tokenary

Minimal Python library to calculate LLM API costs based on the LiteLLM model catalog.

Installation

pip install tokenary

Usage

Functional API

import tokenary
from tokenary import ModelName

result = tokenary.calculate(
    model=ModelName.AZURE_GPT_3_5_TURBO,
    input_tokens=1000,
    output_tokens=500,
)

print(result.total_cost)
print(result.model_dump())

Request object

from tokenary import ModelName, UsageCostRequest, calculate

request = UsageCostRequest(
    model=ModelName.AZURE_GPT_3_5_TURBO,
    input_tokens=2000,
    output_tokens=800,
    reasoning_tokens=200,
)

result = calculate(request)
print(result.model_dump())

Reasoning tokens (e.g. o1)

from tokenary import ModelName, calculate

result = calculate(
    model=ModelName.O1,
    input_tokens=500,
    output_tokens=200,
    reasoning_tokens=300,
)

print(f"Total: ${result.total_cost:.6f}")
print(f"  Reasoning: ${result.reasoning_cost:.6f}")

All supported parameters

Parameter	Type	Description
model	ModelName	Model identifier
input_tokens	int	Number of input tokens
output_tokens	int	Number of output tokens
reasoning_tokens	int	Reasoning tokens (e.g. o1)
audio_input_tokens	int	Audio input tokens
generated_images	int	Number of generated images
code_interpreter_sessions	int	Code interpreter sessions
file_search_calls	int	File search API calls
file_search_gb_days	float	File search storage (GB-days)
vector_store_gb_days	float	Vector store storage (GB-days)

The returned CostBreakdown object contains per-category costs (input_cost, output_cost, reasoning_cost, …) and a total_cost, all in USD.

Generate pricing artifacts

Re-generate the bundled pricing data from the LiteLLM catalog:

python -m tokenary.generator