Although SGD with random reshuffle is widely applied in machine learning applications, there is a limited understanding of how model characteristics affect the convergence of the algorithm. In this work, we introduce model incoherence to characterize the diversity of model characteristics and study its impact on convergence of SGD with random reshuffle. Specifically, minimizer incoherence measures the discrepancy between the global minimizer of a sample loss and that of the total loss and affects the convergence error of SGD. In particular, we show that the variable sequence generated by SGD with random reshuffle converges to a certain global minimizer of the total loss under full minimizer coherence. The other curvature incoherence measures the quality of condition numbers of the sample losses and determines the convergence rate of SGD. With model incoherence, our results show that SGD has a faster convergence rate and smaller convergence error under random reshuffle than those under random sampling, and hence provide theoretical justifications to the superior practical performance of SGD with random reshuffle. We further verify our theoretical results on model incoherence via experiments on training nonconvex machine learning models.