Tokenization Visualizer
See how AI models break text into tokens using BPE-style tokenization
Input Text
45
Characters
19
Tokens
2.37
Chars/Token
Tokens
Token Details
| # | Token | ID | Type | Bytes |
|---|---|---|---|---|
| 0 | Hello | 15496 | word | 5 |
| 1 | , | 11 | punctuation | 1 |
| 2 | ␣ | 220 | special | 1 |
| 3 | world | 995 | word | 5 |
| 4 | ! | 0 | punctuation | 1 |
| 5 | ␣ | 220 | special | 1 |
| 6 | This | 1212 | word | 4 |
| 7 | ␣ | 220 | special | 1 |
| 8 | is | 318 | word | 2 |
| 9 | ␣ | 220 | special | 1 |
| 10 | a | 64 | word | 1 |
| 11 | ␣ | 220 | special | 1 |
| 12 | token | 30001 | word | 5 |
| 13 | ization | 1634 | word | 7 |
| 14 | ␣ | 220 | special | 1 |
| 15 | demo | 31555 | word | 4 |
| 16 | . | 13 | punctuation | 1 |
| 17 | ␣ | 220 | special | 1 |
| 18 | 🎉 | 47249 | special | 4 |
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What is Tokenization?
Tokenization is the process of breaking text into smaller units called tokens. AI language models don't process raw text — they work with numerical token IDs. Each token typically represents a word, part of a word, or a punctuation mark.
Most modern models use Byte-Pair Encoding (BPE) or similar algorithms that learn the most efficient way to break up text based on training data.
Why Tokenization Matters
💰 Cost
API pricing is per token, not per character. Understanding tokenization helps estimate costs.
📏 Context Limits
Context window sizes are measured in tokens. More efficient tokenization = more content in context.
🌍 Multilingual
Non-English text often uses more tokens per character. This affects both cost and capacity.
🔤 Rare Words
Uncommon words get split into subwords, which can affect how models understand them.
Pro Tip: Token Estimation
For English text, a rough estimate is ~4 characters per token. For code, it's closer to 3 characters per token due to punctuation and special characters.
Note: Simplified Demo
This is a simplified tokenization demo. Real tokenizers like tiktoken (OpenAI), SentencePiece (Google), or Hugging Face tokenizers have much larger vocabularies and different splitting rules.
Frequently Asked Questions
Why are some words split into multiple tokens?
BPE tokenizers learn common subword units from training data. Rare words get split into smaller pieces that the model knows. This allows handling any word, even unseen ones.
Do all models use the same tokenizer?
No! GPT models use tiktoken (different versions), Claude uses its own tokenizer, and open-source models often use SentencePiece. Token counts vary between models.
Related Tools
Token Counter
Count tokens for specific models.
Context Windows
Compare context sizes across models.