Multilingual tokenizer
See how the model reads your text.
Before an embedding model can understand a sentence, it breaks it into tokens. This runs the very tokenizer behind Quanfire.ai’s multilingual embeddings — for any of 100+ languages — right here in your browser. Type anything and watch it split, count, and compare across scripts.
Type above to see how the tokenizer splits it.
- Tokens
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- vocabulary pieces
- Characters
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- Unicode code points
- Chars / token
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- compression — higher is more efficient
- Tokens / word
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- fertility — lower is better
- Bytes
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- UTF-8 on the wire
- Unknown
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- fell outside the vocabulary
each colour is one token· marks a word boundaryunkoutside the vocabulary
The same sentence, ten languages
tokens for one parallel sentence · lower is more efficient“Artificial intelligence will change the world.” means the same thing in every row — yet the tokenizer spends far more tokens on some scripts than others. That gap is the fairness problem multilingual embedding work exists to close: fewer tokens means each language keeps more of the model’s effective context for the same content.
What a token is
A token is a chunk of text from a fixed vocabulary — sometimes a whole word, often a fragment, occasionally a single character. Models don’t see letters or words; they see token ids. How text is split decides how much a model can hold at once and how evenly it treats each language. A vocabulary trained mostly on English encodes English cheaply and spends extra tokens on everything else — which is exactly what the comparison above makes visible, and what Quanfire.ai’s work is built to correct.