diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9ca8d9e1bac..2a4e65ca2be 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -12282,7 +12282,7 @@ }, "claude-3-haiku-20240307": { "cache_creation_input_token_cost": 3e-07, - "cache_creation_input_token_cost_above_1hr": 6e-06, + "cache_creation_input_token_cost_above_1hr": 5e-07, "cache_read_input_token_cost": 3e-08, "deprecation_date": "2026-04-20", "input_cost_per_token": 2.5e-07, @@ -12301,7 +12301,7 @@ }, "claude-3-opus-20240229": { "cache_creation_input_token_cost": 1.875e-05, - "cache_creation_input_token_cost_above_1hr": 6e-06, + "cache_creation_input_token_cost_above_1hr": 3e-05, "cache_read_input_token_cost": 1.5e-06, "deprecation_date": "2026-01-05", "input_cost_per_token": 1.5e-05, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 9ca8d9e1bac..2a4e65ca2be 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -12282,7 +12282,7 @@ }, "claude-3-haiku-20240307": { "cache_creation_input_token_cost": 3e-07, - "cache_creation_input_token_cost_above_1hr": 6e-06, + "cache_creation_input_token_cost_above_1hr": 5e-07, "cache_read_input_token_cost": 3e-08, "deprecation_date": "2026-04-20", "input_cost_per_token": 2.5e-07, @@ -12301,7 +12301,7 @@ }, "claude-3-opus-20240229": { "cache_creation_input_token_cost": 1.875e-05, - "cache_creation_input_token_cost_above_1hr": 6e-06, + "cache_creation_input_token_cost_above_1hr": 3e-05, "cache_read_input_token_cost": 1.5e-06, "deprecation_date": "2026-01-05", "input_cost_per_token": 1.5e-05, diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index 1f1c9be973f..3d0921c6fd8 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -1,4 +1,7 @@ +import json +from pathlib import Path + import pytest @@ -14,7 +17,13 @@ from litellm.cost_calculator import ( response_cost_calculator, ) from litellm.types.llms.openai import OpenAIRealtimeStreamList -from litellm.types.utils import ModelInfo, ModelResponse, PromptTokensDetailsWrapper, Usage +from litellm.types.utils import ( + CacheCreationTokenDetails, + ModelInfo, + ModelResponse, + PromptTokensDetailsWrapper, + Usage, +) from litellm.utils import TranscriptionResponse @@ -3781,3 +3790,51 @@ 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) + + +@pytest.mark.parametrize( + ("model", "expected_1hr_rate"), + [("claude-3-haiku-20240307", 5e-07), ("claude-3-opus-20240229", 3e-05)], +) +def test_claude_3_one_hour_cache_writes_bill_at_double_input( + _local_model_cost_map, model: str, expected_1hr_rate: float +): + """Regression: both models carried the Sonnet 1h cache-write rate (6e-06) instead of + 2x their own input price, overbilling haiku 12x and underbilling opus 5x.""" + + usage = Usage( + prompt_tokens=1000, + completion_tokens=0, + total_tokens=1000, + prompt_tokens_details=PromptTokensDetailsWrapper( + cached_tokens=0, + cache_creation_tokens=1000, + cache_creation_token_details=CacheCreationTokenDetails( + ephemeral_5m_input_tokens=0, ephemeral_1h_input_tokens=1000 + ), + ), + ) + + prompt_cost, _ = cost_per_token(model=model, usage_object=usage, custom_llm_provider="anthropic") + + assert prompt_cost == pytest.approx(1000 * expected_1hr_rate, rel=1e-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() + ) + 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]) + for name, entry in cost_map.items() + if isinstance(entry, dict) + for key in entry + if key.startswith(one_hour_prefix) + and entry[key] != pytest.approx(2 * entry["input_cost_per_token" + key[len(one_hour_prefix) :]], rel=1e-9) + } + + assert deviations == {}