010730615,
                    0.1408047,
                    0.09481033,
                    0.018139571,
                    -0.030843062,
                    0.13304341,
                    -0.1516288,
                    -0.051779557,
                    0.46940327,
                    -0.07969027,
                    -0.051570967,
                    -0.038892798,
                    0.11187677,
                    0.1703113,
                    -0.39926252,
                    0.06859773,
                    0.08364686,
                    0.14696898,
                    0.026642298,
                    0.13225247,
                    0.05730332,
                    0.35534015,
                    0.11189959,
                    0.039673142,
                    -0.056019083,
                    0.15707816,
                    -0.11053284,
                    0.12823457,
                    0.20075114,
                    0.040237684,
                    -0.19367051,
                    0.13039409,
                    -0.26038498,
                    -0.05770229,
                    -0.009781617,
                    0.15812513,
                    -0.10420735,
                    -0.020158196,
                    0.13160926,
                    -0.20823349,
                    -0.045596864,
                    -0.2074525,
                    0.1546387,
                    0.30158705,
                    0.13175933,
                    0.11967154,
                    -0.09094463,
                    0.0019428955,
                    -0.06745872,
                    0.02998099,
                    -0.18385777,
                    0.014330351,
                    0.07141392,
                    -0.17461702,
                    0.099743806,
                    -0.016181415,
                    0.1661396,
                    0.070834026,
                    0.110713825,
                    0.14590909,
                    0.15404254,
                    -0.21658006,
                    0.00715122,
                    -0.10229453,
                    -0.09980027,
                    -0.09406554,
                    -0.014849227,
                    -0.26285952,
                    0.069972225,
                    0.05732395,
                    -0.10685719,
                    0.037572138,
                    -0.18863359,
                    -0.00083297276,
                    -0.16088934,
                    -0.117982,
                    -0.16381365,
                    -0.008932539,
                    -0.06549256,
                    -0.08928683,
                    0.29934987,
                    0.16532114,
                    -0.27117223,
                    -0.12302226,
                    -0.28685933,
                    -0.14041144,
                    -0.0062569617,
                    -0.20768198,
                    -0.15385273,
                    0.20506454,
                    -0.21685128,
                    0.1081962,
                    -0.13133131,
                    0.18937315,
                    0.14751591,
                    0.2786974,
                    -0.060183275,
                    0.10365405,
                    0.109799005,
                    -0.044105034,
                    -0.04260162,
                    0.025758557,
                    0.07590695,
                    0.0726137,
                    -0.09882405,
                    0.26437432,
                    0.15884234,
                    0.115702584,
                    0.0015900572,
                    0.11673009,
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                ],
                "index": 0,
                "object": "embedding",
            }
        ],
        object="list",
        usage=Usage(
            completion_tokens=0,
            prompt_tokens=0,
            total_tokens=0,
            completion_tokens_details=None,
        ),
    )

    cost = completion_cost(
        completion_response=response,
        custom_llm_provider="together_ai",
        call_type="embedding",
    )
    assert cost == 0


def test_completion_cost_params():
    """
    Relevant Issue: https://github.com/BerriAI/litellm/issues/6133
    """
    litellm.set_verbose = True
    resp1_prompt_cost, resp1_completion_cost = cost_per_token(
        model="gemini-3.8-flash",
        prompt_tokens=1000,
        completion_tokens=1000,
        custom_llm_provider="vertex_ai_beta",
    )

    resp2_prompt_cost, resp2_completion_cost = cost_per_token(
        model="gemini-3.8-flash", prompt_tokens=1000, completion_tokens=1000
    )

    assert resp2_prompt_cost > 0

    assert resp1_prompt_cost == resp2_prompt_cost
    assert resp1_completion_cost == resp2_completion_cost

    resp3_prompt_cost, resp3_completion_cost = cost_per_token(
        model="vertex_ai/gemini-3.8-flash", prompt_tokens=1000, completion_tokens=1000
    )

    assert resp3_prompt_cost > 0

    assert resp3_prompt_cost == resp1_prompt_cost
    assert resp3_completion_cost == resp1_completion_cost


