Most people working with LLMs don't think about tokenization until they hit a context limit or get a surprising API bill. I built this to make tokenization visible and interactive.
Type any text, and it breaks it down into tokens in real time. You can see exactly how a model would "read" your input.
Why tokenization matters
- API costs are based on token count, not character count
- Models have maximum token limits (4K, 8K, 32K)
- More efficient tokenization means more content within those limits
- Understanding token patterns helps you write better prompts
What it shows
The tool color-codes tokens by type:
- Words in blue
- Punctuation in red
- Spaces in gray
- Numbers in green
- Special characters in yellow
You can switch between three views: visual tokens, token IDs, and byte lengths. Clicking any token shows detailed metadata like its ID, type, and byte length.
Things that surprise people
- "ChatGPT" might be 2-3 tokens, not 1
- Contractions like "don't" get split up
- Extra spaces increase token count
- Emojis can be multiple tokens
- Code tokenizes very differently from natural language
How it works
The tokenization logic splits text on word boundaries and special characters, categorizes each token by type, calculates byte length using TextEncoder, and assigns sequential IDs for tracking.
This is a simplified approach for learning purposes, not a model-specific tokenizer. The goal is understanding the concept, not replicating GPT or Claude's exact tokenizer.
Try it at tokenization.cleverdeveloper.in.