ParFormer's integration of different token mixers impacts its overall performance by enhancing feature extraction capabilities. By combining two distinct token mixers, such as separable convolution and transposed self-attention, ParFormer can effectively capture both local and global dependencies in the data. This integration allows for more precise representation of short- and long-range spatial relationships without the need for computationally intensive methods like shifting windows. The parallel architecture of ParFormer enables the fusion of these different token mixers, leading to improved feature extraction and ultimately better performance in image classification tasks.