# MMLU vs MMLU-Pro: What Changed and Why It Matters

> MMLU and MMLU-Pro are the most cited knowledge benchmarks in AI. Here's what each measures, why MMLU is saturated, and why MMLU-Pro is the better discriminator in 2026.

- Published: 2026-03-07
- Article slug: mmlu-vs-mmlu-pro
- Author: [Glevd](https://x.com/glevd)
- Reading time: 7 minutes
- Topics: benchmarks, knowledge, mmlu, explainer
- Data policy: Dated analysis; static benchmark claims are retained.
- Canonical URL: https://benchlm.ai/blog/posts/mmlu-vs-mmlu-pro

MMLU is saturated: frontier models score 97-99% and the top 5 models are separated by just 2 points. MMLU-Pro fixes this with 10-choice questions and harder reasoning problems, creating a meaningful 88-93 spread that actually differentiates today's best models.

MMLU (Massive Multitask Language Understanding) has been the go-to knowledge benchmark since 2020. It tests models across 57 academic subjects with multiple-choice questions ranging from elementary to professional difficulty. But with frontier models now scoring 97-99%, it's lost its ability to separate the best from the rest.

MMLU-Pro was designed to fix this.

## How MMLU works, and what MMLU-Pro changes

MMLU presents 4-choice multiple-choice questions across subjects like history, biology, computer science, law, and mathematics. A model reads a question and picks A, B, C, or D.

With 4 choices, random guessing gives you 25%. Early models struggled to beat 40-50%. Today's frontier models score 97-99%, meaning the benchmark is effectively saturated.

See current scores: [MMLU leaderboard](/benchmarks/mmlu)

MMLU-Pro makes three key improvements:

1. **10 answer choices instead of 4**
   
   — Random guessing drops from 25% to 10%, reducing the role of luck
2. **More reasoning-focused questions**
   
   — Harder questions that require multi-step thinking, not just recall
3. **Better discrimination**
   
   — Top model scores range from ~88-93 instead of 97-99, creating meaningful separation

This makes MMLU-Pro a much better benchmark for comparing frontier models. A 5-point gap on MMLU-Pro is more informative than a 1-point gap on MMLU.

See current scores: [MMLU-Pro leaderboard](/benchmarks/mmlu-pro)

## Current rankings comparison

| Model | MMLU | MMLU-Pro |
| --- | --- | --- |
| GPT-5.4 | 99 | 93 |
| Claude Opus 4.6 | 99 | 82 |
| GPT-5.3 Codex | 99 | 90 |
| GPT-5.2 | 99 | 88 |
| Gemini 3.1 Pro | 97 | 92 |

On MMLU, the top models are within 2 points. On MMLU-Pro, the spread widens to 11 points. That's the difference between "all models are basically the same" and "there are real performance differences here."

## Which should you look at?

If you're evaluating models in 2026:

- **Use MMLU-Pro**
  
  for comparing frontier models. It's harder and better at showing real differences.
- **Use MMLU as a baseline**
  
  : a model scoring below 90 on MMLU may not be competitive for knowledge-intensive tasks.
- **Combine with HLE**
  
  :
  
  [Humanity's Last Exam](/benchmarks/hle)
  
  is even harder and shows the largest spread among frontier models (10-46% scores).

For a complete view, check our [knowledge benchmark rankings](/knowledge) or compare specific models on their [benchmark detail pages](/benchmarks).

→ [See all models ranked on the full leaderboard](/)

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## Frequently asked questions

### What is MMLU?

MMLU (Massive Multitask Language Understanding) is a benchmark that tests AI models across 57 academic subjects with 4-choice multiple-choice questions. Introduced in 2020, it covers everything from history and biology to law and mathematics. Frontier models now score 97-99%, meaning it is effectively saturated and no longer useful for comparing the best models.

### What is MMLU-Pro and how does it differ from MMLU?

MMLU-Pro extends MMLU with 10-choice questions instead of 4, harder reasoning-focused problems, and a wider score spread (88-93 for top models vs 97-99 for MMLU). The extra answer choices reduce the role of lucky guessing and force models to reason rather than recall. MMLU-Pro is the better benchmark for comparing frontier models in 2026.

### Is MMLU still a useful benchmark in 2026?

MMLU has limited value for comparing frontier models because the top 5 models all score 97-99%, leaving almost no signal. It remains useful as a baseline — a model scoring below 90 on MMLU is unlikely to be competitive for knowledge-intensive tasks — but MMLU-Pro and HLE are better discriminators at the frontier.

### Which model scores highest on MMLU-Pro?

As of July 2026, GPT-5.4 leads MMLU-Pro with a score of 93, followed by Gemini 3.1 Pro at 92 and GPT-5.3 Codex at 90. See the full rankings on the BenchLM.ai MMLU-Pro leaderboard.

### What benchmark should I use instead of MMLU?

For comparing frontier models, use MMLU-Pro for broad knowledge depth, SuperGPQA for breadth across 285 disciplines, or HLE (Humanity's Last Exam) for the hardest questions where top models score only 10-46%. Any of these provides more signal than MMLU for today's frontier models.