Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these methods in real data has been lacking. Here, we report an assessment of 11 state-ofthe-art relatedness inference methods using a dataset with 2,485 individuals contained in several large pedigrees that span up to six generations. We ﬁnd that all methods have high accuracy (∼93%−99%) when reporting ﬁrst and second degree relationships, but their accuracy dwindles to less than 60% for ﬁfth degree relationships. However, the inferred relationships were correct to within one relatedness degree at a rate of 83%−99% across all methods and considered relationship degrees. Furthermore, most methods infer unrelated individuals correctly at a rate of ∼99%, suggesting a low rate of false positives. Overall, the most accurate methods were ERSA 2.0 and approaches that classify relationships using the IBD segments inferred by Reﬁned IBD and IBDseq. Combining results from the most accurate methods provides little accuracy improvement, indicating that novel approaches for relatedness inference may be needed to achieve a sizeable jump in performance.