Multimodal foundation models (MFMs), such as GPT-4o, have recently made remarkable progress. However, their detailed visual understanding beyond question answering remains unclear. In this paper, we benchmark popular MFMs (GPT-4o, o4-mini, Gemini 1.5 Pro and Gemini 2.0 Flash, Claude 3.5 Sonnet, Qwen2-VL, Llama 3.2) on standard computer vision tasks (semantic segmentation, object detection, image classification, depth and surface normal prediction) using established datasets (e.g., COCO, ImageNet, etc). The main challenges in performing this analysis are: 1) most models are trained to output text and cannot natively express versatile domains, such as segments or 3D geometry, and 2) many leading models are proprietary and accessible only at an API level, i.e., there is no weight access to adapt them. We address these by translating vision tasks into text-promptable, API-compatible formats via prompt chaining, creating a standardized benchmarking framework. We observe that: 1) The MFMs are not close to the state-of-the-art specialist models at any task. 2) They are respectable generalists; this is remarkable, as they are presumably trained on image-text-based tasks. 3) They perform semantic tasks notably better than geometric ones. 4) GPT-4o performs the best among non-reasoning models, securing the top position in 4 out of 6 tasks. 5) Reasoning models, e.g., o3, show improvements in geometric tasks. 6) While prompt chaining techniques affect performance, better models are less sensitive to prompt variations. 7) An analysis of models with native image generation, such as the latest GPT-4o, shows they exhibit failure modes, such as hallucinated objects or misalignment between input and output.