Binary Logit is used in logistic regression models to model the probability of outcomes that have only two categories (binary), such as yes/no or success/failure. It is based on a logistic function that relates the independent variables to the probability of one of the two outcomes occurring.

Multinomial Logit is used for cases where the dependent variable has more than two categories, which are not necessarily consecutive. This model generalizes binary logistic regression to more categories (for example, choosing between three or more options) by using the log odds for each category compared to the reference category.