{
  "id": "mmmu",
  "code": "PTL-0196",
  "term": "MMMU",
  "aliases": [],
  "category": "benchmarks",
  "definition": "MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark of college-level questions that require reasoning over images, such as charts, diagrams, tables, and chemical structures, together with text, across 30 subjects in six disciplines.",
  "description": "It tests whether multimodal models can combine visual perception with domain knowledge, not only describe images. It is a standard headline score for vision-language models.",
  "example": null,
  "broader": [
    "benchmark"
  ],
  "narrower": [],
  "related": [
    "multimodal-prompting",
    "mmlu"
  ],
  "introduced": 2023,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI",
      "authors": "Yue et al.",
      "year": 2023,
      "url": "https://arxiv.org/abs/2311.16502"
    }
  ],
  "url": "https://protologue.com/t/mmmu/",
  "citation": "Protologue. (2026). MMMU. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0196). https://protologue.com/t/mmmu/"
}