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test: drop unrelated reformatting from merge resolution
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
shivam
parent
3aeae3c7fe
commit
305caa8260
1 changed files with 241 additions and 94 deletions
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@ -1,3 +1,4 @@
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import json
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from pathlib import Path
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from typing import Final
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@ -148,7 +149,9 @@ def test_jina_rerank_bills_total_tokens_at_input_rate_only(_local_model_cost_map
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def test_cost_calculator_with_response_cost_in_additional_headers():
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class MockResponse(BaseModel):
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_hidden_params = {"additional_headers": {"llm_provider-x-litellm-response-cost": 1000}}
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_hidden_params = {
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"additional_headers": {"llm_provider-x-litellm-response-cost": 1000}
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}
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result = response_cost_calculator(
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response_object=MockResponse(),
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@ -204,9 +207,7 @@ def test_vertex_lyria_speech_cost(
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call_type=call_type,
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)
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expected: Final = (
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0 if runtime_state == "custom_zero" else expected_cost * (2 if runtime_state == "custom_price" else 1)
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)
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expected: Final = 0 if runtime_state == "custom_zero" else expected_cost * (2 if runtime_state == "custom_price" else 1)
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assert cost == pytest.approx(expected)
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@ -333,12 +334,13 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch):
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# Step 1: Test a model where input_cost_per_image_token is not set.
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# In this case the calculation should use input_cost_per_token as fallback.
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assert model_info.get("input_cost_per_image_token") is None, (
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"Test case expects that input_cost_per_image_token is not set"
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)
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assert (
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model_info.get("input_cost_per_image_token") is None
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), "Test case expects that input_cost_per_image_token is not set"
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expected_cost = (
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usage.prompt_tokens_details.audio_tokens * model_info["input_cost_per_audio_token"]
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usage.prompt_tokens_details.audio_tokens
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* model_info["input_cost_per_audio_token"]
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+ usage.prompt_tokens_details.text_tokens * model_info["input_cost_per_token"]
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+ usage.prompt_tokens_details.image_tokens * model_info["input_cost_per_token"]
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+ usage.completion_tokens * model_info["output_cost_per_token"]
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@ -373,9 +375,12 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch):
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)
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expected_cost = (
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usage.prompt_tokens_details.audio_tokens * temp_model_info_object["input_cost_per_audio_token"]
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+ usage.prompt_tokens_details.text_tokens * temp_model_info_object["input_cost_per_token"]
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+ usage.prompt_tokens_details.image_tokens * temp_model_info_object["input_cost_per_image_token"]
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usage.prompt_tokens_details.audio_tokens
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* temp_model_info_object["input_cost_per_audio_token"]
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+ usage.prompt_tokens_details.text_tokens
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* temp_model_info_object["input_cost_per_token"]
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+ usage.prompt_tokens_details.image_tokens
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* temp_model_info_object["input_cost_per_image_token"]
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+ usage.completion_tokens * temp_model_info_object["output_cost_per_token"]
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)
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@ -385,11 +390,14 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch):
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def test_transcription_cost_uses_token_pricing(_local_model_cost_map):
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from litellm import completion_cost
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usage = Usage(
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prompt_tokens=14,
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completion_tokens=45,
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total_tokens=59,
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prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=0, audio_tokens=14),
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prompt_tokens_details=PromptTokensDetailsWrapper(
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text_tokens=0, audio_tokens=14
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),
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)
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response = TranscriptionResponse(text="demo text")
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response.usage = usage
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@ -433,6 +441,7 @@ def test_transcription_token_pricing_is_provider_aware(_local_model_cost_map):
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def test_transcription_cost_falls_back_to_duration(_local_model_cost_map):
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from litellm import completion_cost
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response = TranscriptionResponse(text="demo text")
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response.duration = 10.0
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@ -453,6 +462,7 @@ def test_vertex_chirp_3_transcription_cost_from_duration(_local_model_cost_map):
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every transcription priced to $0.00 instead of using input_cost_per_second."""
