Evals & Safety
MATH Benchmark
A dataset of 12,500 competition mathematics problems, each with a full step-by-step solution, released by Dan Hendrycks and colleagues in 2021.
In depth
A dataset of 12,500 competition mathematics problems, each with a full step-by-step solution, released by Dan Hendrycks and colleagues in 2021. Models are graded on the final answer. The commonly reported MATH-500 is a subset of 500 test problems that OpenAI chose at random for its Let's Verify Step by Step paper. Reasoning models now score above 90% on MATH-500, so harder sets like AIME and FrontierMath carry more signal.
Primary source: arxiv.org
Example
Reasoning models now score above 90% on MATH-500, so harder sets like AIME and FrontierMath carry more signal.
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FAQ
Common questions
What is MATH Benchmark?
A dataset of 12,500 competition mathematics problems, each with a full step-by-step solution, released by Dan Hendrycks and colleagues in 2021.
Why does MATH Benchmark matter for AI developers?
MATH Benchmark sits in the Evals & Safety part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about MATH Benchmark?
Developers Digest publishes tutorials and videos that cover Evals & Safety topics including MATH Benchmark. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
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MLE-bench
An OpenAI benchmark that tests AI agents on machine learning engineering, using 75 real Kaggle competitions that require preparing data, training models, and running experiments.
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MMLU
Massive Multitask Language Understanding, a multiple-choice benchmark covering 57 subjects such as elementary math, US history, computer science, and law.
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HumanEval
A code generation benchmark from OpenAI made of 164 hand-written Python programming problems, each with a function signature, a docstring, and unit tests.
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