In their paper "Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art," Andre Rupp and Jonathan Templin (2008) provide a comparative analysis of selected psychometric models useful for the analysis of multidimensional data for purposes of diagnostic score reporting. Recent assessment literature has seen an increase in the development and analysis of numerous models for this purpose, the larger class of which is often referred to as "cognitive diagnostic models" (CDMs), all of which are appropriate for cognitively-based modeling of educational and psychological assessment data. The term "diagnostic classification model" ("DCM"), is introduced by Rupp and Templin to identify a subset of CDMs with statistical formulations that adhere to a constrained set of criteria. Rupp and Templin should be applauded on two counts. First, for the completeness of their review based on statistical considerations and for the didactic presentation which readers will find useful for understanding these models. Second, they are to be commended for introducing the new term DCM when discussing the set of models included in this paper, as opposed to the more general category CDM. However, while Rupp and Templin are wholly successful in their delineation of statistical differences among and between the various DCM models and other psychometric approaches, the current advantages of DCMs are less clear. The substantive advantage of DCMs relative to other models, as presented by Rupp and Templin, is the criterion-referenced score interpretations generated in terms of cognitively-based diagnostic classifications. If similar score interpretations could be generated without these "state-of-the-art" models, however, what is their advantage? To answer this question, the author considers two characteristics "unique" to DCMs: (1) their numerically derived cut-scores for criterion-reference score interpretations; and (2) their diagnostic power based on multidimensional data.