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Currently, the most popular are mixed logit (MIXL), particularly the version with normal mixing (N\u2010MIXL), and latent class (LC), which assumes discrete consumer types. Recently, several alternative models have been developed. The 'generalized multinomial logit' (G\u2010MNL) extends N\u2010MIXL by allowing for heterogeneity in the logit scale coefficient. Scale heterogeneity logit (S\u2010MNL) is a special case of G\u2010MNL with scale heterogeneity only. The 'mixed\u2010mixed' logit (MM\u2010MNL) assumes a discrete mixture\u2010of\u2010normals heterogeneity distribution. Finally, one can modify N\u2010MIXL by imposing theoretical sign constraints on vertical attributes. We call this 'T\u2010MIXL'. We find that none of these models dominates the others, but G\u2010MNL, MM\u2010MNL and T\u2010MIXL typically outperform the popular N\u2010MIXL and LC models. 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