As mentioned in the MLP-Mixer paper, when pre-trained on large datasets, or with modern regularization schemes, the MLP-Mixer attains competitive scores to state-of-the-art models. You can obtain better results by increasing the embedding dimensions, increasing, increasing the number of mixer blocks, and training the model for longer. You may also try to increase the size of the input images and use different patch sizes.