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https://github.com/BerriAI/litellm.git
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dee18cbf31
* add cache_read_input_token_cost for prompt caching models * add prompt caching for latest models * add openai cost calculator * add openai prompt caching test * fix lint check * add not on how usage._cache_read_input_tokens is used * fix cost calc whisper openai * use output_cost_per_second * add input_cost_per_second
68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
"""
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Helper util for handling openai-specific cost calculation
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- e.g.: prompt caching
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"""
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from typing import Optional, Tuple
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from litellm._logging import verbose_logger
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from litellm.types.utils import Usage
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from litellm.utils import get_model_info
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def cost_per_token(
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model: str, usage: Usage, response_time_ms: Optional[float] = 0.0
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) -> Tuple[float, float]:
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"""
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Calculates the cost per token for a given model, prompt tokens, and completion tokens.
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Input:
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- model: str, the model name without provider prefix
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- usage: LiteLLM Usage block, containing anthropic caching information
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Returns:
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Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
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"""
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## GET MODEL INFO
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model_info = get_model_info(model=model, custom_llm_provider="openai")
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## CALCULATE INPUT COST
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prompt_cost: float = usage["prompt_tokens"] * model_info["input_cost_per_token"]
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## CALCULATE OUTPUT COST
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completion_cost: float = (
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usage["completion_tokens"] * model_info["output_cost_per_token"]
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)
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## Prompt Caching cost calculation
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if model_info.get("cache_read_input_token_cost") is not None:
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# 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
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prompt_cost += usage._cache_read_input_tokens * (
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model_info.get("cache_read_input_token_cost", 0) or 0
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)
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## Speech / Audio cost calculation
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if (
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"output_cost_per_second" in model_info
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and model_info["output_cost_per_second"] 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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f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}"
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)
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## COST PER SECOND ##
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prompt_cost = 0
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completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000
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elif (
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"input_cost_per_second" in model_info
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and model_info["input_cost_per_second"] 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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f"For model={model} - input_cost_per_second: {model_info.get('input_cost_per_second')}; response time: {response_time_ms}"
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)
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## COST PER SECOND ##
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prompt_cost = model_info["input_cost_per_second"] * response_time_ms / 1000
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completion_cost = 0.0
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return prompt_cost, completion_cost
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