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from litellm import completion_cost
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response = TranscriptionResponse(text="demo text")
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response.duration = 18.0
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@ -476,7 +486,9 @@ def test_handle_realtime_stream_cost_calculation():
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{"type": "session.created", "session": {"model": "gpt-3.5-turbo"}},
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{
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"type": "response.done",
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"response": {"usage": {"input_tokens": 100, "output_tokens": 50, "total_tokens": 150}},
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"response": {
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"usage": {"input_tokens": 100, "output_tokens": 50, "total_tokens": 150}
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},
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},
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{
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"type": "response.done",
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@ -507,7 +519,9 @@ def test_handle_realtime_stream_cost_calculation():
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expected_cost = (300 * 0.0015 / 1000) + ( # input tokens (100 + 200)
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150 * 0.002 / 1000
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) # output tokens (50 + 100)
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assert abs(cost - expected_cost) <= 0.00075 # Allow small floating point differences
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assert (
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abs(cost - expected_cost) <= 0.00075
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) # Allow small floating point differences
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# Test with different model name in session
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results[0]["session"]["model"] = "gpt-4"
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@ -587,7 +601,14 @@ def test_handle_realtime_stream_cost_calculation_stores_cost_breakdown():
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assert logging_obj.cost_breakdown is not None
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assert logging_obj.cost_breakdown["input_cost"] > 0
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assert logging_obj.cost_breakdown["output_cost"] > 0
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assert abs(logging_obj.cost_breakdown["input_cost"] + logging_obj.cost_breakdown["output_cost"] - total_cost) < 1e-9
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assert (
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abs(
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logging_obj.cost_breakdown["input_cost"]
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+ logging_obj.cost_breakdown["output_cost"]
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- total_cost
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)
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< 1e-9
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)
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assert abs(logging_obj.cost_breakdown["total_cost"] - total_cost) < 1e-9
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@ -661,7 +682,9 @@ def test_realtime_logging_object_allows_null_transcript_in_conversation_item_add
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},
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]
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usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results)
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usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
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results=results
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)
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logging_result = RealtimeAPITokenUsageProcessor.create_logging_realtime_object(
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usage=usage,
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results=results,
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@ -711,7 +734,9 @@ def test_realtime_logging_object_does_not_validate_unknown_event_types():
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},
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]
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usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results)
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usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
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results=results
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)
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# On unfixed code this raises pydantic ValidationError instead of returning.
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logging_result = RealtimeAPITokenUsageProcessor.create_logging_realtime_object(
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usage=usage,
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@ -723,7 +748,8 @@ def test_realtime_logging_object_does_not_validate_unknown_event_types():
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unknown_types = {
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r["type"]
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for r in logging_result.results
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if r["type"] in ("rate_limits.updated", "response.function_call_arguments.delta")
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if r["type"]
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in ("rate_limits.updated", "response.function_call_arguments.delta")
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}
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assert unknown_types == {
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"rate_limits.updated",
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@ -756,7 +782,9 @@ def test_realtime_transcription_duration_cost(monkeypatch):
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"type": "session.created",
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"session": {
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"type": "transcription",
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"audio": {"input": {"transcription": {"model": "gpt-realtime-whisper"}}},
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"audio": {
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"input": {"transcription": {"model": "gpt-realtime-whisper"}}
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},
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},
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},
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{
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@ -771,7 +799,9 @@ def test_realtime_transcription_duration_cost(monkeypatch):
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},
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]
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combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results)
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combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
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results=results
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)
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logging_obj = Logging(
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model="gpt-realtime-whisper",
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messages=[],
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@ -864,7 +894,9 @@ def test_realtime_transcription_token_billed_fallback(monkeypatch):
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# gpt-4o-transcribe: input_cost_per_audio_token = 2.5e-06, input_cost_per_token = 2.5e-06,
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# output_cost_per_token = 1e-05
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model_info = litellm.get_model_info(model="gpt-4o-transcribe", custom_llm_provider="openai")
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model_info = litellm.get_model_info(
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model="gpt-4o-transcribe", custom_llm_provider="openai"
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)
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usage = {
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"type": "tokens",
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"input_tokens": 40,
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@ -945,7 +977,10 @@ def test_get_transcription_model_falls_back_to_session_model(monkeypatch):
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mock_response=True,
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)
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assert result._hidden_params["response_cost"] > result_2._hidden_params["response_cost"]
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assert (
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result._hidden_params["response_cost"]
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> result_2._hidden_params["response_cost"]
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)
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model_info = router.get_deployment_model_info(
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model_id="my-unique-model-id", model_name="anthropic/claude-sonnet-4-5-20250929"
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@ -1108,7 +1143,9 @@ def test_tiered_pricing_only_deployment_selects_router_model_id():
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assert entry.get("input_cost_per_token") is None
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assert entry.get("tiered_pricing") is not None
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# The stripped shared alias must not carry tiered pricing.
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assert litellm.model_cost["dashscope/qwen-tier-only-test"].get("tiered_pricing") is None
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assert (
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litellm.model_cost["dashscope/qwen-tier-only-test"].get("tiered_pricing") is None
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)
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selected = _select_model_name_for_cost_calc(
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model="dashscope/qwen-tier-only-test",
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@ -1188,7 +1225,9 @@ def test_azure_realtime_cost_calculator(_local_model_cost_map):
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combined_usage_object=Usage(
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prompt_tokens=100,
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completion_tokens=100,
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prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=10, audio_tokens=90),
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prompt_tokens_details=PromptTokensDetailsWrapper(
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text_tokens=10, audio_tokens=90
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),
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),
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custom_llm_provider="azure",
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litellm_model_name="my-custom-azure-deployment",
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@ -1207,6 +1246,7 @@ def test_azure_audio_output_cost_calculation(_local_model_cost_map):
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"""
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from litellm.types.utils import Choices, CompletionTokensDetailsWrapper, Message
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# Scenario from issue #19764:
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# Input: 17 text tokens, 0 audio tokens
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# Output: 110 text tokens, 482 audio tokens
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@ -1262,10 +1302,14 @@ def test_azure_audio_output_cost_calculation(_local_model_cost_map):
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wrong_total_cost = expected_input_cost + wrong_output_cost
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# Verify audio tokens are NOT charged at text rate (the bug)
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assert abs(cost - wrong_total_cost) > 0.001, "Bug: Audio tokens are being charged at text token rate"
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assert (
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abs(cost - wrong_total_cost) > 0.001
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), "Bug: Audio tokens are being charged at text token rate"
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# Verify cost matches
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assert abs(cost - expected_total_cost) < 0.0000001, f"Expected cost {expected_total_cost}, got {cost}"
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assert (
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abs(cost - expected_total_cost) < 0.0000001
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), f"Expected cost {expected_total_cost}, got {cost}"
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def test_default_image_cost_calculator(monkeypatch):
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@ -1279,7 +1323,9 @@ def test_default_image_cost_calculator(monkeypatch):
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monkeypatch.setattr(
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litellm,
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"model_cost",
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{"azure/bf9001cd7209f5734ecb4ab937a5a0e2ba5f119708bd68f184db362930f9dc7b": temp_object},
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{
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"azure/bf9001cd7209f5734ecb4ab937a5a0e2ba5f119708bd68f184db362930f9dc7b": temp_object
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},
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)
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args = {
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@ -1495,7 +1541,9 @@ def test_gemini_25_implicit_caching_cost():
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expected_cost = 0.00068708
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# Allow for small floating point differences
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assert abs(result - expected_cost) < 1e-8, f"Expected cost {expected_cost}, but got {result}"
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assert (
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abs(result - expected_cost) < 1e-8
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), f"Expected cost {expected_cost}, but got {result}"
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print(f"✓ Gemini 2.5 implicit caching cost calculation is correct: ${result:.8f}")
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@ -1566,7 +1614,9 @@ def test_log_context_cost_calculation():
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# Get model info to understand the pricing
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from litellm import get_model_info
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model_info = get_model_info(model="claude-4-sonnet-20250514", custom_llm_provider="anthropic")
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model_info = get_model_info(
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model="claude-4-sonnet-20250514", custom_llm_provider="anthropic"
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)
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# Calculate expected cost based on actual model pricing
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input_cost_per_token = model_info.get("input_cost_per_token", 0)
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@ -1574,8 +1624,12 @@ def test_log_context_cost_calculation():
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cache_creation_cost_per_token = model_info.get("cache_creation_input_token_cost", 0)
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# Check if tiered pricing is applied
