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| Pricing | Free | Free |
| Capabilities | 12 decomposed | 13 decomposed |
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Evaluates multimodal models' ability to interpret visual mathematical representations (geometry diagrams, statistical charts, scientific figures) and perform compositional reasoning combining visual perception with mathematical problem-solving. The benchmark uses a curated dataset of 6,141 examples sourced from 28 existing multimodal datasets plus 3 newly created datasets (IQTest, FunctionQA, PaperQA), with questions presented in multiple-choice and free-form generation formats. Scoring uses exact-match accuracy on the testmini subset (1,000 examples) exposed via a public leaderboard.
Unique: Combines visual understanding with mathematical problem-solving across three newly created datasets (IQTest, FunctionQA, PaperQA) plus 28 existing multimodal datasets, totaling 6,141 examples with explicit focus on compositional reasoning where visual perception and mathematical logic must be jointly applied. Unlike single-domain benchmarks, MathVista spans geometry, statistics, and scientific figures, exposing differential model performance across mathematical reasoning types.
vs alternatives: Broader than domain-specific benchmarks (e.g., geometry-only or chart-only) and more rigorous than general vision-language benchmarks because it requires both accurate visual interpretation AND correct mathematical reasoning, not just image captioning or visual QA on non-mathematical content.
Aggregates and curates 6,141 mathematical reasoning examples from 28 existing multimodal datasets and 3 newly created datasets (IQTest, FunctionQA, PaperQA) with standardized question-answer pairs. The curation process involves selecting examples that require compositional visual-mathematical reasoning, extracting or generating questions, and providing auxiliary annotations (OCR text, image captions) for text-only model baselines. Dataset is hosted on Hugging Face and includes a visualization tool for exploring examples by mathematical domain and visual context type.
Unique: Newly created datasets (IQTest, FunctionQA, PaperQA) are purpose-built for compositional visual-mathematical reasoning rather than repurposed from general vision-language tasks. Includes auxiliary annotations (OCR, captions) enabling evaluation of text-only models as baselines, revealing how much visual understanding contributes to performance vs. text-based reasoning alone.
vs alternatives: More comprehensive than single-source mathematical reasoning datasets because it aggregates 28 existing datasets plus 3 new ones, providing broader coverage of visual mathematical domains and reducing bias from any single source's annotation style or problem distribution.
MathVista is released as open-source with dataset available on Hugging Face and code available on GitHub (links provided), enabling researchers to download, analyze, and build upon the benchmark. Open-source release facilitates reproducibility, enables community contributions, and lowers barriers to adoption. Researchers can access raw data, evaluation code, and visualization tools without proprietary restrictions.
Unique: Benchmark is released as open-source with dataset on Hugging Face and code on GitHub, enabling full reproducibility and community access without proprietary restrictions. This open-source approach facilitates adoption and enables researchers to build upon benchmark.
vs alternatives: More accessible than proprietary benchmarks because open-source release enables researchers to download, analyze, and build upon benchmark without licensing restrictions or vendor lock-in.
Aggregates examples from 28 existing multimodal datasets plus 3 newly created datasets (IQTest, FunctionQA, PaperQA) into a unified benchmark with standardized question-answer format and consistent evaluation protocol. This aggregation approach combines diverse sources (existing datasets covering various visual-mathematical domains plus new datasets targeting specific reasoning types) into a single coherent benchmark. Standardization enables fair comparison across models and reduces bias from any single source's annotation style or problem distribution.
Unique: Aggregates 28 existing datasets plus 3 new datasets into unified benchmark with standardized format, combining diverse sources to reduce bias from any single source. This aggregation approach is more comprehensive than single-source benchmarks but introduces complexity in managing source bias and ensuring consistent quality.
vs alternatives: More comprehensive than single-source benchmarks because it combines diverse sources covering multiple visual-mathematical domains, reducing bias from any single dataset's annotation style or problem distribution.