def test_completion_cost_params_2():
    """
    Relevant Issue: https://github.com/BerriAI/litellm/issues/6133
    """
    litellm.set_verbose = True

    prompt_tokens = 1000
    completion_tokens = 1000
    resp1_prompt_cost, resp1_completion_cost = cost_per_token(
        model="gemini-3.8-flash",
        prompt_tokens=prompt_tokens,
        completion_tokens=completion_tokens,
    )

    print(resp1_prompt_cost, resp1_completion_cost)

    model_info = litellm.get_model_info("gemini-3.8-flash")
    input_cost_per_token = model_info["input_cost_per_token"]
    output_cost_per_token = model_info["output_cost_per_token"]

    assert resp1_prompt_cost == input_cost_per_token * prompt_tokens
    assert resp1_completion_cost == output_cost_per_token * completion_tokens


def test_completion_cost_params_gemini_3():
    from litellm.llms.vertex_ai.cost_calculator import cost_per_character
    from litellm.utils import Choices, Message, ModelResponse, Usage

    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    usage = Usage(
        completion_tokens=2,
        prompt_tokens=3771,
        total_tokens=3773,
        completion_tokens_details=None,
        prompt_tokens_details=None,
    )

    response = ModelResponse(
        id="chatcmpl-61043504-4439-48be-9996-e29bdee24dc3",
        choices=[
            Choices(
                finish_reason="stop",
                index=0,
                message=Message(
                    content="Sí. \n",
                    role="assistant",
                    tool_calls=None,
                    function_call=None,
                ),
            )
        ],
        created=1728529259,
        model="gemini-3.8-flash",
        object="chat.completion",
        system_fingerprint=None,
        usage=usage,
        vertex_ai_grounding_metadata=[],
        vertex_ai_safety_results=[
            [
                {
                    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
                    "probability": "NEGLIGIBLE",
                },
                {"category": "HARM_CATEGORY_HATE_SPEECH", "probability": "NEGLIGIBLE"},
                {"category": "HARM_CATEGORY_HARASSMENT", "probability": "NEGLIGIBLE"},
                {
                    "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                    "probability": "NEGLIGIBLE",
                },
            ]
        ],
        vertex_ai_citation_metadata=[],
    )

    pc, cc = cost_per_character(
        **{
            "model": "gemini-3.8-flash",
            "custom_llm_provider": "vertex_ai",
            "prompt_characters": None,
            "completion_characters": 3,
            "usage": usage,
        }
    )

    model_info = litellm.get_model_info("gemini-3.8-flash")

    # gemini-3.8-flash has no per-character pricing, so cost_per_character
    # falls back to per-token pricing using usage.prompt_tokens / usage.completion_tokens
    assert round(pc, 10) == round(3771 * model_info["input_cost_per_token"], 10)
    assert round(cc, 10) == round(
        2 * model_info["output_cost_per_token"],
        10,
    )


@pytest.mark.asyncio
# @pytest.mark.flaky(retries=3, delay=1)
@pytest.mark.parametrize("stream", [False])  # True,
async def test_test_completion_cost_gpt4o_audio_output_from_model(stream):
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")
    from litellm.types.utils import (
        ChatCompletionAudioResponse,
        Choices,
        CompletionTokensDetailsWrapper,
        Message,
        ModelResponse,
        PromptTokensDetailsWrapper,
        Usage,
    )

    usage_object = Usage(
        completion_tokens=34,
        prompt_tokens=16,
        total_tokens=50,
        completion_tokens_details=CompletionTokensDetailsWrapper(
            audio_tokens=28, reasoning_tokens=0, text_tokens=6
        ),
        prompt_tokens_details=PromptTokensDetailsWrapper(
            audio_tokens=0, cached_tokens=0, text_tokens=16, image_tokens=0
        ),
    )
    completion = ModelResponse(
        id="chatcmpl-AJnhcglpTV5u84s1cTxWFeIkGKAo7",
        choices=[
            Choices(
                finish_reason="stop",
                index=0,
                message=Message(
                    content=None,
                    role="assistant",
                    tool_calls=None,
                    function_call=None,
                    audio=ChatCompletionAudioResponse(
                        id="audio_6712c25ce73c819080b41362648bc6cb",
                        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                        expires_at=1729286252,
                        transcript="Yes.",
                    ),
                ),
            )
        ],
        created=1729282652,
        model="gpt-audio-1.5",
        object="chat.completion",
        system_fingerprint="fp_4eafc16e9d",
        usage=usage_object,
        service_tier=None,
    )