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input_cost_above_200k = model_info.get("input_cost_per_token_above_200k_tokens", input_cost_per_token)
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output_cost_above_200k = model_info.get("output_cost_per_token_above_200k_tokens", output_cost_per_token)
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input_cost_above_200k = model_info.get(
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"input_cost_per_token_above_200k_tokens", input_cost_per_token
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)
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output_cost_above_200k = model_info.get(
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"output_cost_per_token_above_200k_tokens", output_cost_per_token
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)
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cache_creation_above_200k = model_info.get(
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"cache_creation_input_token_cost_above_200k_tokens",
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cache_creation_cost_per_token,
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@ -1583,23 +1637,31 @@ def test_log_context_cost_calculation():
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print(f"DEBUG: Base input cost per token: ${input_cost_per_token:.2e}")
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print(f"DEBUG: Base output cost per token: ${output_cost_per_token:.2e}")
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print(f"DEBUG: Base cache creation cost per token: ${cache_creation_cost_per_token:.2e}")
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print(
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f"DEBUG: Base cache creation cost per token: ${cache_creation_cost_per_token:.2e}"
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)
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# Handle tiered pricing - if not available, use base pricing
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if input_cost_above_200k is not None:
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print(f"DEBUG: Tiered input cost per token (>200k): ${input_cost_above_200k:.2e}")
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print(
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f"DEBUG: Tiered input cost per token (>200k): ${input_cost_above_200k:.2e}"
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)
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else:
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print("DEBUG: No tiered input pricing available, using base pricing")
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input_cost_above_200k = input_cost_per_token
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if output_cost_above_200k is not None:
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print(f"DEBUG: Tiered output cost per token (>200k): ${output_cost_above_200k:.2e}")
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print(
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f"DEBUG: Tiered output cost per token (>200k): ${output_cost_above_200k:.2e}"
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)
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else:
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print("DEBUG: No tiered output pricing available, using base pricing")
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output_cost_above_200k = output_cost_per_token
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if cache_creation_above_200k is not None:
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print(f"DEBUG: Tiered cache creation cost per token (>200k): ${cache_creation_above_200k:.2e}")
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print(
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f"DEBUG: Tiered cache creation cost per token (>200k): ${cache_creation_above_200k:.2e}"
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)
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else:
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print("DEBUG: No tiered cache creation pricing available, using base pricing")
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cache_creation_above_200k = cache_creation_cost_per_token
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@ -1613,9 +1675,13 @@ def test_log_context_cost_calculation():
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print(f"DEBUG: Expected total: ${expected_total:.6f}")
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# Allow for small floating point differences
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assert abs(result - expected_total) < 1e-6, f"Expected cost ${expected_total:.6f}, but got ${result:.6f}"
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assert (
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abs(result - expected_total) < 1e-6
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), f"Expected cost ${expected_total:.6f}, but got ${result:.6f}"
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print(f"✓ Log context cost calculation with tiered pricing is correct: ${result:.6f}")
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print(
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f"✓ Log context cost calculation with tiered pricing is correct: ${result:.6f}"
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)
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print(f" - Input tokens (300k): ${expected_input_cost:.6f}")
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print(f" - Output tokens (50k): ${expected_output_cost:.6f}")
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print(f" - Cache creation (1k): ${expected_cache_cost:.6f}")
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@ -1674,7 +1740,8 @@ def test_gemini_25_explicit_caching_cost_direct_usage():
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expected_actual_cost = (
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model_info["input_cost_per_token"] * usage.prompt_tokens_details.text_tokens
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+ model_info["cache_read_input_token_cost"] * usage.prompt_tokens_details.cached_tokens
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+ model_info["cache_read_input_token_cost"]
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* usage.prompt_tokens_details.cached_tokens
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+ model_info["output_cost_per_token"] * usage.completion_tokens