Maintains a public leaderboard (testmini subset, 1,000 examples) tracking multimodal model performance on mathematical reasoning tasks with exact-match accuracy as the primary metric. The leaderboard displays rankings of models (GPT-4V at 49.9%, Gemini Ultra, Bard at ~34.8%, and others) and enables comparison of model capabilities across visual mathematical domains. Leaderboard is updated as new model submissions are evaluated, providing a living benchmark of progress in multimodal mathematical reasoning.
Unique: Leaderboard focuses specifically on mathematical reasoning (not general vision-language tasks) and exposes performance gaps between SOTA models (GPT-4V at 49.9%) and human performance (~60.3%), demonstrating that even best-in-class models fall short by 10.4 percentage points on compositional visual-mathematical reasoning. This gap motivates continued research and provides a clear target for improvement.
vs alternatives: More specialized than general vision-language leaderboards (e.g., MMVP, LLaVA-Bench) because it focuses on mathematical reasoning where visual understanding and mathematical logic must be jointly applied, not just image captioning or visual QA on non-mathematical content.
Provides OCR-extracted text and image captions for each visual example, enabling evaluation of text-only models (e.g., GPT-4 without vision) as baselines on visual mathematical reasoning tasks. This allows researchers to isolate the contribution of visual understanding vs. text-based reasoning by comparing text-only model performance (using OCR + captions) against multimodal model performance (using images). The auxiliary annotations reveal whether models can solve mathematical problems from text descriptions alone or require direct visual interpretation.
Unique: Enables ablation studies isolating the contribution of visual understanding by providing OCR and caption text alongside images. This allows direct comparison of text-only model performance (using OCR + captions) vs. multimodal model performance (using images), revealing whether mathematical reasoning requires direct visual interpretation or can be solved from text descriptions alone.
vs alternatives: More rigorous than benchmarks without text-only baselines because it quantifies the performance gap attributable to visual understanding, not just reports multimodal model accuracy. This ablation approach is standard in vision-language research but often missing from mathematical reasoning benchmarks.
Enables analysis of model performance across distinct mathematical domains (geometry, statistics, scientific figures) and visual context types, revealing which reasoning types and visual representations challenge models most. The benchmark structure supports stratified evaluation where accuracy can be computed separately for each domain, allowing researchers to identify capability gaps (e.g., models may excel at statistics but struggle with geometry). Documentation mentions performance varies significantly across mathematical reasoning types and visual context types, though specific breakdowns are not provided in public leaderboard.
Unique: Benchmark structure explicitly spans multiple mathematical domains (geometry, statistics, scientific figures) rather than focusing on single domain, enabling analysis of whether model capabilities generalize across mathematical reasoning types or are domain-specific. Documentation indicates performance varies significantly across domains, but detailed breakdowns are not published, requiring researchers to conduct their own analysis.
vs alternatives: More comprehensive than domain-specific benchmarks (e.g., geometry-only or chart-only) because it enables cross-domain comparison, revealing whether models have general visual-mathematical reasoning capabilities or domain-specific strengths/weaknesses.
Provides a web-based visualization tool (🔮 Visualize) accessible at https://mathvista.github.io for exploring individual benchmark examples, filtering by mathematical domain and visual context type, and understanding benchmark composition. The tool enables researchers to browse examples, examine model predictions vs. ground truth, and identify patterns in model failures or benchmark difficulty. This interactive exploration complements the leaderboard and dataset documentation by making benchmark content directly inspectable.
Unique: Provides interactive web-based exploration of benchmark examples rather than requiring researchers to download and process dataset locally. This lowers barrier to entry for understanding benchmark content and enables quick identification of example characteristics without programming.
vs alternatives: More accessible than static dataset documentation or leaderboard-only benchmarks because it enables interactive exploration and visual inspection of examples, making benchmark content directly inspectable rather than requiring researchers to download and analyze data themselves.
+4 more capabilities
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 MathVista at 64/100.
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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.
+5 more capabilities