Next-generation sequencing (NGS) is a key technology in understanding the causes and consequences of human genetic variability. In this context, the validity of NGSinferred single-nucleotide variants (SNVs) is of paramount importance. We therefore developed a statistical framework to assess the fidelity of three common NGS platforms and to estimate the proportion of false-positives heterozygotes based on read distributions. Application of this framework to aligned DNA sequence data from two completely sequenced HapMap samples as included in the 1000 Genomes Project revealed remarkably different error profiles for the three platforms. Newly identified SNVs showed consistently higher proportions of false positives (3-17%) when compared to confirmed HapMap variants. We show that this increase was not due to differences in flanking sequence features, read coverage or quality, nor was this observation limited to a particular data set or variant calling algorithm. Consensus calling by more than one platform yielded significantly lower error rates (1-4%). This implies that the use of multiple NGS platforms may be more cost-efficient than relying upon a single technology alone, particularly in physically localized sequencing experiments that rely upon small error rates. Our study thus highlights that different NGS platforms suit different practical applications differently well.