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)
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@ -1698,6 +1765,7 @@ def test_azure_ai_cache_cost_calculation(_local_model_cost_map):
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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(
|
||||
|
|
@ -1746,12 +1814,13 @@ def test_azure_ai_cache_cost_calculation(_local_model_cost_map):
|
|||
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}"
|
||||
)
|
||||
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}"
|
||||
|
||||
|
||||
|
||||
AZURE_GPT_5_6_MAP_KEYS = (
|
||||
|
|
@ -1820,7 +1889,6 @@ def test_azure_gpt_5_6_rates_match_azure_price_page(_local_model_cost_map, model
|
|||
for key in token_cost_keys:
|
||||
assert entry[key] == pytest.approx(global_entry[key] * 1.1), key
|
||||
|
||||
|
||||
def test_vertex_regional_deployment_costs_uplift_over_global(monkeypatch):
|
||||
"""
|
||||
Regression for https://github.com/BerriAI/litellm/issues/34393: two Vertex
|
||||
|
|
@ -1903,6 +1971,7 @@ def test_cost_discount_vertex_ai(monkeypatch):
|
|||
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",
|
||||
|
|
@ -1931,6 +2000,7 @@ def test_cost_discount_vertex_ai(monkeypatch):
|
|||
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)
|
||||
|
|
@ -1948,6 +2018,7 @@ def test_cost_discount_not_applied_to_other_providers(monkeypatch):
|
|||
from litellm import completion_cost
|
||||
from litellm.types.utils import Usage
|
||||
|
||||
|
||||
# Create mock response for OpenAI
|
||||
response = ModelResponse(
|
||||
id="test-id",
|
||||
|
|
@ -1976,6 +2047,7 @@ def test_cost_discount_not_applied_to_other_providers(monkeypatch):
|
|||
custom_llm_provider="openai",
|
||||
)
|
||||
|
||||
|
||||
# Costs should be the same (no discount applied to OpenAI)
|
||||
assert cost_with_selective_discount == cost_without_discount
|
||||
|
||||
|
|
@ -1991,6 +2063,7 @@ def test_cost_margin_percentage(monkeypatch):
|
|||
from litellm import completion_cost
|
||||
from litellm.types.utils import Usage
|
||||
|
||||
|
||||
# Create mock response
|
||||
response = ModelResponse(
|
||||
id="test-id",
|
||||
|
|
@ -2019,6 +2092,7 @@ def test_cost_margin_percentage(monkeypatch):
|
|||
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)
|
||||
|
|
@ -2036,6 +2110,7 @@ def test_cost_margin_fixed_amount(monkeypatch):
|
|||
from litellm import completion_cost
|
||||
from litellm.types.utils import Usage
|
||||
|
||||
|
||||
# Create mock response
|
||||
response = ModelResponse(
|
||||
id="test-id",
|
||||
|
|
@ -2064,6 +2139,7 @@ def test_cost_margin_fixed_amount(monkeypatch):
|
|||
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)
|
||||
|
|
@ -2081,6 +2157,7 @@ def test_cost_margin_combined(monkeypatch):
|
|||
from litellm import completion_cost
|
||||
from litellm.types.utils import Usage
|
||||
|
||||
|
||||
# Create mock response
|
||||
response = ModelResponse(
|
||||
id="test-id",
|
||||
|
|
@ -2100,7 +2177,9 @@ def test_cost_margin_combined(monkeypatch):
|
|||
)
|
||||
|
||||
# Set 8% margin + $0.0005 fixed for openai
|
||||
monkeypatch.setattr(litellm, "cost_margin_config", {"openai": {"percentage": 0.08, "fixed_amount": 0.0005}})
|
||||
monkeypatch.setattr(litellm, "cost_margin_config", {
|
||||
"openai": {"percentage": 0.08, "fixed_amount": 0.0005}
|
||||
})
|
||||
|
||||
# Calculate cost with margin
|
||||
cost_with_margin = completion_cost(
|
||||
|
|
@ -2109,6 +2188,7 @@ def test_cost_margin_combined(monkeypatch):
|
|||
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)
|
||||
|
|
@ -2126,6 +2206,7 @@ def test_cost_margin_global(monkeypatch):
|
|||
from litellm import completion_cost
|
||||
from litellm.types.utils import Usage
|
||||
|
||||
|
||||
# Create mock response
|
||||
response = ModelResponse(
|
||||
id="test-id",
|
||||
|
|
@ -2154,6 +2235,7 @@ def test_cost_margin_global(monkeypatch):
|
|||
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)
|
||||
|
|
@ -2171,6 +2253,7 @@ def test_cost_margin_provider_overrides_global(monkeypatch):
|
|||
from litellm import completion_cost
|
||||
from litellm.types.utils import Usage
|
||||
|
||||
|
||||
# Create mock response
|
||||
response = ModelResponse(
|
||||
id="test-id",
|
||||
|
|
@ -2199,13 +2282,16 @@ def test_cost_margin_provider_overrides_global(monkeypatch):
|
|||
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" - 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}")
|
||||
|
||||
|
||||
|
|
@ -2216,6 +2302,7 @@ def test_cost_margin_with_discount(monkeypatch):
|
|||
from litellm import completion_cost
|
||||
from litellm.types.utils import Usage
|
||||
|
||||
|
||||
# Create mock response
|
||||
response = ModelResponse(
|
||||
id="test-id",
|
||||
|
|
@ -2246,6 +2333,7 @@ def test_cost_margin_with_discount(monkeypatch):
|
|||
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
|
||||
|
|
@ -2283,7 +2371,9 @@ def test_azure_image_generation_cost_calculator():
|
|||
size=None,
|
||||
usage=ImageUsage(
|
||||
input_tokens=0,
|
||||
input_tokens_details=ImageUsageInputTokensDetails(image_tokens=0, text_tokens=0),
|
||||
input_tokens_details=ImageUsageInputTokensDetails(
|
||||
image_tokens=0, text_tokens=0
|
||||
),
|
||||
output_tokens=0,
|
||||
total_tokens=0,
|
||||
),
|
||||
|
|
@ -2313,6 +2403,7 @@ def test_completion_cost_extracts_service_tier_from_response(_local_model_cost_m
|
|||
"""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"
|
||||
|
||||
|
|
@ -2353,18 +2444,23 @@ def test_completion_cost_extracts_service_tier_from_response(_local_model_cost_m
|
|||
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}"
|
||||
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)
|
||||
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")
|
||||
|
||||
|
|
@ -2382,7 +2478,9 @@ def test_completion_cost_extracts_service_tier_from_usage(_local_model_cost_map)
|
|||
)
|
||||
|
||||
# Create usage object without service_tier
|
||||
usage_without_service_tier = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
|
||||
usage_without_service_tier = Usage(
|
||||
prompt_tokens=1000, completion_tokens=500, total_tokens=1500
|
||||
)
|
||||
|
||||
# Create ModelResponse with usage without service_tier
|
||||
response_standard = ModelResponse(
|
||||
|
|
@ -2403,13 +2501,16 @@ def test_completion_cost_extracts_service_tier_from_usage(_local_model_cost_map)
|
|||
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}"
|
||||
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 service_tier extraction follows priority: optional_params > completion_response > usage."""
