Tiktoken: Definition and Examples

Tiktoken is a fast tokenization library developed and maintained by OpenAI. Its main role is to convert raw text into a sequence of tokens — the basic units that language models actually process. Unlike simple word or character splitting, Tiktoken uses the Byte Pair Encoding (BPE) algorithm to produce an optimal split that balances efficiency and language coverage.

In practice, Tiktoken allows developers to precisely count the number of tokens in a text before sending it to the OpenAI API. This feature is essential because GPT models have context limits expressed in tokens (e.g., 128,000 tokens for GPT-4 Turbo), and billing is also calculated per token. Without a reliable counting tool, it is impossible to optimize costs or avoid context window overflow errors.

The library is written in Rust for performance reasons, with Python bindings that make it easy to use. It supports several encodings corresponding to different OpenAI models: cl100k_base for GPT-4 and GPT-3.5 Turbo, o200k_base for GPT-4o, and p50k_base for older models. Each encoding has its own vocabulary and splitting rules.

Tiktoken has become a reference tool in the prompt engineering ecosystem because it allows designing prompts that fully exploit the available context window without exceeding it. It is also used to estimate API usage costs before execution, making it an indispensable component of any production pipeline using OpenAI models.