Multispecies abundance models (MSAMs) are a framework for modeling biological communities subject to imperfect detection, and community assembly is embodied by an ensemble of species-level Poisson processes. Using a binomial N-mixture model for the detection process, we can construct MSAMs based on repeated counts of every species in a community. MSAMs can provide an abundance-based species richness estimator via data augmentation. To assess the performance of MSAMs, we conducted simulation experiments to examine the effects of mean abundance and detection probability and of the number of sampling sites and visits on the bias and precision of species-level parameters and of community-level species richness. Results showed that bias and imprecision of estimates decreased when all four variables increased. Mean detection probability was the most important for intercepts, while mean abundance was most influential for slopes and species richness. For all parameters, the number of sites was more important than the number of visits.