What Is Token?
The chunks AI models actually read and write: roughly three-quarters of a word each, and the unit you pay for. Here is the plain-English deep dive: what it means, why it matters, and how to use the concept in practice.
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Language models do not process words or letters; they process tokens, small chunks of text from a fixed vocabulary. "Understanding" might be one token; "unbelievable" might split into "un", "believ", "able". In English, a token averages about four characters, so 1,000 tokens is roughly 750 words. Every model has a tokenizer that slices text this way before the network ever sees it.
Tokens matter to your wallet because APIs price per million tokens, input (what you send) and output (what the model writes) billed at different rates. A frontier model might cost $15 per million input tokens and $75 per million output tokens, while small models cost pennies. When your prompt includes a 100-page document, you are paying for every token of it, on every request, which is why caching, summarizing, and choosing the right model class (see our API cost calculator) change bills by orders of magnitude.
Tokens also set the boundaries of what a model can consider at once (see context window) and explain quirky failures: models historically struggled to count letters in a word because they never see letters, only chunks. When output cuts off mid-sentence, you hit a token limit, not a mood.
The practical takeaways: budget roughly 1.3 tokens per English word, watch output tokens because they cost several times input, and remember that verbose prompts are not free. Trimming a system prompt from 2,000 to 500 tokens on a high-volume app is a real raise.
