Recent advances in event-based vision suggest that they complement traditional cameras by providing continuous observation without frame rate limitations and high dynamic range which are well-suited for correspondence tasks such as optical flow and point tracking. However, so far there is still a lack of comprehensive benchmarks for correspondence tasks with both event data and images. To fill this gap, we propose BlinkVision, a large-scale and diverse benchmark with rich modality and dense annotation of correspondence. BlinkVision has several appealing properties: 1) Rich modalities: It encompasses both event data and RGB images. 2) Rich annotations: It provides dense per-pixel annotations covering optical flow, scene flow, and point tracking. 3) Large vocabulary: It incorporates 410 daily categories, sharing common classes with widely-used 2D and 3D datasets such as LVIS and ShapeNet. 4) Naturalistic: It delivers photorealism data and covers a variety of naturalistic factors such as camera shake and deformation. BlinkVision enables extensive benchmarks on three types of correspondence tasks (i.e., optical flow, point tracking and scene flow estimation) for both image-based methods and event-based methods, leading to new observations, practices, and insights for future research. The benchmark website is https://www.blinkvision.net/.