Overview
MATH-500 is a curated subset of the original MATH dataset, which was created by OpenAI to benchmark mathematical reasoning capabilities of AI models. The 500-problem selection represents challenging competition-level mathematics that requires sophisticated problem-solving skills.
Domain Coverage
The benchmark covers six major mathematical domains:
| Domain | Topics Included |
|---|---|
| Algebra | Equations, polynomials, sequences, functions |
| Geometry | Shapes, proofs, spatial reasoning, theorems |
| Number Theory | Divisibility, modular arithmetic, prime numbers |
| Counting & Probability | Combinatorics, probability distributions |
| Precalculus | Limits, derivatives, complex numbers |
| Intermediate Algebra | Advanced algebraic manipulations |
Difficulty Level
Problems in MATH-500 are sourced from:
- Math competitions: AIME, AMC, and similar level challenges
- Textbook exercises: Challenging problems from advanced math texts
- Competition preparation materials: Problems designed to train competitive mathematicians
Evaluation Approach
Models are evaluated based on:
- Numerical Accuracy: Answers must be exact (typically integers or simplified fractions)
- Step-by-step reasoning: Models show their work
- Multiple solution paths: Some problems have elegant and brute-force solutions
Purpose
MATH-500 tests whether AI models can:
- Perform complex mathematical calculations
- Apply mathematical theorems and properties
- Think creatively about problem-solving
- Present clear mathematical reasoning
This benchmark is particularly important because mathematics requires both computational accuracy and abstract reasoning - capabilities that distinguish sophisticated AI systems.
Source: OpenAI
