Tokens and Context Windows

Large Language Models (LLMs) don't read text the way you do, character by character or word by word. They break text into chunks called tokens — which might be a whole word, part of a word, or even a single punctuation mark, depending on how common that chunk is.

As a rough rule of thumb in English, 1 token ≈ ¾ of a word, so 100 words is roughly 130-140 tokens.

Why tokens matter to you

Two practical things depend directly on tokens:

• Cost and speed. Many AI tools charge (or measure limits) per token processed, not per word or per request.
• The context window. This is the maximum number of tokens a model can "see" at once — including your question, any files or history you've provided, and its own response. Once a conversation exceeds that limit, the oldest content gets dropped or summarized, which is why long conversations can sometimes seem to "forget" earlier details.

A useful analogy

Think of the context window like a desk, not a filing cabinet. The model can only "see" what's currently spread out on the desk (within the token limit). It has no separate long-term memory of your conversation unless a product specifically saves and re-feeds that information back in — which is a feature the surrounding application builds, not something the raw model does automatically.

Key takeaway: tokens are the model's unit of "reading," and the context window is the hard limit on how much it can consider at once.