The experimental study of neural information processing in the biological visual system is challenging due to the nonlinear nature of neuronal responses to visual input. Artificial neural networks play a dual role in improving our understanding of this complex system, not only allowing computational neuroscientists to build predictive digital twins for novel hypothesis generation in silico, but also allowing machine learning to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the sensorium benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images.Using this dataset, we will host two benchmark tracks to find the best predictive models of neuronal responses on a held-out test set. The two tracks differ in whether measured behavior signals are made available or not. We provide code, tutorials, and pre-trained baseline models to lower the barrier for entering the competition. Beyond this proposal, our goal is to keep the accompanying website open with new yearly challenges for it to become a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.