minbpe vs tiktoken
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A small, readable Python implementation of Byte Pair Encoding, the text-splitting method used in LLM tokenizer
Open source · MIT
A fast library that splits text into the tokens OpenAI's models read.
The short answer
Choose minbpe if you need…
- Students and engineers who want to understand how LLM tokenizers work by reading code
- Developers who want to train a small custom tokenizer on their own text
- Anyone following the author's BPE lecture or exercise
- Researchers who want a hackable baseline before using heavier tooling
Choose tiktoken if you need…
- Python developers who need to count tokens before sending text to OpenAI models
- Developers who want the same tokenizer that matches a specific OpenAI model, such as gpt-4o
- People who want to learn how BPE tokenization works, using the included educational submodule
- Developers building custom encodings who want to extend tiktoken
Side-by-side
| minbpe | tiktoken | |
|---|---|---|
| Type | Learning resource | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 11k | 19.6k |
| Setup difficulty | Intermediate | Beginner |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Python | Python |
| Last code update | 2 years ago | 3 months ago |
What each one is
minbpe
minbpe is a short Python codebase that implements Byte Pair Encoding (BPE), the algorithm that many large language models use to break text into tokens. It lets you train a tokenizer on your own text, encode text into token IDs, and decode those IDs back into text. The code is kept deliberately minimal and commented so people can read how the algorithm works.
tiktoken
tiktoken is a Python library that converts text into tokens, the numbers language models work with, using byte pair encoding (BPE). It can also turn tokens back into the original text. Developers use it to count tokens or prepare text for OpenAI models.
Best for
minbpe
- Students and engineers who want to understand how LLM tokenizers work by reading code
- Developers who want to train a small custom tokenizer on their own text
- Anyone following the author's BPE lecture or exercise
- Researchers who want a hackable baseline before using heavier tooling
tiktoken
- Python developers who need to count tokens before sending text to OpenAI models
- Developers who want the same tokenizer that matches a specific OpenAI model, such as gpt-4o
- People who want to learn how BPE tokenization works, using the included educational submodule
- Developers building custom encodings who want to extend tiktoken
Strengths
minbpe
- Very short, clean code with comments, designed to be read and modified
- Includes comparisons against tiktoken to verify behavior
- Covers the basic, regex-split, and GPT-4-compatible variants of the algorithm
tiktoken
- The README reports it is 3-6x faster than a comparable open source tokenizer, measured on 1GB of GPT-2 text
- Installs from PyPI with a single pip command
- Released under the MIT license with a large community following
Not ideal for
minbpe
- Production systems that need fast tokenization on large corpora, since the README lists a faster optimized version only as a future todo
- People who just need to tokenize text for an existing model, where a library such as tiktoken is the more direct choice
- Users who need a polished packaged tool with documented releases, since the repo has no listed releases
tiktoken
- People who need a tokenizer for non-OpenAI models, since the README only describes OpenAI's models
- Non-Python projects, because the README only shows Python installation and usage
- Non-developers looking for a ready-made app, since this is a code library with no interface described
Trade-offs
minbpe
- Not optimized for speed; the README lists a more optimized version as an unfinished todo
- Documentation is brief and assumes some familiarity with tokenization concepts
- Last push was 2024-07-01, so recent maintenance activity is unclear
tiktoken
- The README covers only OpenAI-oriented encodings, so support for other model families is not described
- Performance claims come from a single benchmark setup listed in the README, with older library versions
- Extending it with custom encodings requires care, such as avoiding editable installs for plugin packages
Getting started
minbpe
Clone the repository
git clone https://github.com/karpathy/minbpeFrequently asked questions
What is the difference between minbpe and tiktoken?
minbpe: A small, readable Python implementation of Byte Pair Encoding, the text-splitting method used in LLM tokenizer. tiktoken: A fast library that splits text into the tokens OpenAI's models read. minbpe suits students and engineers who want to understand how llm tokenizers work by reading code, while tiktoken suits python developers who need to count tokens before sending text to openai models.
Are minbpe and tiktoken free?
minbpe: Free and open source. tiktoken: Free and open source.
Which is easier to set up, minbpe or tiktoken?
tiktoken is easier to get started with (rated beginner); minbpe is rated intermediate and usually needs more setup.
Is minbpe or tiktoken more popular?
tiktoken has more GitHub stars (19.6k) than minbpe (11k). Popularity isn't the same as fit — check which one matches your use case above.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.