AI Agents
SWE-bench_Pro-os
A benchmark testing whether agents can solve long-horizon coding tasks.
★ 539 starsMIT
View guide →The tougher, more robust successor to the MMLU benchmark.
MMLU-Pro is an open-source ai agents project by TIGER-AI-Lab (TIGER-AI-Lab/MMLU-Pro on GitHub). It has 429 GitHub stars and 58 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 18 March 2026.
Write down the outcome you want in one sentence.
Clone it locally and check the README.
Get dependencies and tooling in place per the docs.
Follow the minimal example, then adapt it to your needs.
Beyond what MMLU could separate.
Ten options per question raise the bar.
Breadth across knowledge domains.
Code and data, open to all.
Peer-reviewed benchmark design.
Increasingly the test labs must pass.
Download the source and move into the project folder.
git clone https://github.com/TIGER-AI-Lab/MMLU-Pro.git
cd MMLU-ProIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m mmlu_pro # check README for the exact commandIt is an open-source project at TIGER-AI-Lab/MMLU-Pro with 58 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — Apache 2.0 allows commercial use and modification. Keep the license and notice files, and note it includes a patent grant.
More answer choices per question and harder items overall — designed to separate frontier models.
Clone the repo and follow the eval instructions; it's code and data, not a service.
Clone the repo and follow the README quickstart — the README is the best starting point.
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