Poor model quality in model predictive controller (MPC) is often an important source of performance degradation. A key issue in MPC model assessment is to identify whether the bad performance comes from model-plant mismatches (MPM) or unmeasured disturbances (UD). This paper proposes a method for distinguishing between such degradation sources, where the main idea is to compare the statistical distribution of the estimated nominal outputs with the actual modeling error. The proposed approach relies on the assessment of three case studies: a simple SISO Linear MPC and two multivariable cases, where the linear controller is subject to a linear and nonlinear plant, respectively. Results show that the proposed method provides a good indicator of the model degradation source, even when both effects are present but one of them is dominant.