Clientside token counting + price estimation for LLM apps and AI agents.

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TokenCost

Tokencost helps calculate the USD cost of using major Large Language Model (LLMs) APIs by calculating the estimated cost of prompts and completions.

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Features

• LLM Price Tracking Major LLM providers frequently add new models and update pricing. This repo helps track the latest price changes
• Token counting Accurately count prompt tokens before sending OpenAI requests
• Easy integration Get the cost of a prompt or completion with a single function

Example usage:

from tokencost import calculate_prompt_cost, calculate_completion_cost

model = "gpt-3.5-turbo"
prompt = [{ "role": "user", "content": "Hello world"}]
completion = "How may I assist you today?"

prompt_cost = calculate_prompt_cost(prompt, model)
completion_cost = calculate_completion_cost(completion, model)

print(f"{prompt_cost} + {completion_cost} = {prompt_cost + completion_cost}")
# 0.0000135 + 0.000014 = 0.0000275

Installation

Recommended: PyPI:

pip install tokencost

Usage

Cost estimates

Calculating the cost of prompts and completions from OpenAI requests

from openai import OpenAI

client = OpenAI()
model = "gpt-3.5-turbo"
prompt = [{ "role": "user", "content": "Say this is a test"}]

chat_completion = client.chat.completions.create(
    messages=prompt, model=model
)

completion = chat_completion.choices[0].message.content
# "This is a test."

prompt_cost = calculate_prompt_cost(prompt, model)
completion_cost = calculate_completion_cost(completion, model)
print(f"{prompt_cost} + {completion_cost} = {prompt_cost + completion_cost}")
# 0.0000180 + 0.000010 = 0.0000280

Calculating cost using string prompts instead of messages:

from tokencost import calculate_prompt_cost

prompt_string = "Hello world" 
response = "How may I assist you today?"
model= "gpt-3.5-turbo"

prompt_cost = calculate_prompt_cost(prompt_string, model)
print(f"Cost: ${prompt_cost}")
# Cost: $3e-06

Counting tokens

from tokencost import count_message_tokens, count_string_tokens

message_prompt = [{ "role": "user", "content": "Hello world"}]
# Counting tokens in prompts formatted as message lists
print(count_message_tokens(message_prompt, model="gpt-3.5-turbo"))
# 9

# Alternatively, counting tokens in string prompts
print(count_string_tokens(prompt="Hello world", model="gpt-3.5-turbo"))
# 2

Cost table

Units denominated in USD. All prices can be located in model_prices.json.

• Prices last updated Jan 30, 2024 from LiteLLM's cost dictionary

Callback handlers

You may also calculate token costs in LLM wrapper/framework libraries using callbacks.

LlamaIndex

pip install `'tokencost[llama-index]'`

To use the base callback handler, you may import it:

from tokencost.callbacks.llama_index import TokenCostHandler

and pass to your framework callback handler.

Langchain

(Coming Soon)

Running locally

Installation via GitHub:

git clone git@github.com:AgentOps-AI/tokencost.git
cd tokencost
pip install -e .

Running tests

1. Install pytest if you don't have it already

pip install pytest

2. Run the tests/ folder while in the parent directory

pytest tests

This repo also supports tox, simply run python -m tox.

Contributing

Contributions to TokenCost are welcome! Feel free to create an issue for any bug reports, complaints, or feature suggestions.

License

TokenCost is released under the MIT License.