Multiregional bulk sequencing data is necessary to characterize intratumor genetic heterogeneity. Novel somatic variant calling approaches aim to address the particular characteristics of multiregional data, but it remains unclear to which extent they improve compared to single-sample strategies. Here we compared the performance of 16 single-nucleotide variant calling approaches on multiregional sequencing data under different scenarios with in-silico and real sequencing reads, including varying sequencing coverage and increasing levels of spatial clonal admixture. Under the conditions simulated, methods that use information across multiple samples do not necessarily perform better than some of the standard calling methods that work sample by sample. Nonetheless, our results indicate that under difficult conditions, Mutect2 in multisample mode, in combination with a correction step, seems to perform best. Our analysis provides data-driven guidance for users and developers of somatic variant calling tools.