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what is tiktoken fr?

openai/tiktoken — explained in plain English

Analysis updated 2026-06-21

18,191PythonAudience · developerComplexity · 2/5Setup · easy

tl;dr

Tiktoken is a fast Python tokenizer for OpenAI language models that converts text to token numbers and back, 3-6x faster than comparable tools, useful for counting tokens before sending API requests.

vibe map

mindmap
  root((repo))
    What it does
      Text tokenization
      Token counting
      Encode and decode
    How it works
      Byte Pair Encoding
      Subword splitting
      Reversible encoding
    Use cases
      Count tokens before API call
      Chunk long documents
      Custom special tokens
    Audience
      AI developers
      Data engineers

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filefunction / class

what do people make with this?

VIBE 1

Count the tokens in a prompt before sending it to an OpenAI API call to avoid exceeding the model's context limit.

VIBE 2

Split a long document into chunks that each fit within a model's token window for reliable batch processing.

VIBE 3

Encode text into token IDs as part of a preprocessing pipeline for fine-tuning or embedding workflows.

what's the stack?

Pythonpip

how it stacks up fr

openai/tiktokenmikf/gallery-dlstate-spaces/mamba
Stars18,19118,15218,240
LanguagePythonPythonPython
Setup difficultyeasyeasymoderate
Complexity2/52/54/5
Audiencedeveloperdeveloperresearcher

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

how do i run it?

Difficulty · easy time til it works · 5min

in plain english

Tiktoken is a fast tokenizer for use with OpenAI's language models. Tokenization is the process of converting text into numbers before feeding it to an AI model, language models do not process words or characters directly, but instead work with chunks called tokens. A token typically corresponds to about four characters of English text, though the exact mapping depends on the encoding. Tiktoken implements Byte Pair Encoding (BPE), an algorithm that learns to split text into common subword chunks based on frequency in training data. This approach is both reversible (tokens can be decoded back to the original text) and lossless, and it handles arbitrary text including content the tokenizer has never seen before. Because common subwords like "ing" appear as single tokens, models can generalize better about language patterns. The library is between three and six times faster than comparable tokenizers, making it practical for applications that need to count tokens or process large amounts of text quickly. It can be installed via pip and used in Python. Tiktoken includes functions to get the tokenizer for a specific OpenAI model, to encode and decode text, and to extend the tokenizer with custom special tokens or entirely new encodings via a plugin system. An educational submodule is also included for learning how BPE works step by step.

prompts (copy fr)

prompt 1
I'm calling GPT-4o and need to check token count before each request to stay under the limit. Show me tiktoken Python code to count tokens for the gpt-4o model.
prompt 2
I have a 200-page PDF I've converted to text. Write a Python function using tiktoken to split it into chunks of at most 4000 tokens.
prompt 3
Show me how to add a custom special token to tiktoken's cl100k_base encoding to mark document boundaries in my training dataset.
prompt 4
What is Byte Pair Encoding and how does tiktoken use it? Explain it as if I have no NLP background, using tiktoken's educational submodule as a guide.

Frequently asked questions

what is tiktoken fr?

Tiktoken is a fast Python tokenizer for OpenAI language models that converts text to token numbers and back, 3-6x faster than comparable tools, useful for counting tokens before sending API requests.

What language is tiktoken written in?

Mainly Python. The stack also includes Python, pip.

How hard is tiktoken to set up?

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

Who is tiktoken for?

Mainly developer.

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