# MATH 2025

MATH 2025 is a benchmark dataset of 5,000 competition-level mathematics problems introduced in 2025 to evaluate large language models' reasoning abilities, with a hidden test set and a quarterly leaderboard.

MATH 2025 is a benchmark dataset designed to evaluate the mathematical reasoning capabilities of large language models (LLMs). Released in 2025, it consists of 5,000 competition-style problems spanning algebra, geometry, number theory, combinatorics, and calculus. Unlike earlier benchmarks, MATH 2025 includes a hidden test set that is not publicly accessible, aiming to reduce overfitting and provide a more robust measure of generalization. The benchmark is maintained by a consortium of academic and industry researchers, with a leaderboard updated quarterly to track progress in AI reasoning.

The dataset was introduced in early 2025 as a successor to the original MATH benchmark (2021), which had 12,500 problems. MATH 2025 focuses on a smaller but more challenging set, with problems drawn from recent olympiad-style competitions and novel contributions from participating institutions. Each problem is paired with a step-by-step solution, enabling detailed error analysis. The hidden test set is controlled via an API, preventing direct access and encouraging fair evaluation. Models are scored on exact-match accuracy, with partial credit for correct intermediate steps.

## Problem Composition and Difficulty

MATH 2025 includes 1,000 problems per topic area: algebra, geometry, number theory, combinatorics, and calculus. The difficulty levels range from high-school olympiad to undergraduate, with an average problem requiring multiple reasoning steps. For example, a sample algebra problem asks: "Find the sum of all real roots of the equation x^5 - 5x^3 + 4x = 0." A geometry problem involves proving a cyclic quadrilateral property. The problems are designed to be unambiguous, with unique numerical or short-form answers.

## Evaluation Methodology

Models are evaluated via the API, which returns a score based on exact answer matching. The leaderboard, updated quarterly (March, June, September, December), ranks submissions by overall accuracy. As of the first quarter, the top-performing model achieved 72% accuracy, while human expert baselines scored around 85%. The benchmark also reports per-topic breakdowns, revealing that geometry remains the hardest category, with average accuracy 15% lower than algebra. To prevent data contamination, the test set is rotated every six months, with new problems added from ongoing competitions.

## Impact on AI Research

MATH 2025 has become a standard reference for evaluating reasoning in [large language models](https://www.wikiprompt.org/wiki/large-language-model). It has highlighted limitations in current approaches, particularly in multi-step deduction and symbolic manipulation. Several [OpenAI](https://www.wikiprompt.org/wiki/openai) and [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind) models have used the benchmark to guide training, incorporating techniques like [curriculum learning](https://www.wikiprompt.org/wiki/curriculum-learning) and [RLHF](https://www.wikiprompt.org/wiki/rlaif) to improve performance. The benchmark has also spurred research in [chain-of-thought](https://www.wikiprompt.org/wiki/chain-of-thought) prompting, which significantly boosts accuracy on complex problems.

## Related Benchmarks and Future Directions

The original MATH 2021 benchmark remains widely used, but MATH 2025 offers a more rigorous test. Other related evaluations include [GSM8K](https://www.wikiprompt.org/wiki/gsm8k) for grade-school math and Hendrycks Math for competition problems. Future versions of MATH 2025 are planned to include more open-ended problems and automated proof verification. The consortium behind the benchmark, which includes researchers from [MIT CSAIL](https://www.wikiprompt.org/wiki/mit-csail) and [Stanford AI Lab](https://www.wikiprompt.org/wiki/stanford-ai-lab), aims to expand the dataset to 10,000 problems by 2026, with increased focus on cross-disciplinary reasoning.

## Controversies and Limitations

Some researchers have criticized the hidden test set for being opaque, making it difficult to diagnose model errors. Others note that the benchmark may still be susceptible to data leakage through indirect exposure. The API-controlled access also raises concerns about reproducibility, as not all researchers can afford repeated evaluations. Despite these issues, MATH 2025 is widely regarded as a significant step forward in AI evaluation, providing a challenging and dynamic test of mathematical intelligence.

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Source: https://www.wikiprompt.org/wiki/math-2025
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-13T03:54:33.349501+00:00