math benchmark
MATH dataset contains 12,500 challenging competition mathematics problems from AMC 10, AMC 12, AIME, and other mathematics competitions. Each problem includes full step-by-step solutions and spans multiple difficulty levels (1-5) across seven mathematical subjects. This variant uses Chain-of-Thought prompting to encourage step-by-step reasoning.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | ME | 68.0% | 100.0% | 6 | C | |
| 02 | MA | 67.6% | 80.0% | 6 | C | |
| 03 | MA | 67.6% | 60.0% | 6 | C | |
| 04 | MA | 62.6% | 40.0% | 6 | C | |
| 05 | MA | 60.1% | 20.0% | 6 | C | |
| 06 | ME | 51.9% | 0.0% | 6 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What MATH (CoT) measures and how its scores work.
MATH dataset contains 12,500 challenging competition mathematics problems from AMC 10, AMC 12, AIME, and other mathematics competitions. Each problem includes full step-by-step solutions and spans multiple difficulty levels (1-5) across seven mathematical subjects. This variant uses Chain-of-Thought prompting to encourage step-by-step reasoning.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MATH (CoT).
Llama 3.1 70B Instruct is currently ranked first with 68.0%.
MATH dataset contains 12,500 challenging competition mathematics problems from AMC 10, AMC 12, AIME, and other mathematics competitions. Each problem includes full step-by-step solutions and spans multiple difficulty levels (1-5) across seven mathematical subjects. This variant uses Chain-of-Thought prompting to encourage step-by-step reasoning.
Yes. Higher values rank better for this benchmark.
6 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.