MATH (CoT)
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.
Llama 3.1 70B Instruct from Meta currently leads the MATH (CoT) leaderboard with a score of 0.680 across 6 evaluated AI models.
Llama 3.1 70B Instruct leads with 68.0%, followed by 
Ministral 3 (14B Base 2512) at 67.6% and Mistral Large 3 at 67.6%.
Progress Over Time
Interactive timeline showing model performance evolution on MATH (CoT)
MATH (CoT) Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 70B | 128K | $0.20 / $0.20 | |||
| 2 | Mistral AI | 14B | — | — | ||
| 2 | Mistral AI | 675B | 128K | $2.00 / $5.00 | ||
| 4 | Mistral AI | 8B | — | — | ||
| 5 | Mistral AI | 3B | — | — | ||
| 6 | 8B | 131K | $0.03 / $0.03 |
FAQ
Common questions about MATH (CoT).
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions