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chore(openai): drop commented-out legacy cost_per_token implementation

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Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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mateo 2026-09-17 20:03:05 +00:00
parent decbb96382
commit ea109cd5c6
1 changed files with 0 additions and 44 deletions
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44
litellm/llms/openai/cost_calculation.py
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@ -38,7 +38,6 @@ def cost_per_token(
Returns:
Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
"""
## CALCULATE INPUT COST
return generic_cost_per_token(
model=model,
usage=usage,
@ -46,49 +45,6 @@ def cost_per_token(
service_tier=service_tier,
data_residency=data_residency,
)
# ### Non-cached text tokens
# non_cached_text_tokens = usage.prompt_tokens
# cached_tokens: Optional[int] = None
# if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens:
# cached_tokens = usage.prompt_tokens_details.cached_tokens
# non_cached_text_tokens = non_cached_text_tokens - cached_tokens
# prompt_cost: float = non_cached_text_tokens * model_info["input_cost_per_token"]
# ## Prompt Caching cost calculation
# if model_info.get("cache_read_input_token_cost") is not None and cached_tokens:
# # Note: We read ._cache_read_input_tokens from the Usage - since cost_calculator.py standardizes the cache read tokens on usage._cache_read_input_tokens
# prompt_cost += cached_tokens * (
# model_info.get("cache_read_input_token_cost", 0) or 0
# )
# _audio_tokens: Optional[int] = (
# usage.prompt_tokens_details.audio_tokens
# if usage.prompt_tokens_details is not None
# else None
# )
# _audio_cost_per_token: Optional[float] = model_info.get(
# "input_cost_per_audio_token"
# )
# if _audio_tokens is not None and _audio_cost_per_token is not None:
# audio_cost: float = _audio_tokens * _audio_cost_per_token
# prompt_cost += audio_cost
# ## CALCULATE OUTPUT COST
# completion_cost: float = (
# usage["completion_tokens"] * model_info["output_cost_per_token"]
# )
# _output_cost_per_audio_token: Optional[float] = model_info.get(
# "output_cost_per_audio_token"
# )
# _output_audio_tokens: Optional[int] = (
# usage.completion_tokens_details.audio_tokens
# if usage.completion_tokens_details is not None
# else None
# )
# if _output_cost_per_audio_token is not None and _output_audio_tokens is not None:
# audio_cost = _output_audio_tokens * _output_cost_per_audio_token
# completion_cost += audio_cost
# return prompt_cost, completion_cost
def cost_per_second(model: str, custom_llm_provider: str | None, duration: float = 0.0) -> tuple[float, float]: