tiktoken-rs 0.11.0

Library for encoding and decoding with the tiktoken library in Rust
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154

## `tiktoken-rs`

[![Github Contributors](https://img.shields.io/github/contributors/zurawiki/tiktoken-rs.svg)](https://github.com/zurawiki/tiktoken-rs/graphs/contributors)
[![Github Stars](https://img.shields.io/github/stars/zurawiki/tiktoken-rs.svg)](https://github.com/zurawiki/tiktoken-rs/stargazers)
[![CI](https://github.com/zurawiki/tiktoken-rs/actions/workflows/ci.yml/badge.svg)](https://github.com/zurawiki/tiktoken-rs/actions/workflows/ci.yml)

[![crates.io status](https://img.shields.io/crates/v/tiktoken-rs.svg)](https://crates.io/crates/tiktoken-rs)
[![crates.io downloads](https://img.shields.io/crates/d/tiktoken-rs.svg)](https://crates.io/crates/tiktoken-rs)
[![Rust dependency status](https://deps.rs/repo/github/zurawiki/tiktoken-rs/status.svg)](https://deps.rs/repo/github/zurawiki/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`](https://crates.io/crates/tokenizers) crate.

# Examples

For full working examples for all supported features, see the [examples](https://github.com/zurawiki/tiktoken-rs/tree/main/tiktoken-rs/examples) directory in the repository.

# Usage

1. Install this tool locally with `cargo`

```sh
cargo add tiktoken-rs
```

Then in your rust code, call the API

## Counting token length

```rust
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:

```rust
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

```rust
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](https://crates.io/crates/async-openai)

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

```rust
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:

| Encoding name           | OpenAI models                                                                  |
| ----------------------- | ------------------------------------------------------------------------------ |
| `o200k_harmony`         | `gpt-oss-20b`, `gpt-oss-120b`                                                  |
| `o200k_base`            | GPT-5 series, `o1`/`o3`/`o4` series, `gpt-4o`, `gpt-4.5`, `gpt-4.1`, `codex-*` |
| `cl100k_base`           | `gpt-4`, `gpt-3.5-turbo`, `text-embedding-ada-002`, `text-embedding-3-*`       |
| `p50k_base`             | Code models, `text-davinci-002`, `text-davinci-003`                            |
| `p50k_edit`             | Edit models like `text-davinci-edit-001`, `code-davinci-edit-001`              |
| `r50k_base` (or `gpt2`) | GPT-3 models like `davinci`                                                    |

### Context sizes

| Model                                                               | Context window |
| ------------------------------------------------------------------- | -------------- |
| `gpt-5.4`, `gpt-5.4-pro`                                            | 1,050,000      |
| `gpt-4.1`, `gpt-4.1-mini`, `gpt-4.1-nano`                           | 1,047,576      |
| `gpt-5`, `gpt-5-mini`, `gpt-5-nano`, `gpt-5.4-mini`, `gpt-5.4-nano` | 400,000        |
| `o1`, `o3`, `o3-mini`, `o3-pro`, `o4-mini`                          | 200,000        |
| `codex-mini`                                                        | 200,000        |
| `gpt-oss`                                                           | 131,072        |
| `gpt-4o`, `gpt-4o-mini`                                             | 128,000        |
| `o1-mini`, `gpt-5.3-codex-spark`                                    | 128,000        |
| `gpt-3.5-turbo`                                                     | 16,385         |
| `gpt-4`                                                             | 8,192          |

See the [examples](https://github.com/zurawiki/tiktoken-rs/tree/main/tiktoken-rs/examples) in the repo for use cases. For more context on the different tokenizers, see the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/blob/66b988407d8d13cad5060a881dc8c892141f2d5c/examples/How_to_count_tokens_with_tiktoken.ipynb)

# 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](./LICENSE).