|
||||
from litellm import completion_cost
|
||||
|
||||
|
||||
# Test with gpt-5-nano which has flex pricing
|
||||
model = "gpt-5-nano"
|
||||
|
||||
|
|
@ -2458,13 +2559,16 @@ def test_completion_cost_service_tier_priority(_local_model_cost_map):
|
|||
assert cost_from_usage > 0, "Cost from usage should be greater than 0"
|
||||
|
||||
# Costs should be similar (all using flex)
|
||||
assert abs(cost_from_params - cost_from_usage) < 1e-6, "Costs from params and usage should be similar (both flex)"
|
||||
assert (
|
||||
abs(cost_from_params - cost_from_usage) < 1e-6
|
||||
), "Costs from params and usage should be similar (both flex)"
|
||||
|
||||
|
||||
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={
|
||||
|
|
@ -2520,6 +2624,7 @@ def test_completion_cost_service_tier_for_anthropic(_local_model_cost_map):
|
|||
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={
|
||||
|
|
@ -2572,6 +2677,7 @@ def test_completion_cost_anthropic_auto_tier_uses_served_priority_rate(_local_mo
|
|||
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={
|
||||
|
|
@ -2665,6 +2771,7 @@ def test_completion_cost_non_string_service_tier_defers_to_served_tier(_local_mo
|
|||
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={
|
||||
|
|
@ -2714,6 +2821,7 @@ def test_completion_cost_non_string_response_service_tier_defers_to_served_tier(
|
|||
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={
|
||||
|
|
@ -2736,7 +2844,9 @@ def test_completion_cost_non_string_response_service_tier_defers_to_served_tier(
|
|||
},
|
||||
reasoning_content=None,
|
||||
)
|
||||
response = ModelResponse(usage=usage, model=model, service_tier={"name": "priority"})
|
||||
response = ModelResponse(
|
||||
usage=usage, model=model, service_tier={"name": "priority"}
|
||||
)
|
||||
|
||||
cost = completion_cost(
|
||||
completion_response=response,
|
||||
|
|
@ -2759,6 +2869,7 @@ def test_completion_cost_non_string_usage_service_tier_prices_standard(_local_mo
|
|||
"""
|
||||
from litellm import completion_cost
|
||||
|
||||
|
||||
model = "claude-test-usage-non-string-tier-cost-model"
|
||||
litellm.register_model(
|
||||
model_cost={
|
||||
|
|
@ -2805,6 +2916,7 @@ def test_anthropic_cost_per_token_prices_cache_at_served_tier_with_multiplier(_l
|
|||
)
|
||||
from litellm.types.utils import PromptTokensDetailsWrapper, Usage
|
||||
|
||||
|
||||
model = "claude-test-priority-cache-fast-model"
|
||||
litellm.register_model(
|
||||
model_cost={
|
||||
|
|
@ -2830,7 +2942,9 @@ def test_anthropic_cost_per_token_prices_cache_at_served_tier_with_multiplier(_l
|
|||
)
|
||||
usage.speed = "fast"
|
||||
|
||||
prompt_cost, completion_cost = anthropic_cost_per_token(model=model, usage=usage, service_tier="priority")
|
||||
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
|
||||
|
|
@ -2960,7 +3074,9 @@ def test_anthropic_fast_multiplier_only_on_models_with_fast_mode(_local_model_co
|
|||
"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):
|
||||
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
|
||||
|
|
@ -3025,26 +3141,28 @@ def test_gemini_cache_tokens_details_no_negative_values():
|
|||
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}"
|
||||
)
|
||||
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}"
|
||||
)
|
||||
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}"
|
||||
)
|
||||
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"
|
||||
)
|
||||
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")
|
||||
print(
|
||||
"✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative"
|
||||
)
|
||||
|
||||
|
||||
def test_gemini_without_cache_tokens_details():
|
||||
|
|
