explaingit

ambrosex/openai_count_tokens

Analysis updated 2026-08-04 · repo last pushed 2023-04-24

PythonAudience · developerComplexity · 2/5DormantSetup · easy

TLDR

A small web service that counts how many tokens a piece of text will use before you send it to OpenAI models like GPT-3.5-turbo, helping you predict API costs and avoid exceeding model input limits.

Mindmap

mindmap
  root((repo))
    What it does
      Counts tokens in text
      Supports multilingual text
      Returns token count per model
    Tech stack
      Python
      Docker
      Tokenization libraries
      Web service on port 8001
    Use cases
      Budget API costs
      Warn users near token limits
      Check prompt fits model max size
    Audience
      Founders budgeting costs
      Developers building on OpenAI
      App creators needing pre-checks

Code map

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What do people build with it?

USE CASE 1

Estimate OpenAI API costs before sending requests by checking token counts upfront.

USE CASE 2

Build a feature that warns users when their input is approaching a model's token limit.

USE CASE 3

Verify that a long prompt fits within a model's maximum input size before submitting it.

What is it built with?

PythonDocker

How does it compare?

ambrosex/openai_count_tokens0verflowme/learnings0verflowme/r2ai
LanguagePythonPythonPython
Last pushed2023-04-242022-06-182025-11-19
MaintenanceDormantDormantQuiet
Setup difficultyeasyeasymoderate
Complexity2/51/53/5
Audiencedeveloperresearcherdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Run via Docker and access the local endpoint on port 8001, no external API keys or complex configuration needed.

The README does not mention a license, so it is unclear what permissions you have to use or modify this code.

In plain English

openai_count_tokens is a small utility that counts how many "tokens" a piece of text will use when sent to OpenAI models like GPT-3.5-turbo. This matters because OpenAI charges by the token rather than by the word, and a token is roughly a piece of a word, so keeping track of token counts helps you predict costs before making API calls. The project runs as a simple web service. You send it a string of text and tell it which model you're targeting, and it replies with the number of tokens that text contains. For example, a 60-character sample with some Chinese characters returns 28 tokens. The service handles multilingual text and special characters without issue, so you don't have to worry about text complexity skewing the count. Anyone building apps on top of OpenAI's API would find this useful. If you're a founder budgeting for chat costs or a developer building a feature that warns users when they're approaching a token limit, this gives you a reliable pre-check. It could also help when you need to make sure a prompt fits within a model's maximum input size before submitting it. The README keeps setup brief: you can run it through Docker and access it via a local endpoint on port 8001. The code itself relies on well-established libraries for tokenization and web serving, so the counting logic should match what OpenAI actually uses. The README doesn't go into much detail beyond the basic usage example and setup instructions, so you may need to explore the code or experiment with it to understand advanced options or edge cases.

Copy-paste prompts

Prompt 1
Set up openai_count_tokens using Docker and write a Python script that sends a sample prompt to the local endpoint on port 8001, then prints the returned token count.
Prompt 2
Use openai_count_tokens to build a small cost estimator: send a batch of prompts to the service, collect token counts, and calculate the estimated total cost for GPT-3.5-turbo at current pricing.
Prompt 3
Integrate openai_count_tokens into my chat app so that before a user submits a message, the service checks the token count and shows a warning if the input exceeds 2,000 tokens.
Prompt 4
Test openai_count_tokens with multilingual text including English and Chinese characters, and compare the returned token counts to see how different languages affect token usage.

Frequently asked questions

What is openai_count_tokens?

A small web service that counts how many tokens a piece of text will use before you send it to OpenAI models like GPT-3.5-turbo, helping you predict API costs and avoid exceeding model input limits.

What language is openai_count_tokens written in?

Mainly Python. The stack also includes Python, Docker.

Is openai_count_tokens actively maintained?

Dormant — no commits in 2+ years (last push 2023-04-24).

What license does openai_count_tokens use?

The README does not mention a license, so it is unclear what permissions you have to use or modify this code.

How hard is openai_count_tokens to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is openai_count_tokens for?

Mainly developer.

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