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Fundamentals

Tokenizer

The component that splits text into tokens (sub-word units) before feeding it to an AI model. Different models use different tokenizers — affecting how they count input length, handle multilingual text, and process code.

Why it matters

Models don't read letters or whole words; they read tokens, the small chunks a tokenizer breaks your text into. This quietly shapes cost, since you're usually billed per token, and it explains quirks like models miscounting characters or struggling with rare words. Understanding tokens helps you write leaner prompts and makes sense of why the same sentence can cost more in one model than another.

In practice

The word "unbelievable" might be split into pieces like "un," "believ," and "able," while a common word like "the" is a single token. Emojis or unusual names can take several tokens each. If you paste a long document and hit a length limit, it's the token count, not the word count, that matters, which is why trimming filler text can let more of your content fit.

Related terms

Put Tokenizer into practice

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