The "workhorse" model for analysing discrete choice data, the conditional logit model, can be implemented in Stata using the official clogit and asclogit commands. While widely used, this model has several well-known limitations that have led researchers in various disciplines to consider more flexible alternatives. The mixed logit model extends the standard conditional logit model by allowing one or more of the parameters in the model to be randomly distributed. When one models the choices of individuals (as is common in several disciplines, including economics, marketing, and transport), this allows for preference of heterogeneity among respondents. Other advantages of the mixed logit model include the ability to allow for correlations across observations in cases where an individual made more than one choice, and relaxing the restrictive independence from the irrelevant alternatives property of the conditional logit model. There are a range of commands that can be used to estimate mixed logit models in Stata. With the exception of xtmelogit, the official Stata command for estimating binary mixed logit models, all of them are userwritten. The module that is probably best known is gllamm, but while very flexible, it can be slow when the model includes several random parameters. This talk will focus on alternative commands for estimating logit models, with focus on the mixlogit module. We will also look at alternatives and extensions to mixlogit, including the recent lclogit, bayesmlogit, and gmnl commands. The talk will review the theory behind the methods implemented by these commands and present examples of their use.