Logits are the raw, unnormalized scores produced by a classification model before conversion into probabilities. In multiclass classification, logits are commonly passed through a softmax function to produce a probability distribution over possible classes.
The vector of raw (non-normalized) predictions that a classification model generates, which is ordinarily then passed to a normalization function. If the model is solving a multi-class classification problem, logits typically become an input to the softmax function. The softmax function then generates a vector of (normalized) probabilities with one value for each possible class.