As Multimodal Large Language Models (MLLMs) continue to evolve, the need for robust evaluation methods becomes critical. It's no longer enough for a model to simply "see" an image; it must reason about it, understand spatial relationships, parse complex documents, and interpret temporal dynamics in video.
This post introduces a comprehensive collection of benchmarks designed to test these specific capabilities, categorized by the domain they target.
Based on the image provided, here are the benchmarks listed along with their categories, descriptions, and associated links.
Visual Reasoning
These benchmarks test a model's ability to perform complex reasoning tasks based on visual inputs, going beyond simple object recognition.
- MMMU Pro: Focuses on robust multimodal understanding and visual reasoning. It challenges models with tasks that require expert-level knowledge and reasoning. Hugging Face
- VLMsAreBiased: An interesting benchmark that evaluates whether a model grounds its answers in visual evidence or relies on memorized priors (biases) from its training data. arXiv Paper
Document Understanding
For AI agents to be useful in professional settings, they must be able to read and understand documents, including charts and complex layouts.
- CharXiv Reasoning: specifically targets scientific chart understanding and reasoning, a crucial skill for analyzing academic papers. Project Page
- OmniDocBench1.5: Evaluates diverse PDF document parsing capabilities, including Optical Character Recognition (OCR), layout analysis, and table extraction. GitHub
Spatial Awareness
Spatial reasoning is key for robotics and agents that need to interact with the physical world or understand 3D environments.
- ERQA: Tests the grounding of objects and spatial concepts within the physical world. arXiv Paper
- Point-Bench: Focuses on fine-grained grounded pointing for spatial and affordance tasks. Project Page
- RefSpatial: Evaluates spatial referring expressions requiring multi-step reasoning. Hugging Face
- CV-Bench: covers fundamental 2D/3D spatial understanding and relationship reasoning. Hugging Face
- MindCube: A novel benchmark for spatial mental modeling and reasoning derived from limited visual viewpoints. ResearchGate
Screen & GUI Agents
With the rise of "computer-using agents," benchmarking how models perceive and interact with software interfaces is essential.
- ScreenSpot Pro: Evaluates GUI grounding in professional software environments. Leaderboard
- Gui-World QA: Tests dynamic and sequential GUI understanding across diverse software environments. arXiv Paper
Video Analysis
Video benchmarks add the dimension of time, testing a model's memory and temporal reasoning.
- Video-MMMU: Targets knowledge acquisition from educational videos. Project Page
- Video-MME: Evaluates temporal reasoning and long-context video analysis. Project Page
- 1H-VideoQA: Specifically designed for hour-long videos, testing time-specific query understanding. arXiv Paper
- Perception Test: Measures perception and reasoning skills in real-world video scenarios. GitHub
- YouCook2: A classic benchmark for procedural video understanding, specifically for cooking recipes. Website
- Vatex: Focuses on multilingual video captioning and translation. Website
- Motion Bench: Tests fine-grained video motion perception and comprehension. Hugging Face
Specialized Domains: Education & Biomedical
Finally, we have benchmarks for high-stakes, specialized domains.
- Math Kangaroo (Education): Evaluates multimodal mathematical reasoning where the problem depends on visual context. Official Site
- MedXpertQA-MM (Biomedical): Tests expert-level medical reasoning in clinical scenarios. Paper
- VQA-RAD (Biomedical): Visual question answering specifically for radiology images. RadBench
- MicroVQA (Biomedical): A benchmark for reasoning about microscopy-based biological research. Paper
Conclusion
Selecting the right benchmark depends entirely on your use case. Whether you are building an agent to navigate a GUI, a tool to parse financial PDFs, or a robot to understand spatial commands, this list provides a starting point for evaluating the state-of-the-art in multimodal AI.