    cost = completion_cost(completion, model="gpt-audio-1.5")

    model_info = litellm.get_model_info("gpt-audio-1.5")
    print(f"model_info: {model_info}")
    ## input cost

    input_audio_cost = (
        model_info["input_cost_per_audio_token"]
        * usage_object.prompt_tokens_details.audio_tokens
    )
    input_text_cost = (
        model_info["input_cost_per_token"]
        * usage_object.prompt_tokens_details.text_tokens
    )

    total_input_cost = input_audio_cost + input_text_cost

    ## output cost

    output_audio_cost = (
        model_info["output_cost_per_audio_token"]
        * usage_object.completion_tokens_details.audio_tokens
    )
    output_text_cost = (
        model_info["output_cost_per_token"]
        * usage_object.completion_tokens_details.text_tokens
    )

    total_output_cost = output_audio_cost + output_text_cost

    assert round(cost, 2) == round(total_input_cost + total_output_cost, 2)


@pytest.mark.parametrize(
    "response_model, custom_llm_provider",
    [
        ("azure_ai/Meta-Llama-3.1-70B-Instruct", "azure_ai"),
        ("anthropic.claude-3-5-sonnet-20240620-v1:0", "bedrock"),
    ],
)
def test_completion_cost_model_response_cost(response_model, custom_llm_provider):
    """
    Relevant issue: https://github.com/BerriAI/litellm/issues/6310
    """
    from litellm import ModelResponse

    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")

    litellm.set_verbose = True
    response = {
        "id": "cmpl-55db75e0b05344058b0bd8ee4e00bf84",
        "choices": [
            {
                "finish_reason": "stop",
                "index": 0,
                "logprobs": None,
                "message": {
                    "content": 'Here\'s one:\n\nWhy did the Linux kernel go to therapy?\n\nBecause it had a lot of "core" issues!\n\nHope that one made you laugh!',
                    "refusal": None,
                    "role": "assistant",
                    "audio": None,
                    "function_call": None,
                    "tool_calls": [],
                },
            }
        ],
        "created": 1729243714,
        "model": response_model,
        "object": "chat.completion",
        "service_tier": None,
        "system_fingerprint": None,
        "usage": {
            "completion_tokens": 32,
            "prompt_tokens": 16,
            "total_tokens": 48,
            "completion_tokens_details": None,
            "prompt_tokens_details": None,
        },
    }

    model_response = ModelResponse(**response)
    cost = completion_cost(model_response, custom_llm_provider=custom_llm_provider)

    assert cost > 0


def test_select_model_name_for_cost_calc():
    from litellm.cost_calculator import select_model_name_for_cost_calc
    from litellm.types.utils import ModelResponse, Choices, Usage, Message

    args = {
        "model": "Mistral-large-nmefg",
        "completion_response": ModelResponse(
            id="127f24aed4984b4c9a4c5e32ad3752f3",
            created=1734406048,
            model="azure_ai/mistral-large",
            object="chat.completion",
            system_fingerprint=None,
            choices=[
                Choices(
                    finish_reason="length",
                    index=0,
                    message=Message(
                        content="I'm an artificial intelligence and do not have an LLM (Master",
                        role="assistant",
                        tool_calls=None,
                        function_call=None,
                    ),
                )
            ],
            usage=Usage(
                completion_tokens=15,
                prompt_tokens=8,
                total_tokens=23,
                completion_tokens_details=None,
                prompt_tokens_details=None,
            ),
            service_tier=None,
        ),
        "base_model": None,
        "custom_pricing": None,
    }

    return_model = select_model_name_for_cost_calc(**args)
    assert return_model == "azure_ai/mistral-large"


def test_cost_calculator_with_base_model():
    resp = litellm.completion(
        model="bedrock/random-model",
        messages=[{"role": "user", "content": "Hello, how are you?"}],
        base_model="bedrock/anthropic.claude-sonnet-5",
        mock_response="Hello, how are you?",
    )
    assert resp.model == "random-model"
    assert resp._hidden_params["response_cost"] > 0


