FunctionalMATH
A functional variant of the MATH benchmark that tests language models' ability to generalize reasoning patterns across different problem instances, revealing the reasoning gap between static and functional performance.
Gemini 1.5 Pro from Google currently leads the FunctionalMATH leaderboard with a score of 0.646 across 2 evaluated AI models.
What FunctionalMATH measures
FunctionalMATH is a text benchmark that evaluates large language models on math and reasoning tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.6, with the leader reaching 0.6.
Compare leaders on the best AI for math and best AI for reasoning leaderboards.
Publication
- Paper
- Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap
- Authors
- Saurabh Srivastava, Annarose M B, Anto P, Shashank Menon, and 5 others
- Published
- arXiv
- 2402.19450
Abstract
We propose a framework for robust evaluation of reasoning capabilities of language models, using functional variants of benchmarks. Models that solve a reasoning test should exhibit no difference in performance over the static version of a problem compared to a snapshot of the functional variant. We have rewritten the relevant fragment of the MATH benchmark into its functional variant MATH(), with functionalization of other benchmarks to follow. When evaluating current state-of-the-art models over snapshots of MATH(), we find a reasoning gap -- the percentage difference between the static and functional accuracies. We find reasoning gaps from 58.35% to 80.31% among the state-of-the-art closed and open weights models that perform well on static benchmarks, with the caveat that the gaps are likely to be smaller with more sophisticated prompting strategies. Here we show that models which anecdotally have good reasoning performance over real-world tasks, have quantifiable lower gaps, motivating the open problem of building "gap 0" models. Code for evaluation and new evaluation datasets, three MATH() snapshots, are publicly available at https://github.com/consequentai/fneval/.
Gemini 1.5 Pro leads with 64.6%, followed by Gemini 1.5 Flash at 53.6%.
Progress Over Time
Interactive timeline showing model performance evolution on FunctionalMATH
FunctionalMATH Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Google | — | — | — | ||
| 2 | Google | — | — | — |
FAQ
Common questions about FunctionalMATH.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions