# tiktoken

> OpenAI's tokenizer library for encoding and decoding text into tokens, primarily used for calculating token counts with OpenAI's models and estimating chunk sizes for vector database document processing.

- **URL:** https://github.com/openai/tiktoken
- **Category:** SDKs & Libraries
- **Tags:** tokenization, Open Source, text-processing
- **Updated:** 2026-04-04 23:01
- **Canonical page:** https://vectordb.ever.works/id/items/tiktoken

## Details

## Overview

tiktoken is OpenAI's tokenizer library that enables fast token counting and text encoding/decoding using the same tokenizers used by OpenAI's language models. It is commonly used in chunking strategies for vector databases to ensure accurate token counts before embedding.

## Features

- Fast token encoding and decoding for multiple tokenizer models (cl100k_base, r50k_base, p50k_base)
- Compatible with GPT-4, GPT-3.5-Turbo, and other OpenAI models
- Supports encoding, decoding, and token counting in a single pass
- Written in Rust with Python bindings for performance
- Useful for calculating chunk sizes when preparing documents for vector database ingestion

## Common Use Cases

- Estimating token counts before sending text to embedding models
- Implementing fixed token window chunking strategies
- Calculating overlap between chunks for semantic coherence
- Validating that text inputs fit within model context limits

## Pricing

Free and open-source under the MIT license.

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