@ -3112,18 +3230,18 @@ def test_gemini_implicit_caching_cost_calculation():
|
|||
usage = VertexGeminiConfig._calculate_usage(completion_response)
|
||||
|
||||
# Verify parsing
|
||||
assert usage.cache_read_input_tokens == 8000, (
|
||||
f"cache_read_input_tokens should be 8000, got {usage.cache_read_input_tokens}"
|
||||
)
|
||||
assert usage.prompt_tokens_details.cached_tokens == 8000, (
|
||||
f"cached_tokens should be 8000, got {usage.prompt_tokens_details.cached_tokens}"
|
||||
)
|
||||
assert (
|
||||
usage.cache_read_input_tokens == 8000
|
||||
), f"cache_read_input_tokens should be 8000, got {usage.cache_read_input_tokens}"
|
||||
assert (
|
||||
usage.prompt_tokens_details.cached_tokens == 8000
|
||||
), f"cached_tokens should be 8000, got {usage.prompt_tokens_details.cached_tokens}"
|
||||
|
||||
# CRITICAL: text_tokens should be (10000 - 8000) = 2000, NOT 10000
|
||||
# This is the fix for issue #16341
|
||||
assert usage.prompt_tokens_details.text_tokens == 2000, (
|
||||
f"text_tokens should be 2000 (10000 - 8000), got {usage.prompt_tokens_details.text_tokens}"
|
||||
)
|
||||
assert (
|
||||
usage.prompt_tokens_details.text_tokens == 2000
|
||||
), f"text_tokens should be 2000 (10000 - 8000), got {usage.prompt_tokens_details.text_tokens}"
|
||||
|
||||
# Verify cost calculation uses cached token pricing
|
||||
response = ModelResponse(
|
||||
|
|
@ -3161,7 +3279,9 @@ def test_gemini_implicit_caching_cost_calculation():
|
|||
f"Cached tokens may not be using reduced pricing."
|
||||
)
|
||||
|
||||
print("✅ Issue #16341 fix verified: Gemini implicit caching cost calculated correctly")
|
||||
print(
|
||||
"✅ Issue #16341 fix verified: Gemini implicit caching cost calculated correctly"
|
||||
)
|
||||
|
||||
|
||||
def test_additional_costs_only_for_azure_ai(_local_model_cost_map):
|
||||
|
|
@ -3175,6 +3295,7 @@ def test_additional_costs_only_for_azure_ai(_local_model_cost_map):
|
|||
"""
|
||||
from litellm.cost_calculator import _get_additional_costs
|
||||
|
||||
|
||||
# Non-azure_ai providers should return None
|
||||
result = _get_additional_costs(
|
||||
model="gpt-4o",
|
||||
|
|
@ -3317,7 +3438,12 @@ def test_custom_pricing_applies_cache_creation_input_cost_via_prompt_details():
|
|||
},
|
||||
)
|
||||
|
||||
expected = (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125 + 100 * 0.000015
|
||||
expected = (
|
||||
(4000 - 1000 - 500) * 0.0000025
|
||||
+ 1000 * 0.00000025
|
||||
+ 500 * 0.000003125
|
||||
+ 100 * 0.000015
|
||||
)
|
||||
|
||||
assert cost == pytest.approx(expected)
|
||||
|
||||
|
|
@ -3362,7 +3488,9 @@ def test_custom_pricing_applies_cache_creation_input_cost_via_cache_write_tokens
|
|||
},
|
||||
)
|
||||
|
||||
expected_prompt = (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 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)
|
||||
|
|
@ -3402,7 +3530,10 @@ def test_extract_cache_read_tokens_zero_when_missing():
|
|||
|
||||
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
|
||||
assert (
|
||||
_extract_cache_read_tokens({"prompt_tokens_details": {"cached_tokens": None}})
|
||||
== 0
|
||||
)
|
||||
|
||||
|
||||
def test_extract_cache_creation_tokens_anthropic_top_level():
|
||||
|
|
@ -3444,7 +3575,12 @@ def test_extract_cache_creation_tokens_zero_when_missing():
|
|||
|
||||
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
|
||||
assert (
|
||||
_extract_cache_creation_tokens(
|
||||
{"prompt_tokens_details": {"cache_write_tokens": None}}
|
||||
)
|
||||
== 0
|
||||
)
|
||||
|
||||
|
||||
def test_custom_pricing_anthropic_style_cache_tokens_not_double_counted():
|
||||
|
|
@ -3571,6 +3707,7 @@ def test_completion_cost_logs_reasoning_and_cache_breakdown(_local_model_cost_ma
