FrontierMath
BenchmarkFreeExpert-level math problems created by mathematicians.
Capabilities5 decomposed
expert-authored frontier mathematics problem curation
Medium confidenceCurates several hundred original, unpublished mathematics problems authored and peer-reviewed by expert mathematicians across number theory, algebra, geometry, and analysis. Problems are tiered from undergraduate through research-level difficulty (Tiers 1-4), with a separate collection of genuinely unsolved problems that have resisted professional mathematician attempts. The curation process involves expert validation to ensure problems are novel, mathematically sound, and appropriately calibrated for difficulty.
Uses unpublished, expert-authored problems across four mathematical subdisciplines with explicit tiering from undergraduate to research level, plus a separate collection of genuinely unsolved problems — avoiding contamination from public datasets and testing on problems that have resisted professional mathematician attempts
Differs from MATH and other public benchmarks by using original, unpublished problems authored by expert mathematicians with peer review, providing frontier-level difficulty calibration that public datasets cannot offer
multi-tier mathematical difficulty stratification
Medium confidenceOrganizes problems into four explicit difficulty tiers (Tiers 1-4) spanning undergraduate through postdoctoral to research-level mathematics, enabling granular measurement of AI reasoning capability across the difficulty spectrum. This tiered structure allows evaluation of whether models can progress from foundational to frontier-level problem-solving, with separate tracking of performance at each tier to identify capability boundaries.
Explicitly structures problems into four tiers from undergraduate through research level with peer-reviewed expert calibration, enabling fine-grained measurement of where AI reasoning capabilities plateau rather than binary pass/fail assessment
More granular than single-difficulty benchmarks; provides tier-specific performance tracking that reveals capability boundaries and progression, whereas most benchmarks report aggregate scores
unsolved mathematics problem evaluation
Medium confidenceMaintains a separate collection of genuinely unsolved mathematics problems that have resisted serious attempts by professional mathematicians, enabling evaluation of whether AI can make progress on open research problems. The evaluation approach for these problems is unspecified but conceptually distinct from standard problem-solving — measuring whether AI can contribute novel insights, partial solutions, or proof strategies to problems without known solutions.
Includes a dedicated collection of genuinely unsolved problems that professional mathematicians have not solved, testing whether AI can generate novel mathematical insights rather than reproduce known solutions — a capability distinct from standard benchmarking
Unique among mathematics benchmarks in explicitly including unsolved problems; most benchmarks measure performance on problems with known solutions, whereas this tests AI's potential for actual mathematical discovery
cross-subdiscipline mathematical reasoning measurement
Medium confidenceEvaluates mathematical reasoning across four distinct subdisciplines (number theory, algebra, geometry, analysis) within a single benchmark, enabling assessment of whether AI reasoning generalizes across mathematical domains or exhibits domain-specific strengths and weaknesses. The multi-subdiscipline structure allows identification of which mathematical areas AI handles well versus poorly.
Explicitly structures evaluation across four mathematical subdisciplines (number theory, algebra, geometry, analysis) to measure generalization and identify domain-specific reasoning patterns, rather than treating mathematics as a monolithic domain
Provides subdiscipline-specific performance insights that reveal whether AI reasoning is broadly generalizable or domain-dependent, whereas most benchmarks report aggregate mathematical performance
independent ai capability measurement and publication
Medium confidenceOperates as a free, open-source benchmark maintained by Epoch AI (a nonprofit focused on neutral, evidence-grounded AI capability measurement) with no commercial incentives or vendor lock-in. The benchmark is designed for independent evaluation of AI models, enabling researchers and organizations to assess frontier mathematical reasoning without reliance on proprietary evaluation infrastructure or vendor-controlled leaderboards.
Maintained by Epoch AI, a nonprofit focused on neutral AI capability measurement with no commercial incentives, providing independent evaluation infrastructure free from vendor bias or proprietary constraints — distinct from benchmarks maintained by AI companies with commercial interests
Provides neutral, nonprofit-maintained evaluation infrastructure without vendor bias, whereas benchmarks from OpenAI, Anthropic, or Google may have incentives to favor their own models or present results in commercially advantageous ways
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with FrontierMath, ranked by overlap. Discovered automatically through the match graph.
MATH Benchmark
12.5K competition math problems — AMC/AIME/Olympiad level, 7 subjects, standard math benchmark.
MATH
12.5K competition math problems across 7 subjects and 5 difficulty levels.
GSM8K
8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.
DeepSeek R1
Open-source reasoning model matching OpenAI o1.
APPS (Automated Programming Progress Standard)
10K coding problems across 3 difficulty levels with test suites.
MATH
Competition mathematics problems (harder than GSM8K)
Best For
- ✓AI capability researchers measuring frontier mathematical reasoning
- ✓Organizations conducting independent model evaluations
- ✓Teams building mathematical reasoning systems who need ground-truth difficulty calibration
- ✓Researchers studying AI capability scaling and frontier boundaries
- ✓Teams building progressive mathematical reasoning systems
- ✓Organizations publishing model evaluation reports with granular difficulty analysis
- ✓Research organizations studying AI's potential for mathematical discovery
- ✓Teams evaluating whether AI can contribute to open research problems
Known Limitations
- ⚠Exact problem count unknown — documentation states 'several hundred' without precise inventory
- ⚠No public leaderboard or baseline performance data available to contextualize results
- ⚠Problem format specifications unknown — unclear if problems require proofs, numerical answers, or symbolic computation
- ⚠No information on train/test split or data contamination screening procedures
- ⚠Evaluation methodology for genuinely unsolved problems not specified
- ⚠Tier definitions and calibration methodology not specified in documentation
Requirements
Input / Output
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UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
Expert-level mathematics benchmark containing original problems created by mathematicians across number theory, algebra, geometry, and analysis, designed to test mathematical reasoning far beyond current AI capabilities.
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