tokencost 0.0.2

To calculate token and translated USD cost of string and message calls to OpenAI, for example when used by AI agents

TokenCost

Overview

TokenCost is a specialized tool designed for calculating the USD cost of using major Large Language Models (LLMs) APIs.

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 = "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}")
# 30 + 140 = 170
# In TPUs (token price units), which is 1/10,000,000th of a USD.

Installation

Recommended: PyPI:

pip install tokencost

Usage

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 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}")
# 180 + 100 = 280

from tokencost import USD_PER_TPU
print(f"Cost USD: ${(prompt_cost + completion_cost)/USD_PER_TPU}")
# $2.8e-05

Calculating cost using string prompts instead of messages:

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/USD_PER_TPU}")
# Cost: $3e-06

Counting tokens

from tokencost import count_message_tokens, count_string_tokens
prompt = [{ "role": "user", "content": "Say this is a test"}]
prompt_message_token_count = count_message_tokens(prompt, model)
print(f"{prompt_message_token_count=}")
# prompt_message_token_count=12

completion_string_token_count = count_string_tokens(response, model)
print(f"{completion_string_token_count=}")
# completion_string_token_count=7

Cost table

Units denominated in TPUs (Token Price Units = 1/10,000,000 USD)

Model Name	Prompt Cost	Completion Cost	Max Prompt Tokens
gpt-3.5-turbo	15	20	4097
gpt-3.5-turbo-0301	15	20	4097
gpt-3.5-turbo-0613	15	20	4097
gpt-3.5-turbo-16k	30	40	16385
gpt-3.5-turbo-16k-0613	30	40	16385
gpt-3.5-turbo-1106	10	20	16385
gpt-3.5-turbo-instruct	15	20	4096
gpt-4	300	600	8192
gpt-4-0314	300	600	8192
gpt-4-0613	300	600	8192
gpt-4-32k	600	1200	32768
gpt-4-32k-0314	600	1200	32768
gpt-4-32k-0613	600	1200	32768
gpt-4-1106-preview	100	300	128000
gpt-4-vision-preview	100	300	128000
text-embedding-ada-002	1	N/A	8192

Running locally

Installation via GitHub:

git clone [e-mail]:AgentOps-AI/tokencost.git
cd tokencost
pip install -e .

Running tests

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

pip install pytest

1. 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.