Installation

uv add genai-prices

(or pip install genai-prices if you're old school)

To use the CLI with Rich output/help, install the optional CLI dependencies:

uv add "genai-prices[cli]"

(or pip install "genai-prices[cli]")

Warning: these prices will not be 100% accurate

See the project README for more information.

Usage

calc_price

from genai_prices import Usage, calc_price

price_data = calc_price(
    Usage(input_tokens=1000, output_tokens=100),
    model_ref='gpt-4o',
    provider_id='openai',
)
print(f"Total Price: ${price_data.total_price} (input: ${price_data.input_price}, output: ${price_data.output_price})")

extract_usage

extract_usage can be used to extract usage data and the model_ref from response data, which in turn can be used to calculate prices:

from genai_prices import extract_usage

response_data = {
    'model': 'claude-sonnet-4-20250514',
    'usage': {
        'input_tokens': 504,
        'cache_creation_input_tokens': 123,
        'cache_read_input_tokens': 0,
        'output_tokens': 97,
    },
}
extracted_usage = extract_usage(response_data, provider_id='anthropic')
price = extracted_usage.calc_price()
print(price.total_price)

or with OpenAI where there are two API flavors:

from genai_prices import extract_usage

response_data = {
    'model': 'gpt-5',
    'usage': {'prompt_tokens': 100, 'completion_tokens': 200},
}
extracted_usage = extract_usage(response_data, provider_id='openai', api_flavor='chat')
price = extracted_usage.calc_price()
print(price.total_price)

UpdatePrices

UpdatePrices can be used to periodically update the price data by downloading it from GitHub

Please note:

• this functionality is explicitly opt-in
• we download data directly from GitHub (https://raw.githubusercontent.com/pydantic/genai-prices/refs/heads/main/prices/new_data/v2/data.json) so we don't and can't monitor requests or gather telemetry

At the time of writing, the v2 data.json file downloaded by UpdatePrices is around 51KB when compressed, so is generally very quick to download.

By default UpdatePrices downloads price data immediately after it's started in the background, then every hour after that.

Usage with UpdatePrices as as context manager:

from genai_prices import UpdatePrices, Usage, calc_price

with UpdatePrices() as update_prices:
    update_prices.wait()  # optionally wait for prices to have updated
    p = calc_price(Usage(input_tokens=123, output_tokens=456), 'gpt-5')
    print(p)

Usage with UpdatePrices as a simple class:

from genai_prices import UpdatePrices, Usage, calc_price

update_prices = UpdatePrices()
update_prices.start(wait=True)  # start updating prices, optionally wait for prices to have updated
p = calc_price(Usage(input_tokens=123, output_tokens=456), 'gpt-5')
print(p)
update_prices.stop()  # stop updating prices

Only one UpdatePrices instance can be running at a time.

If you'd like to wait for prices to be updated without access to the UpdatePrices instance, you can use the wait_prices_updated_sync function:

from genai_prices import wait_prices_updated_sync

wait_prices_updated_sync()
...

Or it's async variant, wait_prices_updated_async.

CLI Usage

Run the CLI with:

uvx genai-prices --help

Or, if installed locally, make sure CLI extras are present:

pip install "genai-prices[cli]"
genai-prices --help

If local CLI extras are not installed, the command will print an install hint for genai-prices[cli].

To list providers and models, run:

uvx genai-prices list

To calculate the price of models, run for example:

uvx genai-prices calc --input-tokens 100000 --output-tokens 3000 o1 o3 claude-opus-4

CLI output notes:

• Rich output is the default.
• Use --plain (-p) for legacy/plain output.
• Use --no-color to keep rich formatting without colors.
• Use -T / --table for wide table output.

Further Documentation

We do not yet build API documentation for this package, but the source code is relatively simple and well documented.

If you need further information on the API, we encourage you to read the source code.

Fractional usage values

Every reportable usage unit accepts finite non-negative integers or fractional values. For example, duration usage can include fractions of a second:

from decimal import Decimal

from genai_prices import Usage

duration = Usage(audio_seconds=0.1) + Usage(audio_seconds=0.2)
exact_duration = Usage(audio_seconds=Decimal('0.1')) + Usage(audio_seconds=0.2)

assert duration.audio_seconds == 0.3
assert type(duration.audio_seconds) is float
assert exact_duration.audio_seconds == Decimal('0.3')
assert type(Usage(audio_seconds=3.0).audio_seconds) is float

Python preserves supplied int, float, and Decimal values, including 3.0 as a float. Other accepted non-boolean integer implementations normalize to built-in int. Arithmetic interprets floats through their shortest round-trippable decimal spellings: a result is Decimal if any operand was Decimal, otherwise float if any operand was float, otherwise int. This cannot recover decimal precision that was already lost before a float reached Usage.

Standard JSON decoding normally supplies int and float, while callers may request Decimal parsing before extraction. Decimal-bearing usage is not serializable by Python's standard JSON encoder without a custom conversion.