What is tokenization in AI language models?

Tokenization is the process of breaking down text into smaller units called tokens, which AI language models use to understand and process text. A token can be a word, part of a word, or even a single character. For example, "hello" might be one token, while "unprecedented" might be split into multiple tokens like "un", "pre", "cedent", and "ed". Understanding tokenization is crucial because AI models have token limits for their inputs and outputs, and API costs are often calculated based on the number of tokens used.

Tool description

The GPT Tokenizer tool allows you to see exactly how OpenAI's various GPT models tokenize text input. You can enter any text prompt and select from a wide range of GPT models to see the token breakdown with color-coded visualization. Each token is highlighted with a unique color, making it easy to understand how the model processes your text. The tool displays the total token count and shows special characters (spaces as dots and line breaks as arrows) for better visibility.

Examples

Input:

  • Model: GPT-5
  • Prompt: "Hello, how are you today?"

Output:

  • Tokens: 7
  • Visualization: Each word/punctuation shown in different colors

Features

  • Multiple Model Support: Choose from 30+ GPT and OpenAI models
  • Real-time Tokenization: See tokens update instantly as you type
  • Color-coded Visualization: Each token gets a unique color for easy identification
  • Special Character Display: Spaces shown as dots (·) and line breaks as arrows (↵)
  • Token Count: Real-time display of total tokens used
  • Model-specific Encoding: Each model uses its own tokenization rules

Supported Models

The tool supports the following OpenAI models:

ChatGPT Series:

  • ChatGPT-4o Latest

GPT-5 Series:

  • GPT-5
  • GPT-5 Pro
  • GPT-5 mini
  • GPT-5 nano

GPT-4.x Series:

  • GPT-4.5 Preview
  • GPT-4.1
  • GPT-4.1 mini
  • GPT-4.1 nano

GPT-4 Series:

  • GPT-4o
  • GPT-4o mini
  • GPT-4
  • GPT-4 turbo

GPT-3.5 Series:

  • GPT-3.5 turbo
  • GPT-3.5 turbo instruct

O-Series (Reasoning Models):

  • o4-mini
  • o3
  • o3-mini
  • o3-pro
  • o1
  • o1-mini
  • o1-preview
  • o1-pro

Legacy Models:

  • text-davinci-003
  • text-davinci-002
  • text-davinci-001

Use Cases

  • API Cost Estimation: Calculate token usage before making API calls to estimate costs
  • Prompt Optimization: Reduce token count by understanding how text is tokenized
  • Context Window Planning: Ensure your prompts fit within model token limits
  • Debugging AI Responses: Understand why certain inputs produce unexpected outputs
  • Educational Purposes: Learn how different models handle tokenization differently
  • Content Length Planning: Plan content that fits within token constraints