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| Pricing | Free | Free |
| Capabilities | 1 decomposed | 8 decomposed |
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VQAv2 serves as a benchmark for evaluating vision-language models by providing a dataset of 1.1 million questions paired with 650,000 images from the COCO dataset. It requires models to understand both visual content and generate natural language answers, utilizing a diverse set of question types such as color identification and quantity assessment. This dual requirement distinguishes it from other benchmarks that may focus solely on either vision or language tasks.
Unique: VQAv2 combines a large-scale dataset with a diverse range of question types, enabling comprehensive evaluation of vision-language models, unlike simpler datasets that may focus on a narrower scope.
vs alternatives: More comprehensive than other visual question-answering benchmarks due to its extensive question variety and large image corpus.
Evaluates AI models on 11,500 expert-level questions spanning 6 disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 183 subfields, requiring simultaneous perception of heterogeneous visual modalities (charts, diagrams, chemical structures, music sheets, maps, tables) and application of college-level domain knowledge with deliberate multi-step reasoning. Questions are sourced from actual college exams, textbooks, and lectures to ensure authentic difficulty and real-world relevance.
Unique: MMMU is the only benchmark combining (1) 11,500 questions across 30 college subjects and 183 subfields, (2) 30 heterogeneous visual modality types (including domain-specific visuals like chemical structures and music sheets), and (3) explicit sourcing from authentic college exams/textbooks/lectures rather than synthetic or crowdsourced data. This scale and diversity of real-world academic content distinguishes it from narrower benchmarks like MMVP or ScienceQA which focus on single domains or simpler visual reasoning.
vs alternatives: MMMU covers 6x more disciplines and 3x more subjects than domain-specific benchmarks (e.g., MedQA for medicine only), and includes heterogeneous visual types (chemical structures, music sheets) absent from general-purpose multimodal benchmarks like LVLM-eHub, making it the most comprehensive test of expert-level multimodal reasoning across academic domains.
Provides granular performance metrics stratified across 6 core academic disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 183 subfields, enabling identification of which knowledge domains and subject areas a model excels or struggles with. Leaderboard and evaluation infrastructure expose per-discipline accuracy, per-subject accuracy, and per-visual-modality accuracy to support targeted model improvement and domain-specific capability assessment.
Unique: MMMU's 183-subfield taxonomy enables fine-grained diagnostic analysis unavailable in coarser benchmarks. The explicit mapping of questions to both discipline and visual modality type allows simultaneous analysis of domain knowledge gaps and visual perception weaknesses, supporting root-cause analysis of model failures.
vs alternatives: Unlike general multimodal benchmarks (LVLM-eHub, MMBench) that report only aggregate accuracy, MMMU's discipline-stratified breakdown enables targeted optimization for specific domains, making it actionable for domain-specific AI development rather than just comparative ranking.
Evaluates multimodal model performance across 30 distinct visual modality types including domain-specific visuals (chemical structures, music sheets, mathematical diagrams) alongside common types (charts, tables, maps, photographs, illustrations). The benchmark explicitly tests whether models can perceive and reason over specialized visual representations used in professional and academic contexts, not just natural images or generic diagrams.
Unique: MMMU explicitly includes 30 heterogeneous visual modality types with emphasis on domain-specific visuals (chemical structures, music sheets, mathematical diagrams) rarely tested in general multimodal benchmarks. This design choice reflects real-world use cases where multimodal AI must handle specialized visual representations, not just natural images and generic charts.
vs alternatives: Most multimodal benchmarks (MMBench, LLaVA-Bench) focus on natural images and simple charts; MMMU's inclusion of domain-specific visuals (chemistry, music, engineering) makes it the only benchmark validating multimodal AI for professional knowledge work requiring specialized visual literacy.
Provides two evaluation pathways: (1) remote submission via EvalAI server (established 2023-12-04) with test set answers released for local verification (2026-02-12), and (2) local evaluation capability enabling offline batch evaluation of models on the full 11,500-question dataset. The dual infrastructure supports both cloud-based leaderboard submission and self-hosted evaluation for organizations with data privacy or latency constraints.
