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fix(cost_calculator): narrow exception types and add second test case
- First guard: catch litellm.exceptions.BadRequestError from get_llm_provider() - Provider dispatch: lift ValueError guard to cover all provider-specific calls (get_model_info() wraps ValueError as Exception, so guard catches both) - Add test_cost_per_token_returns_zero_with_explicit_provider covering the case where custom_llm_provider is given but model is not in the cost map Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Parinith
parent
b3fc2aa4d4
commit
7ed463e141
2 changed files with 133 additions and 106 deletions
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@ -352,7 +352,11 @@ def cost_per_token( # noqa: PLR0915
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else:
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try:
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_, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
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except Exception:
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except litellm.exceptions.BadRequestError:
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verbose_logger.debug(
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"cost_per_token: unknown model=%s, no provider inferred. Returning 0.0, 0.0",
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model,
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)
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return 0.0, 0.0
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model_without_prefix = model
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model_parts = model.split("/", 1)
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@ -479,129 +483,138 @@ def cost_per_token( # noqa: PLR0915
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else None
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),
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)
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elif custom_llm_provider == "vertex_ai":
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cost_router = google_cost_router(
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model=model_without_prefix,
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custom_llm_provider=custom_llm_provider,
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call_type=call_type,
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)
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if cost_router == "cost_per_character":
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return google_cost_per_character(
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# All call-type branches above return explicitly. Execution reaches here only
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# for standard completion calls. Wrap the provider dispatch in a single
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# ValueError guard so that any "model not in cost map" error from any
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# provider's cost function returns (0.0, 0.0) instead of raising.
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try:
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if custom_llm_provider == "vertex_ai":
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cost_router = google_cost_router(
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model=model_without_prefix,
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custom_llm_provider=custom_llm_provider,
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prompt_characters=prompt_characters,
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completion_characters=completion_characters,
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usage=usage_block,
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call_type=call_type,
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)
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elif cost_router == "cost_per_token":
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return google_cost_per_token(
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model=model_without_prefix,
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custom_llm_provider=custom_llm_provider,
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if cost_router == "cost_per_character":
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return google_cost_per_character(
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model=model_without_prefix,
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custom_llm_provider=custom_llm_provider,
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prompt_characters=prompt_characters,
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completion_characters=completion_characters,
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usage=usage_block,
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)
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elif cost_router == "cost_per_token":
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return google_cost_per_token(
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model=model_without_prefix,
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custom_llm_provider=custom_llm_provider,
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usage=usage_block,
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service_tier=service_tier,
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)
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elif custom_llm_provider == "anthropic":
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return anthropic_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "bedrock":
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return bedrock_cost_per_token(
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model=model, usage=usage_block, service_tier=service_tier
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)
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elif custom_llm_provider == "openai":
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return openai_cost_per_token(
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model=model, usage=usage_block, service_tier=service_tier
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)
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elif custom_llm_provider == "databricks":
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return databricks_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "fireworks_ai":
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return fireworks_ai_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "azure":
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return azure_openai_cost_per_token(
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model=model,
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usage=usage_block,
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response_time_ms=response_time_ms,
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service_tier=service_tier,
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)
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elif custom_llm_provider == "anthropic":
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return anthropic_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "bedrock":
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return bedrock_cost_per_token(
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model=model, usage=usage_block, service_tier=service_tier
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)
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elif custom_llm_provider == "openai":
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return openai_cost_per_token(
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model=model, usage=usage_block, service_tier=service_tier
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)
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elif custom_llm_provider == "databricks":
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return databricks_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "fireworks_ai":
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return fireworks_ai_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "azure":
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return azure_openai_cost_per_token(
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model=model,
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usage=usage_block,
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response_time_ms=response_time_ms,
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service_tier=service_tier,
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)
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elif custom_llm_provider == "gemini":
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return gemini_cost_per_token(
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model=model, usage=usage_block, service_tier=service_tier
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)
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elif custom_llm_provider == "deepseek":
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return deepseek_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "perplexity":
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return perplexity_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "xai":
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return xai_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "lemonade":
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return lemonade_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "dashscope":
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from litellm.llms.dashscope.cost_calculator import (
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cost_per_token as dashscope_cost_per_token,
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)
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elif custom_llm_provider == "gemini":
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return gemini_cost_per_token(
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model=model, usage=usage_block, service_tier=service_tier
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)
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elif custom_llm_provider == "deepseek":
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return deepseek_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "perplexity":
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return perplexity_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "xai":
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return xai_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "lemonade":
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return lemonade_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "dashscope":
