The basic unit of text a language model reads and writes, usually a word, part of a word, or punctuation mark.
Language models do not see text as letters or whole sentences. A tokenizer first splits input into tokens, which are common chunks of characters. A short English word is often one token, while a long or unusual word may be split into several. Numbers, code, and non-English text often use more tokens per word.
Tokens matter in practice because most AI services price usage per token and limit how many tokens a model can handle at once. For example, if you paste a long contract into a chatbot, both the contract and the reply count toward the token total, which affects cost and whether the whole document fits. Counting tokens before sending a large request helps teams estimate spending and avoid truncated answers.
The maximum amount of text, measured in tokens, that a language model can consider at one time, including the prompt and its reply.
Large Language ModelAn AI model trained on vast amounts of text to understand and generate human-like language, code, and other content.
TokenizationReplacing sensitive data with non-sensitive substitutes called tokens, or breaking text into tokens that AI models can process.
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