The assessment of test data for the presence of differential item functioning (DIF) is a key component of instrument development and validation. Among the many methods that have been used successfully in such analyses is the mixture modeling approach. Using this approach to identify the presence of DIF has been touted as potentially superior for gaining insights into the etiology of DIF, as compared to using intact groups. Recently, researchers have expanded on this work to incorporate multilevel mixture modeling, for cases in which examinees are nested within schools. The current study further expands on this multilevel mixture modeling for DIF detection by using a multidimensional multilevel mixture model that incorporates multiple measured dimensions, as well as the presence of multiple subgroups in the population. This model was applied to a national sample of third-grade students who completed math and language tests. Results of the analysis demonstrate that the multidimensional model provides more complete information regarding the nature of DIF than do separate unidimensional models.