@pytest.mark.parametrize("base_model_arg", ["litellm_param", "model_info"])
def test_cost_calculator_with_base_model_with_router(base_model_arg):
    from litellm import Router

    model_item = {
        "model_name": "random-model",
        "litellm_params": {
            "model": "bedrock/random-model",
        },
    }

    if base_model_arg == "litellm_param":
        model_item["litellm_params"][
            "base_model"
        ] = "bedrock/anthropic.claude-sonnet-5"
    elif base_model_arg == "model_info":
        model_item["model_info"] = {
            "base_model": "bedrock/anthropic.claude-sonnet-5",
        }

    router = Router(model_list=[model_item])
    resp = router.completion(
        model="random-model",
        messages=[{"role": "user", "content": "Hello, how are you?"}],
        mock_response="Hello, how are you?",
    )
    assert resp.model == "random-model"
    assert resp._hidden_params["response_cost"] > 0


@pytest.mark.parametrize("base_model_arg", ["litellm_param", "model_info"])
def test_cost_calculator_with_base_model_with_router_embedding(base_model_arg):
    from litellm import Router

    litellm.turn_on_debug()

    model_item = {
        "model_name": "random-model",
        "litellm_params": {
            "model": "bedrock/random-model",
        },
    }

    if base_model_arg == "litellm_param":
        model_item["litellm_params"]["base_model"] = "cohere.embed-english-v3"
    elif base_model_arg == "model_info":
        model_item["model_info"] = {
            "base_model": "cohere.embed-english-v3",
        }

    router = Router(model_list=[model_item])
    resp = router.embedding(
        model="random-model",
        input="Hello, how are you?",
        mock_response=[1, 2, 3],
    )
    assert resp.model == "random-model"
    assert resp._hidden_params["response_cost"] > 0


def test_cost_calculator_with_custom_pricing():
    resp = litellm.completion(
        model="bedrock/random-model",
        messages=[{"role": "user", "content": "Hello, how are you?"}],
        mock_response="Hello, how are you?",
        input_cost_per_token=0.0000008,
        output_cost_per_token=0.0000032,
    )
    assert resp.model == "random-model"
    assert resp._hidden_params["response_cost"] > 0


@pytest.fixture
def isolated_pricing_registry(local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
    monkeypatch.setattr(litellm, "open_ai_chat_completion_models", set(litellm.open_ai_chat_completion_models))
    monkeypatch.setattr(litellm, "models_by_provider", dict(litellm.models_by_provider))


_CUSTOM_PRICING_MODEL: Final = "migration-custom-pricing-chat"
_CUSTOM_PRICING_RATES: Final = MappingProxyType(
    {"input_cost_per_token": 0.0000008, "output_cost_per_token": 0.0000032}
)
_CUSTOM_PRICING_EXPECTED_COST: Final = 7 * 0.0000008 + 4 * 0.0000032


def _custom_pricing_chunk(choices: list[dict[str, object]], usage: dict[str, int] | None = None) -> str:
    chunk: Final = {
        "id": "chatcmpl-custom-pricing",
        "object": "chat.completion.chunk",
        "created": 1,
        "model": _CUSTOM_PRICING_MODEL,
        "choices": choices,
        **({"usage": usage} if usage is not None else {}),
    }
    return f"data: {json.dumps(chunk)}\n\n"


