The MATH-500 and AIME 2024 benchmarks are both used to evaluate mathematical reasoning capabilities in AI models, but they differ in several key aspects:
1. Origin and Purpose:
- MATH-500 is derived from a larger dataset created by OpenAI, focusing on mathematical problem-solving across various difficulty levels. It is designed to assess a model's ability to solve mathematical problems in a general sense[1].
- AIME 2024 is based on the American Invitational Mathematics Examination, a prestigious competition for high-school students. It tests advanced mathematical reasoning skills, particularly in areas like algebra, geometry, and number theory[2].
2. Difficulty Level:
- MATH-500 includes a wide range of mathematical problems but is generally considered less challenging than AIME. It is used to evaluate basic mathematical reasoning capabilities[1].
- AIME 2024 consists of highly challenging questions that are significantly harder than those in MATH-500. It is designed to assess advanced mathematical skills, often surpassing the capabilities of non-reasoning models and even human performance in some cases[2].
3. Evaluation Method:
- MATH-500 uses a two-stage answer validation mechanism involving script-based grading with SymPy for symbolic equality checking and a language model equality checker as a backup. This ensures precise grading of mathematical answers[1].
- AIME 2024 evaluates models based on their ability to provide correct numerical answers to the questions. The evaluation is straightforward, focusing on the accuracy of the integer answers provided by the models[2].
4. Question Format and Availability:
- MATH-500 questions are part of a larger dataset and are not as publicly exposed as AIME questions. The dataset is used for evaluating models' mathematical capabilities without the influence of pretraining on specific questions[1].
- AIME 2024 questions and answers are publicly available, which could potentially influence model performance if the questions are included in the pretraining corpus. This has led to observations where models perform better on older versions of AIME due to potential exposure during training[2].
5. Weighting in Evaluation Suites:
- Both MATH-500 and AIME 2024 are part of the Artificial Analysis Intelligence Index, but they are weighted equally within the mathematical reasoning component, which accounts for 25% of the overall index. This means they both contribute equally to assessing a model's mathematical abilities[1].
In summary, while both benchmarks evaluate mathematical reasoning, they differ in difficulty, origin, evaluation methods, and the type of mathematical skills they assess.
Citations:
[1] https://artificialanalysis.ai/methodology/intelligence-benchmarking
[2] https://www.vals.ai/benchmarks/aime-2025-03-11
[3] https://www.credo.ai/model-trust-scores-ai-evaluation?_bhlid=c0cc9970c0c61aac64f22e2216b45b92bb72c69a
[4] https://arxiv.org/html/2502.06781v1
[5] https://github.com/GAIR-NLP/AIME-Preview
[6] https://arxiv.org/html/2503.04550
[7] https://huggingface.co/datasets/HuggingFaceH4/MATH-500
[8] https://arxiv.org/html/2410.03131v1