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from litellm.llms.dashscope.cost_calculator import (
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cost_per_token as dashscope_cost_per_token,
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)
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return dashscope_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "azure_ai":
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return azure_ai_cost_per_token(
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model=model,
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usage=usage_block,
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response_time_ms=response_time_ms,
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request_model=request_model,
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service_tier=service_tier,
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)
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else:
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try:
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return dashscope_cost_per_token(model=model, usage=usage_block)
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elif custom_llm_provider == "azure_ai":
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return azure_ai_cost_per_token(
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model=model,
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usage=usage_block,
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response_time_ms=response_time_ms,
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request_model=request_model,
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service_tier=service_tier,
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)
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else:
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model_info = _cached_get_model_info_helper(
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model=model, custom_llm_provider=custom_llm_provider
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)
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except Exception:
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return 0.0, 0.0
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if (model_info.get("input_cost_per_token") or 0.0) > 0 or (
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model_info.get("output_cost_per_token") or 0.0
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) > 0:
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return generic_cost_per_token(
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model=model,
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usage=usage_block,
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custom_llm_provider=custom_llm_provider,
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service_tier=service_tier,
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)
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if (model_info.get("input_cost_per_token") or 0.0) > 0 or (
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model_info.get("output_cost_per_token") or 0.0
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) > 0:
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return generic_cost_per_token(
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model=model,
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usage=usage_block,
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custom_llm_provider=custom_llm_provider,
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service_tier=service_tier,
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)
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if (
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model_info.get("input_cost_per_second", None) is not None
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and response_time_ms is not None
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):
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verbose_logger.debug(
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"For model=%s - input_cost_per_second: %s; response time: %s",
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model,
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model_info.get("input_cost_per_second", None),
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response_time_ms,
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)
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## COST PER SECOND ##
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prompt_tokens_cost_usd_dollar = (
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model_info["input_cost_per_second"] * response_time_ms / 1000 # type: ignore
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)
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if (
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model_info.get("output_cost_per_second", None) is not None
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and response_time_ms is not None
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):
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verbose_logger.debug(
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"For model=%s - output_cost_per_second: %s; response time: %s",
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model,
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model_info.get("output_cost_per_second", None),
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response_time_ms,
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)
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## COST PER SECOND ##
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completion_tokens_cost_usd_dollar = (
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model_info["output_cost_per_second"] * response_time_ms / 1000 # type: ignore
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)
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if (
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model_info.get("input_cost_per_second", None) is not None
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and response_time_ms is not None
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):
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verbose_logger.debug(
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"For model=%s - input_cost_per_second: %s; response time: %s",
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"Returned custom cost for model=%s - prompt_tokens_cost_usd_dollar: %s, completion_tokens_cost_usd_dollar: %s",
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model,
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model_info.get("input_cost_per_second", None),
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response_time_ms,
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prompt_tokens_cost_usd_dollar,
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completion_tokens_cost_usd_dollar,
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)
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## COST PER SECOND ##
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prompt_tokens_cost_usd_dollar = (
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model_info["input_cost_per_second"] * response_time_ms / 1000 # type: ignore
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)
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if (
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model_info.get("output_cost_per_second", None) is not None
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and response_time_ms is not None
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):
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verbose_logger.debug(
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"For model=%s - output_cost_per_second: %s; response time: %s",
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model,
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model_info.get("output_cost_per_second", None),
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response_time_ms,
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)
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## COST PER SECOND ##
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completion_tokens_cost_usd_dollar = (
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model_info["output_cost_per_second"] * response_time_ms / 1000 # type: ignore
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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except (ValueError, Exception):
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verbose_logger.debug(
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"Returned custom cost for model=%s - prompt_tokens_cost_usd_dollar: %s, completion_tokens_cost_usd_dollar: %s",
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"cost_per_token: model=%s not in cost map for provider=%s. Returning 0.0, 0.0",
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model,
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prompt_tokens_cost_usd_dollar,
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completion_tokens_cost_usd_dollar,
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custom_llm_provider,
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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return 0.0, 0.0
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def get_replicate_completion_pricing(completion_response: dict, total_time=0.0):
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@ -2067,3 +2067,17 @@ def test_cost_per_token_returns_zero_for_unknown_model():
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"""
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result = cost_per_token(model="fake-model-xyz-123")
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assert result == (0.0, 0.0)
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def test_cost_per_token_returns_zero_with_explicit_provider():
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"""
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cost_per_token() must return (0.0, 0.0) when a known provider is supplied
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but the model is not in the cost map (GitHub issue #27581).
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"""
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result = litellm.cost_per_token(
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model="fake-model-xyz-123",
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custom_llm_provider="openai",
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prompt_tokens=10,
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completion_tokens=5,
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)
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assert result == (0.0, 0.0)
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