def _custom_pricing_upstream(request: httpx.Request) -> httpx.Response:
    if json.loads(request.content).get("stream") is True:
        body: Final = "".join(
            (
                _custom_pricing_chunk(
                    [{"index": 0, "delta": {"role": "assistant", "content": "I am ready to help."}, "finish_reason": None}]
                ),
                _custom_pricing_chunk([{"index": 0, "delta": {}, "finish_reason": "stop"}]),
                _custom_pricing_chunk([], {"prompt_tokens": 7, "completion_tokens": 4, "total_tokens": 11}),
                "data: [DONE]\n\n",
            )
        )
        return httpx.Response(200, text=body, headers={"content-type": "text/event-stream"})
    return httpx.Response(
        200,
        json={
            "id": "chatcmpl-custom-pricing",
            "object": "chat.completion",
            "created": 1,
            "model": _CUSTOM_PRICING_MODEL,
            "choices": [
                {"index": 0, "message": {"role": "assistant", "content": "I am ready to help."}, "finish_reason": "stop"}
            ],
            "usage": {"prompt_tokens": 7, "completion_tokens": 4, "total_tokens": 11},
        },
    )


@pytest.mark.parametrize("async_mode", [False, True])
@pytest.mark.asyncio
@pytest.mark.usefixtures("isolated_pricing_registry")
@respx.mock
async def test_custom_pricing_matches_response_usage(async_mode: bool) -> None:
    route: Final = respx.post("https://api.openai.com/v1/chat/completions").mock(side_effect=_custom_pricing_upstream)
    request: Final = {
        "model": f"openai/{_CUSTOM_PRICING_MODEL}",
        "messages": [{"role": "user", "content": "How are you?"}],
        "api_key": "sk-test",
        **_CUSTOM_PRICING_RATES,
    }

    response: Final = await litellm.acompletion(**request) if async_mode else litellm.completion(**request)

    assert len(route.calls) == 1
    assert response._hidden_params["response_cost"] == pytest.approx(_CUSTOM_PRICING_EXPECTED_COST)


@pytest.mark.usefixtures("isolated_pricing_registry")
@respx.mock
def test_streamed_custom_pricing_cost_matches_non_streamed_cost() -> None:
    respx.post("https://api.openai.com/v1/chat/completions").mock(side_effect=_custom_pricing_upstream)
    request: Final = {
        "model": f"openai/{_CUSTOM_PRICING_MODEL}",
        "messages": [{"role": "user", "content": "Hi"}],
        "api_key": "sk-test",
        **_CUSTOM_PRICING_RATES,
    }

    non_streamed: Final = litellm.completion(**request)
    rebuilt: Final = litellm.stream_chunk_builder(
        list(litellm.completion(**request, stream=True, stream_options={"include_usage": True})),
        messages=request["messages"],
    )
    streamed_cost: Final = completion_cost(rebuilt, custom_cost_per_token=_CUSTOM_PRICING_RATES)

    assert rebuilt.choices[0].message.content == non_streamed.choices[0].message.content == "I am ready to help."
    assert non_streamed._hidden_params["response_cost"] == pytest.approx(_CUSTOM_PRICING_EXPECTED_COST)
    assert streamed_cost == pytest.approx(non_streamed._hidden_params["response_cost"])


@pytest.mark.parametrize(
    ("model", "provider"),
    [
        ("fireworks_ai/accounts/fireworks/models/custom-migration-model", "fireworks_ai"),
        ("vertex_ai/meta/llama-custom-migration-model", "vertex_ai"),
        ("azure/custom-migration-model", "azure"),
        ("mistral/mistral-large-latest", "mistral"),
    ],
)
def test_custom_cost_uses_test_owned_rates_across_providers(model: str, provider: str):
    response: Final = litellm.ModelResponse(
        model=model,
        choices=[],
        usage=litellm.Usage(prompt_tokens=9, completion_tokens=4, total_tokens=13),
    )
    rates: Final = MappingProxyType({"input_cost_per_token": 0.002, "output_cost_per_token": 0.004})

    cost: Final = completion_cost(
        completion_response=response,
        model=model,
        custom_llm_provider=provider,
        custom_cost_per_token=rates,
    )

    assert cost == pytest.approx(0.034)


