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| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
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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.
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs MMMU at 62/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
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