Psychopathy is a primary risk factor of re-offending in sexual offenders. Conceptually, both variable-centered (e.g., factor analysis) and clustering methods (e.g., latent profile analysis) have been used in previous research. Variable-centered and clustering methods were merged in a simultaneous modeling strategy for two purposes: First, to test assumptions on the emergence of psychopathic versus sociopathic (antisocial) sub-groups. And second to compare the predictive validity of clusters with that afforded by a dimensional cut-score. Using mixture modeling, two types of models were estimated: Latent class factor-analytic (LCFA) and factor-mixture models (FMM). The four-factor model of psychopathy as assessed with the Psychopathy Checklist-Revised (PCL-R) was estimated for up to 12 latent classes in a sample of adult male sexual offenders from Austria ( N = 1,266). Solutions with five (LCFA) and two latent classes (FMM) provided a good and parsimonious fit for the data. The two-latent-class FMM solution yielded higher predictive validity than a cut-score but only for general offense recidivism. Theoretically, this solution goes against etiological models that distinguish psychopathic from sociopathic (antisocial) individuals. Official data on offense recidivism (at a fixed 7-year-interval post-release) corroborate the importance of psychopathic offender subtypes. The rates of recidivism varied considerably between the subgroups.