@respx.mock
def test_fireworks_completion_cost_uses_registered_rate(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
    model: Final = "fireworks_ai/accounts/fireworks/models/migration-model"
    monkeypatch.setitem(
        litellm.model_cost,
        model,
        {"input_cost_per_token": 0.001, "output_cost_per_token": 0.002, "litellm_provider": "fireworks_ai", "mode": "chat"},
    )
    respx.post("https://api.fireworks.ai/inference/v1/chat/completions").mock(
        return_value=httpx.Response(
            200,
            json={
                "id": "chatcmpl-fireworks",
                "object": "chat.completion",
                "created": 1,
                "model": "accounts/fireworks/models/migration-model",
                "choices": [
                    {"index": 0, "message": {"role": "assistant", "content": "Going great"}, "finish_reason": "stop"}
                ],
                "usage": {"prompt_tokens": 8, "completion_tokens": 5, "total_tokens": 13},
            },
        )
    )

    response: Final = litellm.completion(
        model=model, messages=[{"role": "user", "content": "Hey, how's it going?"}], api_key="fw-test"
    )

    assert completion_cost(completion_response=response) == pytest.approx(0.018)


def test_azure_embedding_cost_uses_registered_base_model_rate(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setitem(
        litellm.model_cost,
        "azure/migration-embedding-model",
        {
            "input_cost_per_token": 0.002,
            "output_cost_per_token": 0.0,
            "litellm_provider": "azure",
            "mode": "embedding",
        },
    )

    cost: Final = response_cost_calculator(
        response_object=EmbeddingResponse(
            model="text-embedding-3-small",
            data=[{"embedding": [1, 2, 3]}],
            usage=Usage(prompt_tokens=10, completion_tokens=10),
        ),
        model="text-embedding-3-small",
        custom_llm_provider=None,
        base_model="azure/migration-embedding-model",
        call_type="aembedding",
        optional_params={},
        custom_pricing=False,
        prompt="Hello, world!",
    )

    assert cost == pytest.approx(0.02)


def test_azure_common_deployment_uses_registered_base_model_pricing(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setitem(
        litellm.model_cost,
        "azure/migration-pricing-model",
        {
            "input_cost_per_token": 0.001,
            "output_cost_per_token": 0.002,
            "litellm_provider": "azure",
            "mode": "chat",
        },
    )
    response: Final = ModelResponse(
        id="chatcmpl-migration",
        choices=[Choices(finish_reason="stop", index=0, message=Message(content="done", role="assistant"))],
        created=1,
        model="migration-deployment",
        object="chat.completion",
        usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15),
    )

    cost: Final = response_cost_calculator(
        response_object=response,
        model="migration-deployment",
        custom_llm_provider="azure",
        call_type="acompletion",
        optional_params={},
        base_model="azure/migration-pricing-model",
    )

    assert cost == pytest.approx(0.02)


def test_azure_tts_cost_uses_test_owned_character_rate(monkeypatch: pytest.MonkeyPatch) -> None:
    model: Final = "migration-tts-model"
    monkeypatch.setitem(
        litellm.model_cost,
        f"azure/{model}",
        {
            "input_cost_per_character": 0.01,
            "output_cost_per_character": 0.0,
            "litellm_provider": "azure",
            "mode": "audio_speech",
        },
    )

    cost: Final = response_cost_calculator(
        response_object=TranscriptionResponse(text=""),
        model=model,
        custom_llm_provider="azure",
        call_type="aspeech",
        optional_params={},
        prompt="hello",
    )

    assert cost == pytest.approx(0.05)


def test_moderation_cost_uses_test_owned_token_rate() -> None:
    prompt: Final = "moderation input"
    rates: Final = MappingProxyType(
        {"input_cost_per_token": 0.001, "output_cost_per_token": 0.0}
    )

    cost: Final = completion_cost(
        completion_response=None,
        model="omni-moderation-latest",
        custom_llm_provider="openai",
        call_type="moderation",
        prompt=prompt,
        custom_cost_per_token=rates,
    )
    expected_cost: Final = litellm.token_counter(model="omni-moderation-latest", text=prompt) * 0.001

    assert cost == pytest.approx(expected_cost)


def test_vertex_predictive_cost_uses_test_owned_token_rate() -> None:
    model: Final = "vertex_ai/migration-llama"
    messages: Final = [{"role": "user", "content": "price control"}]
    rates: Final = MappingProxyType(
        {"input_cost_per_token": 0.001, "output_cost_per_token": 0.002}
    )

    cost: Final = completion_cost(
        model=model,
        messages=messages,
        custom_llm_provider="vertex_ai",
        custom_cost_per_token=rates,
    )
    expected_cost: Final = (
        litellm.token_counter(model=model, messages=messages) * 0.001
    )

    assert cost == pytest.approx(expected_cost)


