Tokens, Context & Representation

How a model reads and holds text: tokens, context windows, embeddings and knowledge cutoffs, explained for readers who work with AI tools.

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Tokens, Context & Representation

ComparisonContext Window vs. Knowledge CutoffA context window limits how much information an AI model can use in one request, while a knowledge cutoff describes how recent its built-in knowledge is. One is a capacity measured in tokens; the other is a date associated with training.
ComparisonInput vs. Output vs. Reasoning TokensInput tokens are what a model receives, output tokens are what it generates, and reasoning tokens are internal generated tokens used to work toward an answer. The catch is that reasoning tokens are often part of output usage even when an API displays them as a separate detail.
ConceptWhat Are Input Tokens?Input tokens are the tokens a model receives and processes before it starts generating the next response. They come from the complete model-facing request, not only the latest words you typed.
ConceptWhat Are Output Tokens?Output tokens are the units a model generates as it produces a response. They usually make up the text, code, structured data, or tool instructions you receive, although a provider may also count hidden reasoning as output usage.
ConceptWhat Are Reasoning Tokens?Reasoning tokens are tokens an AI model generates while working toward an answer, separate from the answer text you see. They represent extra generated steps such as planning, checking, or trying an approach, and they can count toward usage and limits even when the service hides them.
ConceptWhat Is a Context Window?A context window is the maximum amount of tokenized material an AI model can use while producing one response. In many language-model APIs, that budget covers the input and the generated output, although providers may also impose separate input or output limits.
ConceptWhat Is a Token in AI?A token in AI is one item from the vocabulary an LLM uses to represent and generate text. Depending on the model's tokenizer, a token can correspond to a word, part of a word, punctuation, whitespace, or even part of an encoded character.
ConceptWhat Is an AI Knowledge Cutoff?An AI knowledge cutoff is the date up to which a model's built-in knowledge is expected to reflect its training data. It is a rough boundary, not a guarantee that the model knows every earlier fact or that an AI product cannot fetch later information.