Interactive GPT Tokenizer Visualization Tool
Explore how different GPT models tokenize your text in real-time. Privacy-focused, client-side processing.
- Real-time Visualization - See your text being tokenized as you type
- Multiple Model Support - Compare tokenization across all OpenAI GPT models
- Cost Optimization - Understand token counts to optimize your API usage
- Privacy First - All processing happens in your browser, no data sent to servers
- Lightning Fast - Instant tokenization with OpenAI's official tiktoken library
- Comprehensive Models - Support for 60+ models including GPT-4o, GPT-3.5, and code models
When using OpenAI's API, you're charged per token, not per word. Understanding tokenization helps you:
- Reduce API costs by writing efficient prompts
- Avoid hitting token limits in your requests
- Optimize model performance by understanding how text is processed
- Compare different models for your specific use case
Example: "Hello, world!" might be:
- 3 tokens in GPT-4o
- 4 tokens in GPT-3.5-turbo
- This directly affects your API costs!
Visit happytokenizer.com and start typing in the text area. That's it!
- Enter or paste your text in the main text area
- Select a model from the dropdown to see how it tokenizes your text
- View token breakdown including token IDs and character mappings
- Compare models to find the most cost-effective option for your needs
- GPT-4o series (gpt-4o, gpt-4o-mini, etc.)
- Latest OpenAI models with optimized encoding
- GPT-3.5 series (gpt-3.5-turbo, gpt-3.5-turbo-16k)
- GPT-4 series (gpt-4, gpt-4-32k)
- Code-davinci series
- Optimized for code generation
- Legacy models (r50k_base)
- Edit models (p50k_edit)
- Open-source models (o200k_harmony)
View the complete list at happytokenizer.com/models
Want to run HappyTokenizer locally or contribute? Here's how:
# Clone the repository
git clone https://github.com/yourusername/happytokenizer.git
cd happytokenizer
# Install dependencies
pnpm install
# Start development server
pnpm dev
# Build for production
pnpm build- Framework: Astro with React
- Styling: Tailwind CSS with custom design system
- Tokenization: OpenAI's official tiktoken library
- Type Safety: TypeScript throughout
- Build Tool: Vite
src/
├── components/
│ ├── react/ # React components with client-side logic
│ └── Footer.astro # Footer component
├── layouts/
│ └── MainLayout.astro # SEO-optimized base layout
├── pages/
│ ├── index.astro # Main tokenizer page
│ └── models.astro # Complete model manifest
└── utils/
└── modelEncodings.ts # Model configurations
HappyTokenizer is designed with privacy as a core principle:
- No data sent to servers - All processing happens locally
- No tracking or analytics - Your data stays yours
- Open source - Transparent and auditable code
- No cookies - Zero tracking technologies
We welcome contributions! Here's how you can help:
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Make your changes and test them
- Commit your changes:
git commit -m 'Add amazing feature' - Push to the branch:
git push origin feature/amazing-feature - Open a Pull Request
- UI/UX improvements - Better visualization ideas
- Mobile enhancements - Responsive design improvements
- Performance - Faster tokenization or loading
- Documentation - Better guides and examples
- Bug fixes - Report and fix issues
This project is licensed under the MIT License - see the LICENSE file for details.
- OpenAI for the tiktoken library
- The Astro team for the amazing framework
- All contributors who help improve this tool
Built for the AI community