The quality of evaluated nuclear data can be impacted by, for example, the choice of the evaluation algorithm. The objective of this work is to compare the performance of the evaluation techniques generalized least squares (GLS), generalized nonlinear least squares in the parameter domain (GLS-P), and the Unified Monte Carlo evaluation algorithms B (UMC-B) and G (UMC-G), by using synthetic data. In particular, the effects of model defects are investigated. For small model defects, UMC-B and GLS-P are found to perform best, while these techniques yield the worst results for a significantly defective model; in particular, they seriously underestimate the uncertainties. If UMC-B is augmented with Gaussian processes, it performs distinctly better for a defective model but is more susceptible to an inadequate experimental covariance estimate.