Token

In the context of large language models, a token is the basic unit of text the model reads and generates. Tokens are not words—they are chunks of text determined by the model's tokenizer, typically three to four characters. The word "embedding" is one token. The word "unbelievable" is three. A period is one. This matters because everything in LLM-land is priced, measured, and constrained by tokens: API costs are per-token, context windows are measured in tokens, and rate limits cap tokens per minute. When someone says a model has a 128,000-token context window, that is the total budget for input and output combined. Understanding tokenization is not academic—it directly affects what you can fit in a prompt, how much a query costs, and why the model sometimes splits words in unexpected places.