|
|||
from litellm.litellm_core_utils.litellm_logging import Logging
|
||||
from litellm.types.utils import Choices, CompletionTokensDetailsWrapper, Message
|
||||
|
||||
|
||||
logging_obj = Logging(
|
||||
model="gemini-2.5-flash",
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
|
|
@ -3597,8 +3734,12 @@ def test_completion_cost_logs_reasoning_and_cache_breakdown(_local_model_cost_ma
|
|||
prompt_tokens=209,
|
||||
completion_tokens=3996,
|
||||
total_tokens=4205,
|
||||
completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=3114, text_tokens=882),
|
||||
prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=100, text_tokens=109),
|
||||
completion_tokens_details=CompletionTokensDetailsWrapper(
|
||||
reasoning_tokens=3114, text_tokens=882
|
||||
),
|
||||
prompt_tokens_details=PromptTokensDetailsWrapper(
|
||||
cached_tokens=100, text_tokens=109
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
|
|
@ -3664,7 +3805,9 @@ def test_completion_cost_logs_the_rates_it_billed_at(monkeypatch):
|
|||
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["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)
|
||||
|
||||
|
||||
|
|
@ -3835,7 +3978,11 @@ def test_completion_cost_bills_interactions_api_response():
|
|||
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
|
||||
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
|
||||
|
||||
|
|
@ -4006,9 +4153,7 @@ def test_completion_cost_prices_anthropic_shaped_cache_read_tokens(_local_model_
|
|||
assert cost == pytest.approx(3 * 4e-6 + 4014 * 4e-7 + 5 * 2e-5, rel=1e-9)
|
||||
|
||||
|
||||
def _together_chat_response(
|
||||
model: str, prompt_tokens: int, completion_tokens: int, cached_tokens: int
|
||||
) -> ModelResponse:
|
||||
def _together_chat_response(model: str, prompt_tokens: int, completion_tokens: int, cached_tokens: int) -> ModelResponse:
|
||||
return ModelResponse(
|
||||
id="chatcmpl-together-cache",
|
||||
choices=[{"finish_reason": "stop", "index": 0, "message": {"content": "acknowledged", "role": "assistant"}}],
|
||||
|
|
@ -4076,8 +4221,6 @@ def test_completion_cost_together_metadata_only_model_still_uses_size_bucket(_lo
|
|||
)
|
||||
|
||||
assert cost == pytest.approx((23 + 15) * 8e-07, rel=1e-9)
|
||||
|
||||
|
||||
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.
|
||||
|
|
@ -4318,7 +4461,9 @@ def test_every_one_hour_cache_write_rate_is_double_its_input_rate():
|
|||
"""Guard against pasting one model's 1h cache-write price onto another: every provider
|
||||
LiteLLM tracks (Anthropic, Bedrock, Vertex, Azure) publishes the 1h write at 2x input."""
|
||||
|
||||
cost_map = json.loads((Path(__file__).parents[2] / "model_prices_and_context_window.json").read_text())
|
||||
cost_map = json.loads(
|
||||
(Path(__file__).parents[2] / "model_prices_and_context_window.json").read_text()
|
||||
)
|
||||
one_hour_prefix = "cache_creation_input_token_cost_above_1hr"
|
||||
deviations = {
|
||||
(name, key): (entry["input_cost_per_token" + key[len(one_hour_prefix) :]], entry[key])
|
||||
|
|
@ -4524,7 +4669,9 @@ def test_batch_cost_calculator_gpt_6_astra_bills_half_the_standard_rate(_local_m
|
|||
|
||||
usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
|
||||
|
||||
prompt_cost, completion_cost = batch_cost_calculator(usage=usage, model="gpt-6-astra", custom_llm_provider="openai")
|
||||
prompt_cost, completion_cost = batch_cost_calculator(
|
||||
usage=usage, model="gpt-6-astra", custom_llm_provider="openai"
|
||||
)
|
||||
|
||||
assert prompt_cost == pytest.approx(1000 * 5e-6)
|
||||
assert completion_cost == pytest.approx(500 * 2.5e-5)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue