MMLU-Pro
MMLU-Pro - extended MMLU covering 14 categories with complex reasoning questions.
Category
Knowledge
Score type
percent
Scale
100%
higher is better
Models tested
36
What is MMLU-Pro?
MMLU-Pro is the harder successor of MMLU: 14 subject areas, ten answer options instead of four and questions that require real reasoning instead of simply recognising trivia. This reduces random-guess luck and separates strong models more clearly.
What does it test?
MMLU-Pro measures broad factual and academic knowledge. In detail, it checks:
- knowledge combined with multi-step reasoning
- robustness against guessing (10 instead of 4 options)
- harder, more discriminative questions
- broad coverage of 14 academic areas
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Frequently asked questions
What is the MMLU-Pro benchmark?
MMLU-Pro is the harder successor of MMLU: 14 subject areas, ten answer options instead of four and questions that require real reasoning instead of simply recognising trivia. This reduces random-guess luck and separates strong models more clearly.
What does MMLU-Pro test?
MMLU-Pro mainly measures broad factual and academic knowledge. Concretely, it checks: knowledge combined with multi-step reasoning; robustness against guessing (10 instead of 4 options); harder, more discriminative questions.
How is the MMLU-Pro score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get MMLU-Pro data and scores?
The official source is https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/mmlu-pro/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 36 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Kimi K2.6
Moonshot AI
|
88.1%
|
04.06.2026 | Report | Unverified |
| 2 |
Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
|
87.9%
|
— | Report | Unverified |
| 3 |
DeepSeek-V4-Pro
DeepSeek
|
87.5%
|
04.06.2026 | Report | Unverified |
| 4 |
NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
|
86.8%
|
04.06.2026 | Report | Verified |
| 5 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
86.7%
|
01.02.2026 | Report | Verified |
| 6 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
86.2%
|
22.04.2026 | Qwen Team (official model card) | Verified |
| 7 |
DeepSeek-V4-Flash
DeepSeek
|
86.2%
|
26.06.2026 | Report | Verified |
| 8 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
86.1%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 9 |
Qwen3.8-27B
Qwen Team (Alibaba Cloud)
|
85.7%
|
21.10.2026 | Report | Unverified |
| 10 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
85.3%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 11 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
85.2%
|
16.04.2026 | Qwen Team (official model card); KRAFTON re-eval (Raon-1-mini-preview card, gpt-5.6-luna harness): 85.9 | Verified |
| 12 |
Gemma4-31B-it
Google
|
85.2%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 13 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
84.4%
|
01.02.2026 | Report | Unverified |
| 14 |
Ripple-65B Preview
Ricursive Intelligence
|
84.2%
|
— | Report | Verified |
| 15 |
Muse-Glimmer-30B
Meta Superintelligence Lab
|
84%
|
21.10.2026 | Report | Unverified |
| 16 |
Qwen3-Next-80B-A3B-Thinking
Qwen
|
82.7%
|
10.09.2025 | Report | Verified |
| 17 |
Gemma4-26B-A4B
Google
|
82.6%
|
16.04.2026 | Qwen Team (official model card - comparison); KRAFTON re-eval (Raon-1-mini-preview card, gpt-5.6-luna harness): 82.9 | Unverified |
| 18 |
Qwen3-30B-A3B-Thinking-2507
Qwen
|
80.9%
|
31.07.2025 | Report | Verified |
| 19 |
Qwen3.5-4B
Qwen
|
79.1%
|
01.02.2026 | Report | Verified |
| 20 |
Qwen3-30B-A3B
Qwen
|
78.5%
|
29.04.2025 | Report | Verified |
| 21 |
ERNIE-5.5-Flash-Base
Baidu
|
78.4%
|
— | HF model card baidu/ERNIE-5.5-Flash-Base (EM, 5-shot, Unified Pipeline) | Unverified |
| 22 |
Qwen3-VL-30B-A3B
Qwen
|
77.8%
|
04.10.2025 | Report | Verified |
| 23 |
Gemma4-12B
Google
|
77.2%
|
02.07.2026 | Report | Verified |
| 24 |
Raon-1 mini
KRAFTON AI
|
76.5%
|
21.10.2026 | Report | Verified |
| 25 |
DeepSeek-V4-Pro
DeepSeek
|
73.5%
|
26.06.2026 | Report | Verified |