tiktoken-rs

Rust library for tokenizing text with OpenAI models using tiktoken.

This library provides a set of ready-made tokenizer libraries for working with GPT, tiktoken and related OpenAI models. Use cases cover tokenizing and counting tokens in text inputs.

This library is built on top of the tiktoken library and includes some additional features and enhancements for ease of use with Rust code.

Supports all current OpenAI models including GPT-5.4, GPT-5, GPT-4.1, GPT-4o, o1, o3, o4-mini, and gpt-oss models.

Scope: This crate is focused on OpenAI tokenizers (tiktoken). For non-OpenAI models (Llama, Gemini, Mistral, etc.), use the HuggingFace tokenizers crate.

Examples

For full working examples for all supported features, see the examples directory in the repository.

Usage

1. Install this tool locally with cargo

cargo add tiktoken-rs

Then in your rust code, call the API

Counting token length

use tiktoken_rs::o200k_base;

let bpe = o200k_base().unwrap();
let tokens = bpe.encode_with_special_tokens(
  "This is a sentence   with spaces"
);
println!("Token count: {}", tokens.len());

For repeated calls, use the singleton to avoid re-initializing the tokenizer:

use tiktoken_rs::o200k_base_singleton;

let bpe = o200k_base_singleton();
let tokens = bpe.encode_with_special_tokens(
  "This is a sentence   with spaces"
);
println!("Token count: {}", tokens.len());

Counting max_tokens parameter for a chat completion request

use tiktoken_rs::{get_chat_completion_max_tokens, ChatCompletionRequestMessage};

let messages = vec![
    ChatCompletionRequestMessage {
        content: Some("You are a helpful assistant that only speaks French.".to_string()),
        role: "system".to_string(),
        ..Default::default()
    },
    ChatCompletionRequestMessage {
        content: Some("Hello, how are you?".to_string()),
        role: "user".to_string(),
        ..Default::default()
    },
    ChatCompletionRequestMessage {
        content: Some("Parlez-vous francais?".to_string()),
        role: "system".to_string(),
        ..Default::default()
    },
];
let max_tokens = get_chat_completion_max_tokens("o1-mini", &messages).unwrap();
println!("max_tokens: {}", max_tokens);

Counting max_tokens parameter for a chat completion request with async-openai

Need to enable the async-openai feature in your Cargo.toml file.

use tiktoken_rs::async_openai::get_chat_completion_max_tokens;
use async_openai::types::chat::{
    ChatCompletionRequestMessage, ChatCompletionRequestSystemMessage,
    ChatCompletionRequestSystemMessageContent, ChatCompletionRequestUserMessage,
    ChatCompletionRequestUserMessageContent,
};

let messages = vec![
    ChatCompletionRequestMessage::System(ChatCompletionRequestSystemMessage {
        content: ChatCompletionRequestSystemMessageContent::Text(
            "You are a helpful assistant that only speaks French.".to_string(),
        ),
        name: None,
    }),
    ChatCompletionRequestMessage::User(ChatCompletionRequestUserMessage {
        content: ChatCompletionRequestUserMessageContent::Text(
            "Hello, how are you?".to_string(),
        ),
        name: None,
    }),
];
let max_tokens = get_chat_completion_max_tokens("o1-mini", &messages).unwrap();
println!("max_tokens: {}", max_tokens);

tiktoken supports these encodings used by OpenAI models:

Context sizes

See the examples in the repo for use cases. For more context on the different tokenizers, see the OpenAI Cookbook

Encountered any bugs?

If you encounter any bugs or have any suggestions for improvements, please open an issue on the repository.

Acknowledgements

Thanks @spolu for the original code, and .tiktoken files.

License

This project is licensed under the MIT License.