Unique: MMMU's dual evaluation infrastructure (remote EvalAI + local offline) is unusual for academic benchmarks, enabling both official leaderboard participation and privacy-preserving self-hosted evaluation. The 2026-02-12 release of test set answers for local verification suggests a hybrid model balancing leaderboard integrity with reproducibility.
vs alternatives: Unlike benchmarks requiring cloud submission (e.g., GLUE, SuperGLUE), MMMU enables local evaluation for organizations with data privacy constraints, while still supporting official leaderboard ranking for research reproducibility.
Explicitly evaluates three integrated capabilities: (1) perception (understanding diverse visual modalities), (2) knowledge (domain-specific subject expertise), and (3) reasoning (deliberate multi-step reasoning over multimodal inputs). Questions are designed to require simultaneous visual understanding and domain knowledge application, preventing models from succeeding through either perception alone or knowledge lookup alone. This integration testing approach validates end-to-end multimodal reasoning rather than isolated sub-capabilities.
Unique: MMMU's explicit design to require simultaneous perception, knowledge, and reasoning (rather than testing each in isolation) reflects real-world expert tasks where these capabilities must be integrated. Questions cannot be solved by visual recognition alone or knowledge lookup alone, forcing genuine multimodal reasoning.
vs alternatives: Most multimodal benchmarks (MMBench, LLaVA-Bench) test visual recognition or simple visual question-answering; MMMU's integration of expert-level domain knowledge with visual reasoning creates a more realistic assessment of multimodal AI readiness for professional applications.
MMMU-Pro (introduced 2024-09-05) is a refined version of the base MMMU benchmark designed for more robust multimodal AI evaluation. The distinction from base MMMU is not fully documented in public materials, but the designation as 'robust' suggests improvements in question quality, answer verification, or evaluation methodology to reduce noise and improve benchmark reliability.
Unique: unknown — insufficient data. MMMU-Pro is mentioned as a 'robust version' but specific improvements over base MMMU are not documented in available materials.
vs alternatives: unknown — insufficient data to compare MMMU-Pro against base MMMU or other robust benchmark variants.
Provides human expert performance baseline on the full 11,500-question dataset, enabling assessment of whether AI models are approaching or exceeding human-level performance on expert-level multimodal reasoning tasks. The leaderboard (updated 2024-01-31) includes human expert scores, allowing direct comparison of AI model performance against domain expert accuracy.
Unique: MMMU's inclusion of human expert baseline (updated 2024-01-31) enables direct AI-vs-human comparison on expert-level tasks, a feature absent from many multimodal benchmarks. This design choice reflects the benchmark's focus on assessing AI readiness for professional knowledge work where human performance is the relevant reference point.
vs alternatives: Unlike benchmarks with only AI baselines (GPT-4V, Claude), MMMU's human expert baseline enables assessment of whether AI is approaching human-level performance, critical for evaluating deployment readiness in professional domains.
Questions are explicitly sourced from authentic college-level materials (exams, textbooks, lectures) rather than synthetic generation or crowdsourcing, ensuring real-world difficulty, relevance, and alignment with actual academic standards. This sourcing approach guarantees that benchmark questions reflect genuine expert-level reasoning requirements rather than artificial or simplified tasks, and reduces risk of benchmark gaming through memorization of synthetic patterns.
Unique: MMMU's explicit commitment to sourcing questions from authentic college exams, textbooks, and lectures (rather than synthetic generation) ensures benchmark questions reflect genuine expert-level reasoning requirements. This design choice reduces benchmark gaming and improves correlation with real-world expert task performance.
vs alternatives: Most multimodal benchmarks use crowdsourced or synthetically generated questions; MMMU's authentic sourcing from college materials ensures questions reflect real academic standards and reduces risk of AI systems gaming synthetic patterns without genuine reasoning capability.
MMMU scores higher at 62/100 vs VQAv2 at 47/100. VQAv2 leads on adoption and ecosystem, while MMMU is stronger on quality.
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