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| """ | |
| Helper util for handling azure openai-specific cost calculation | |
| - e.g.: prompt caching | |
| """ | |
| from typing import Optional, Tuple | |
| from litellm._logging import verbose_logger | |
| from litellm.types.utils import Usage | |
| from litellm.utils import get_model_info | |
| def cost_per_token( | |
| model: str, usage: Usage, response_time_ms: Optional[float] = 0.0 | |
| ) -> Tuple[float, float]: | |
| """ | |
| Calculates the cost per token for a given model, prompt tokens, and completion tokens. | |
| Input: | |
| - model: str, the model name without provider prefix | |
| - usage: LiteLLM Usage block, containing anthropic caching information | |
| Returns: | |
| Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd | |
| """ | |
| ## GET MODEL INFO | |
| model_info = get_model_info(model=model, custom_llm_provider="azure") | |
| cached_tokens: Optional[int] = None | |
| ## CALCULATE INPUT COST | |
| non_cached_text_tokens = usage.prompt_tokens | |
| 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"] | |
| ## CALCULATE OUTPUT COST | |
| completion_cost: float = ( | |
| usage["completion_tokens"] * model_info["output_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 | |
| ) | |
| ## Speech / Audio cost calculation | |
| if ( | |
| "output_cost_per_second" in model_info | |
| and model_info["output_cost_per_second"] is not None | |
| and response_time_ms is not None | |
| ): | |
| verbose_logger.debug( | |
| f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}" | |
| ) | |
| ## COST PER SECOND ## | |
| prompt_cost = 0 | |
| completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000 | |
| return prompt_cost, completion_cost | |