The quality of model-based OPC correction depends strongly on how the model is calibrated in order to generate a resist image as close to the desired shapes as possible. As the k1 process factor decreases and design complexity increases, the correction accuracy and the model stability become more important. It is also assumed that the stability of one model can be tested when its response to a small variation in one or several parameters is small. In order to quantify this, the small-variation method has been tested on a variable threshold based model initially optimized for the 65nm node using measurements done with a test pattern mask. This method consists of introducing small variations to one input model parameter and analyzing the induced effects on the simulated edge placement error (EPE). In this paper, we study the impact of small changes in the optical and resist parameters (focus settings, inner and outer partial coherent factors, NA, resist thickness) on the model stability. And then, we quantify the sensitivity of the model towards each parameter shift. We also study the effects of modeling parameters (kernel count, model fitness, optical diameter) on the resulting simulated EPE. This kind of study allows us to detect coverage or process window problems. The process and modeling parameters have been modified one by one. The ranges of variations correspond to those observed during a typical experiment. Then the difference in simulated EPE between the reference model and the modified one has been calculated. Simulations show that the loss in model accuracy is essentially caused by changes in focus, outer sigma and NA and lower values of optical diameter and kernel count. Model results agree well with a production layout.