Tokenizer Viewer
Visualize how your text is split into tokens. See the tokenization process and understand token boundaries.
Word
7
Chinese
4
Space
7
Punctuation
2
Symbol
1
Legend
What is Tokenization?
Tokenization is the process of breaking down text into smaller units called tokens. These tokens are what AI models actually process. Different types of content (words, punctuation, whitespace, Chinese characters) are handled differently during tokenization. Understanding this helps you optimize your prompts and estimate costs more accurately.
Token Types
- Words:Sequences of letters and numbers
- Chinese:Chinese characters (each is typically one token)
- Whitespace:Spaces, tabs, and newlines
- Punctuation:Commas, periods, quotes, etc.
- Symbols:Special characters and operators
Frequently Asked Questions
How does tokenization work?
Tokenization breaks text into smaller pieces called tokens. Different models use different tokenization algorithms, but they generally split text at word boundaries, punctuation, and whitespace. Some languages like Chinese may have each character as a separate token.
Why do spaces count as tokens?
Whitespace (spaces, tabs, newlines) are often tokenized separately because they carry semantic meaning in text structure. However, some tokenizers may combine whitespace with adjacent words depending on their algorithm.
Is this the same as GPT tokenization?
This viewer provides a simplified visualization of how text might be tokenized. Actual GPT models use more sophisticated subword tokenization (like BPE or tiktoken) which can split words into smaller pieces. For exact GPT tokenization, use OpenAI's official tokenizer.
How can I reduce my token count?
To reduce tokens: remove unnecessary whitespace, use shorter words when possible, avoid redundant information, and structure your prompts concisely. However, always prioritize clarity over token savings to ensure the AI understands your request.