OpenAI MMLU
Progress Over Time
Interactive timeline showing model performance evolution on OpenAI MMLU
OpenAI MMLU Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Google | 8B | — | — | ||
| 2 | Google | 8B | — | — |
What is OpenAI MMLU?
MMLU (Massive Multitask Language Understanding) is a comprehensive benchmark that measures a text model's multitask accuracy across 57 diverse academic and professional subjects. The test covers elementary mathematics, US history, computer science, law, morality, business ethics, clinical knowledge, and many other domains spanning STEM, humanities, social sciences, and professional fields. To attain high accuracy, models must possess extensive world knowledge and problem-solving ability.
OpenAI MMLU is a text benchmark evaluating models on legal, math, physics, psychology, reasoning, finance, general, healthcare, chemistry, and economics tasks. LLM Stats tracks 2 models on this benchmark, scored on a 0–1 scale. The current average is 0.3, with the leader at 0.4.
Compare leaders on the best AI for legal, best AI for math, best AI for physics, best AI for psychology, best AI for reasoning, best AI for finance, best AI for general, best AI for healthcare, best AI for chemistry and best AI for economics leaderboards.
Current leaders
Gemma 3n E4B Instructed from Google currently leads the OpenAI MMLU leaderboard with a score of 0.356 across 2 evaluated AI models.
Source paper
- Title
- Measuring Massive Multitask Language Understanding
- Authors
- Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, and 3 others
- Published
- arXiv
- 2009.03300
Abstract
We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.
FAQ
Common questions about the OpenAI MMLU benchmark and leaderboard.