The MMLU
The MMLU (Massive Multitask Language Understanding) benchmark is currently the most referenced and most reputable benchmark for evaluating the general intelligence of large language models (LLMs), regardless of size.
About the MMLU
The MMLU rating consists of 16,000 multiple-choice questions across 57 academic subjects. Questions are given to a LLM in the form of text. The LLM must return the correct answer to get a point.
Note that most human's do not get a perfect score. The average person only gets a score of 89.6% correct.
Because the MMLU is so popular, many people feel that some LLM builders pre-train their models on MMLU questions. This will skew the results and make some models appear more capable than they really are.
✅ Why MMLU is the Most Trusted Benchmark
| Feature | Explanation |
|---|---|
| Comprehensive Coverage | Tests models across 57 subjects spanning STEM, humanities, law, and more. |
| Multistep Reasoning | Focuses on tasks requiring knowledge recall, reasoning, and comprehension, not just surface-level text matching. |
| Human Comparison | Allows comparison to average human performance (e.g., college graduate benchmarks). |
| Adopted by Major Players | Used in the evaluation of GPT-4 (OpenAI), Gemini (Google DeepMind), Claude (Anthropic), LLaMA (Meta), and others. |
| Backed by Academics | Developed by AI alignment researchers including Dan Hendrycks at UC Berkeley. |
| Stable & Reproducible | Public, well-documented, and widely supported in academic and commercial research. |
🏆 Other Prominent Benchmarks (Honorable Mentions)
| Name | Focus Area | Notes |
|---|---|---|
| HELLASWAG | Commonsense inference | Good for measuring plausibility reasoning but less broad than MMLU. |
| ARC-Challenge | Grade-school level science questions | Focuses on reasoning, useful but narrower scope. |
| Big-Bench Hard (BBH) | Multitask difficult questions | Curated by Google; strong but newer and less universal than MMLU. |
| HumanEval / MBPP | Code generation | Specialized in programming, not general reasoning. |
| TruthfulQA | Truthfulness & factual consistency | Measures how likely a model is to hallucinate; not comprehensive in scope. |
| Elo Ratings (Arena) | Battle-tested via model-vs-model voting | Popular in community evals (e.g., LMSYS), but less academic and not skill-targeted. |
🧠 Summary
If you're looking for a single benchmark that best captures the overall academic reputation, adoption by leading labs, and usefulness for comparing models across general tasks, then:
MMLU is the gold standard.
Would you like a chart comparing how top models rank on MMLU?