@pytest.mark.parametrize("async_mode", [False, True])
@pytest.mark.asyncio
@pytest.mark.usefixtures("isolated_pricing_registry")
@respx.mock
async def test_custom_pricing_cost_reaches_success_callback(
    async_mode: bool, monkeypatch: pytest.MonkeyPatch
) -> None:
    model: Final = "migration-callback-pricing-model"
    litellm.register_model(
        {
            model: {
                "input_cost_per_token": 0.0001,
                "output_cost_per_token": 0.0002,
                "litellm_provider": "openai",
                "mode": "chat",
            }
        },
        persist_across_reloads=False,
    )
    callback: Final = ResponseCostCapture()
    if async_mode:
        monkeypatch.setattr(litellm, "_async_success_callback", [callback])
    else:
        monkeypatch.setattr(litellm, "success_callback", [callback])
    response_data: Final = {
        "id": "chatcmpl-priced",
        "object": "chat.completion",
        "created": 1,
        "model": model,
        "choices": [
            {
                "index": 0,
                "message": {"role": "assistant", "content": "response"},
                "finish_reason": "stop",
            }
        ],
        "usage": {"prompt_tokens": 8, "completion_tokens": 5, "total_tokens": 13},
    }
    route: Final = respx.post("https://api.openai.com/v1/chat/completions").mock(
        return_value=httpx.Response(200, json=response_data)
    )
    request: Final = {
        "model": model,
        "messages": [{"role": "user", "content": "price control"}],
        "api_key": "test-openai-key",
        "input_cost_per_token": 0.001,
        "output_cost_per_token": 0.002,
    }
    response: Final = (
        await litellm.acompletion(**request) if async_mode else litellm.completion(**request)
    )
    await GLOBAL_LOGGING_WORKER.flush()

    assert await asyncio.to_thread(callback.success_event.wait, 10.0)
    assert len(route.calls) == 1
    assert response.choices[0].message.content == "response"
    assert callback.success_cost == pytest.approx(0.018)


@pytest.mark.parametrize("async_mode", [False, True])
@pytest.mark.asyncio
async def test_failure_completion_cost_reaches_failure_callback(
    async_mode: bool, monkeypatch: pytest.MonkeyPatch
) -> None:
    callback: Final = ResponseCostCapture()
    monkeypatch.setattr(litellm, "success_callback", [callback])
    monkeypatch.setattr(litellm, "_async_success_callback", [callback])
    monkeypatch.setattr(litellm, "failure_callback", [callback])
    monkeypatch.setattr(litellm, "_async_failure_callback", [callback])
    failure: Final = litellm.InternalServerError(
        message="scripted failure",
        llm_provider="openai",
        model="gpt-6-sol",
    )

    if async_mode:
        with pytest.raises(litellm.InternalServerError):
            await litellm.acompletion(
                model="gpt-6-sol",
                messages=[{"role": "user", "content": "fail"}],
                mock_response=failure,
            )
    else:
        with pytest.raises(litellm.InternalServerError):
            litellm.completion(
                model="gpt-6-sol",
                messages=[{"role": "user", "content": "fail"}],
                mock_response=failure,
            )

    assert callback.failure_cost == 0.0


@pytest.fixture
def model_item():
    return {
        "model_name": "random-model",
        "litellm_params": {
            "model": "openai/my-fake-model",
            "api_key": "my-fake-key",
            "api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
        },
        "model_info": {},
    }


