Token Visualizer
AI token visualizer online. See how AI models break text into tokens. Understand tokenization visually—free token boundary viewer.
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Understanding Tokenization
- • Tokens are the smallest units AI models process (not always whole words)
- • Common words are often single tokens, while rare words get split
- • Spaces are usually attached to the following word token
- • Numbers often get split digit-by-digit
- • Understanding tokenization helps optimize prompts and costs
Features
- Interactive color-coded token visualization mapping text exact to model vocabularies
- Detailed hover tooltips displaying the exact Integer Token ID and raw byte string
- Granular support for both modern (GPT-4o cl100k_base) and legacy tokenizers
- Advanced toggle to visualize hidden special control tokens (e.g., <|endoftext|>)
- One-click export to copy the exact token array as structured JSON for backend testing
- Deep insight into how whitespace, punctuation, and emojis are fragmented
Common Use Cases
- Debugging why a specific AI model struggles to rhyme or spell a specific word (due to sub-word tokenization)
- Optimizing massive prompts by replacing expensive, heavily fragmented words with single-token synonyms
- Understanding the underlying mechanics of NLP (Natural Language Processing) byte-pair encoding
- Analyzing how differently open-source models handle non-English characters compared to OpenAI models
- Visualizing exactly how code indentation (tabs vs spaces) impacts your overall API spend
The Anatomy of a Token Array
To an AI model, text does not exist. The very first step of processing a prompt is converting your readable text into an array of integers (Token IDs). Tokenization is the algorithm that determines where to slice the text.
Common Byte-Pair Encoding (BPE) Behaviors:
- Whole Words: Highly common words (like "the", "apple", "computer") are usually assigned a single token ID.
- Sub-words: Complex, rare, or compound words (like "unbelievable") are sliced into smaller morphological chunks (e.g., "un" + "believ" + "able").
- Whitespace Merging: In modern tokenizers, a space character is rarely its own token. It is almost always fused to the beginning of the next word (e.g., the string " Hello" is a completely different token than "Hello").
Our visualizer uses alternating background colors to expose exactly where these invisible slices occur, allowing you to "see" text exactly how an LLM sees it.
Examples
Valid - CamelCase Fragmentation
tokenVisualizerTool Valid - URL & Email Splitting
contact@example.com / https://example.com/path Valid - Whitespace & Indentation
def test():
print("Notice the space tokens!")Frequently Asked Questions
What exactly is a Token ID?
A Token ID is the unique integer assigned to a specific string of characters in the model's predefined dictionary (vocabulary). For instance, in OpenAI's `cl100k_base` tokenizer, the word "apple" might be mapped to ID `4321`. The LLM only ever processes these integer IDs, never the raw letters.
Why are names and typos split into so many tiny colors?
LLMs have a finite vocabulary (usually between 30,000 to 100,000 tokens). If a word is not in that dictionary—like a unique surname, a typo, or highly technical jargon—the tokenizer falls back to splitting it into smaller sub-tokens it does recognize, sometimes breaking it down all the way to individual letters or raw UTF-8 bytes.
Does capitalization change the token boundaries?
Yes, drastically. Tokenizers are strictly case-sensitive. The word "Apple" with a capital A has a completely different Token ID than "apple" with a lowercase a. Depending on the context, changing the case can sometimes cause a word to be split into multiple tokens instead of one.
How do emojis map to tokens?
Emojis are rarely stored as single tokens. Because they are complex Unicode characters, they are often broken down into 2 to 4 raw byte tokens. You will often see emojis split across multiple blocks in the visualizer.
💡 Tips
- Hover over any colored block in the visualizer to reveal its exact integer Token ID and the raw string it represents.
- Look closely at the leading spaces on words. You will notice that " word" and "word" are completely different entities to the AI.