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Project description

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

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