@pytest.mark.parametrize(
    "custom_pricing",
    [
        "litellm_params",
        "model_info",
    ],
)
@pytest.mark.asyncio
async def test_cost_calculator_with_custom_pricing_router(model_item, custom_pricing):
    from litellm import Router

    if custom_pricing == "litellm_params":
        model_item["litellm_params"]["input_cost_per_token"] = 0.0000008
        model_item["litellm_params"]["output_cost_per_token"] = 0.0000032
    elif custom_pricing == "model_info":
        model_item["model_info"]["input_cost_per_token"] = 0.0000008
        model_item["model_info"]["output_cost_per_token"] = 0.0000032

    router = Router(model_list=[model_item])
    resp = await router.acompletion(
        model="random-model",
        messages=[{"role": "user", "content": "Hello, how are you?"}],
        mock_response="Hello, how are you?",
    )
    # assert resp.model == "random-model"
    assert resp._hidden_params["response_cost"] > 0


def test_json_valid_model_cost_map():
    import json

    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"

    model_cost = litellm.get_model_cost_map(url="")

    try:
        # Attempt to serialize and deserialize the JSON
        json_str = json.dumps(model_cost)
        json.loads(json_str)
    except json.JSONDecodeError as e:
        pytest.fail(f"Invalid JSON format: {str(e)}")


def test_batch_cost_calculator():

    args = {
        "completion_response": {
            "choices": [
                {
                    "content_filter_results": {
                        "hate": {"filtered": False, "severity": "safe"},
                        "protected_material_code": {
                            "filtered": False,
                            "detected": False,
                        },
                        "protected_material_text": {
                            "filtered": False,
                            "detected": False,
                        },
                        "self_harm": {"filtered": False, "severity": "safe"},
                        "sexual": {"filtered": False, "severity": "safe"},
                        "violence": {"filtered": False, "severity": "safe"},
                    },
                    "finish_reason": "stop",
                    "index": 0,
                    "logprobs": None,
                    "message": {
                        "content": 'As of my last update in October 2023, there are eight recognized planets in the solar system. They are:\n\n1. **Mercury** - The closest planet to the Sun, known for its extreme temperature fluctuations.\n2. **Venus** - Similar in size to Earth but with a thick atmosphere rich in carbon dioxide, leading to a greenhouse effect that makes it the hottest planet.\n3. **Earth** - The only planet known to support life, with a diverse environment and liquid water.\n4. **Mars** - Known as the Red Planet, it has the largest volcano and canyon in the solar system and features signs of past water.\n5. **Jupiter** - The largest planet in the solar system, known for its Great Red Spot and numerous moons.\n6. **Saturn** - Famous for its stunning rings, it is a gas giant also known for its extensive moon system.\n7. **Uranus** - An ice giant with a unique tilt, it rotates on its side and has a blue color due to methane in its atmosphere.\n8. **Neptune** - Another ice giant, known for its deep blue color and strong winds, it is the farthest planet from the Sun.\n\nPluto was previously classified as the ninth planet but was reclassified as a "dwarf planet" in 2006 by the International Astronomical Union.',
                        "refusal": None,
                        "role": "assistant",
                    },
                }
            ],
            "created": 1741135408,
            "id": "chatcmpl-B7X96teepFM4ILP7cm4Ga62eRuV8p",
            "model": "gpt-4o-mini-2024-07-18",
            "object": "chat.completion",
            "prompt_filter_results": [
                {
                    "prompt_index": 0,
                    "content_filter_results": {
                        "hate": {"filtered": False, "severity": "safe"},
                        "jailbreak": {"filtered": False, "detected": False},
                        "self_harm": {"filtered": False, "severity": "safe"},
                        "sexual": {"filtered": False, "severity": "safe"},
                        "violence": {"filtered": False, "severity": "safe"},
                    },
                }
            ],
            "system_fingerprint": "fp_b705f0c291",
            "usage": {
                "completion_tokens": 278,
                "completion_tokens_details": {
                    "accepted_prediction_tokens": 0,
                    "audio_tokens": 0,
                    "reasoning_tokens": 0,
                    "rejected_prediction_tokens": 0,
                },
                "prompt_tokens": 20,
                "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0},
                "total_tokens": 298,
            },
        },
        "model": None,
    }

    cost = completion_cost(**args)
    assert cost > 0