LLM Model Cost Calculator

A Python package to calculate token costs for various LLM models based on input and output tokens. Prices come from LiteLLM's public model_prices_and_context_window.json, fetched on first use.

Installation

pip install llm_model_cost

https://pypi.org/project/llm-model-cost/

Usage

from llm_model_cost import ModelCost

# Calculate cost for a model
costInfo = ModelCost(name="gpt-4o", input_tokens=1000, output_tokens=500)

# Get cost details
print(f"Input cost: ${costInfo.input_cost:.6f}")
print(f"Output cost: ${costInfo.output_cost:.6f}")
print(f"Total cost: ${costInfo.cost:.6f}")

# Prompt caching: input_tokens is the FULL prompt, cached_tokens the subset served from cache
#   OpenAI:    input_tokens=usage.prompt_tokens, cached_tokens=usage.prompt_tokens_details.cached_tokens
#   Anthropic: input_tokens=input_tokens + cache_read_input_tokens + cache_creation_input_tokens,
#              cached_tokens=cache_read_input_tokens, cache_creation_tokens=cache_creation_input_tokens
costInfo = ModelCost(name="gpt-4o", input_tokens=10_000, output_tokens=500, cached_tokens=8_000)
print(costInfo.uncached_input_cost, costInfo.cached_input_cost, costInfo.cache_creation_cost)

# Get model information
model_info = ModelCost.get_model_info("gpt-4o")
print(f"Max tokens: {model_info['max_tokens']}")
print(f"Provider: {model_info['litellm_provider']}")

# List all available models
models = ModelCost.list_models()

How the cost is computed

• input_cost = uncached_tokens * input_rate + cached_tokens * cache_read_rate + cache_creation_tokens * cache_creation_rate
• output_cost = output_tokens * output_rate (reasoning tokens are part of output_tokens)
• Long-context tiers (*_above_200k_tokens etc.) apply to the whole request once input_tokens exceeds the threshold, matching provider billing.
• Models with no cache price bill cached tokens at the normal input rate (no discount known).
• ValueError is raised for unknown models, models not priced per token (image / audio / TTS), negative counts, or cached_tokens + cache_creation_tokens > input_tokens.

Not covered: batch / flex / priority service tiers, per-image / per-second / per-character pricing, tool-call surcharges (web search, code interpreter).

Features

• Calculate token costs for various LLM models, including prompt-cache reads/writes
• Get model information including max tokens, costs, and provider details
• List all available models and their capabilities
• Automatic updates from the latest pricing data

Development

python -m unittest discover tests