# tiktoken — Fast BPE Tokenizer for OpenAI Models

> A high-performance byte pair encoding tokenizer used by OpenAI GPT models, written in Rust with Python bindings for counting and splitting tokens.

## Install

Save as a script file and run:

# tiktoken — Fast BPE Tokenizer for OpenAI Models

## Quick Use
```bash
pip install tiktoken

python -c "import tiktoken; enc = tiktoken.encoding_for_model('gpt-4o'); print(len(enc.encode('Hello world!')))"
```

## Introduction
tiktoken is a fast BPE (Byte Pair Encoding) tokenizer maintained by OpenAI. It lets developers count tokens, split text, and debug prompts before sending them to GPT-family models, preventing unexpected truncation and controlling costs.

## What tiktoken Does
- Encodes and decodes text using the exact tokenization schemes of GPT-3.5, GPT-4, and GPT-4o
- Counts tokens accurately so you can stay within context-window limits
- Provides multiple encoding presets (cl100k_base, o200k_base, p50k_base)
- Returns byte-level token IDs for low-level prompt inspection
- Offers a thread-safe Rust core with Python bindings for high throughput

## Architecture Overview
tiktoken is implemented in Rust for speed and exposes a thin Python wrapper via PyO3. The core performs regex-based pre-tokenization followed by BPE merging against a precomputed rank table. Encoding tables are lazy-loaded from a remote blob store on first use and cached locally.

## Self-Hosting & Configuration
- Install from PyPI: `pip install tiktoken`
- No server component required; runs entirely in-process
- Encoding files are fetched once and cached in `~/.cache/tiktoken`
- Set `TIKTOKEN_CACHE_DIR` to override the cache path
- Use `tiktoken.get_encoding("cl100k_base")` to load a specific vocabulary

## Key Features
- Sub-millisecond encoding of typical prompts due to Rust core
- Supports all current OpenAI model tokenization schemes
- Deterministic output matching the OpenAI API token count exactly
- Lightweight with minimal dependencies
- Works offline after initial cache warm-up

## Comparison with Similar Tools
- **Hugging Face tokenizers** — more general but does not guarantee OpenAI-compatible counts
- **SentencePiece** — supports BPE and Unigram but needs manual vocab loading for GPT models
- **transformers AutoTokenizer** — convenient for HF models, heavier dependency tree
- **GPT-2 Encoder (Python)** — reference implementation, much slower than tiktoken

## FAQ
**Q: Which encoding should I use for GPT-4o?**
A: Use `o200k_base`, which tiktoken selects automatically when you call `encoding_for_model("gpt-4o")`.

**Q: Can I use tiktoken without an internet connection?**
A: Yes, once the encoding file is cached locally. Pre-warm by running any encode call while online.

**Q: Does tiktoken work with non-OpenAI models?**
A: It only ships OpenAI vocabularies. For other models, use Hugging Face tokenizers or SentencePiece.

**Q: Is tiktoken thread-safe?**
A: Yes. The Rust core is safe to call from multiple Python threads concurrently.

## Sources
- https://github.com/openai/tiktoken
- https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken

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Source: https://tokrepo.com/en/workflows/asset-9b284e97
Author: Script Depot