One Score for Every Candidate

Given the token sequence so far, the model produces a raw score, called a logit, for every entry in its vocabulary. The vocabulary is fixed and large, so the output is a full row of scores, one per possible next token. These logits are not yet probabilities; they are unbounded numbers that can be positive or negative, and only their relative size matters at this stage.