MMLU-ProX

Extended version of MMLU-Pro providing additional challenging multiple-choice questions for evaluating language models across diverse academic and professional domains. Built on the foundation of the Massive Multitask Language Understanding benchmark framework.

Qwen3.5-397B-A17B from Alibaba Cloud / Qwen Team currently leads the MMLU-ProX leaderboard with a score of 0.847 across 29 evaluated AI models.

Paper

Alibaba Cloud / Qwen TeamQwen3.5-397B-A17B leads with 84.7%, followed by Alibaba Cloud / Qwen TeamQwen3.6 Plus at 84.7% and Alibaba Cloud / Qwen TeamQwen3.5-122B-A10B at 82.2%.

Progress Over Time

Interactive timeline showing model performance evolution on MMLU-ProX

State-of-the-art frontier
Open
Proprietary

MMLU-ProX Leaderboard

29 models
ContextCostLicense
1
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
397B262K$0.60 / $3.60
1
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
1.0M$0.50 / $3.00
3
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
122B262K$0.40 / $3.20
3
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
27B262K$0.30 / $2.40
5
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
35B262K$0.25 / $2.00
5
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
235B
7
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B
8
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
235B
9120B
10
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
80B
11
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.30 / $1.49
12
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
13
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
80B
14
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B
15
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
16
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
17
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B
18
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
19
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.18 / $2.09
20
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.08 / $0.50
21
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $1.00
2232B262K$0.06 / $0.24
23
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $0.60
24
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
2B
25
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
800M
262B
268B
288B
282B
Notice missing or incorrect data?

FAQ

Common questions about MMLU-ProX.

What is the MMLU-ProX benchmark?

Extended version of MMLU-Pro providing additional challenging multiple-choice questions for evaluating language models across diverse academic and professional domains. Built on the foundation of the Massive Multitask Language Understanding benchmark framework.

What is the MMLU-ProX leaderboard?

The MMLU-ProX leaderboard ranks 29 AI models based on their performance on this benchmark. Currently, Qwen3.5-397B-A17B by Alibaba Cloud / Qwen Team leads with a score of 0.847. The average score across all models is 0.647.

What is the highest MMLU-ProX score?

The highest MMLU-ProX score is 0.847, achieved by Qwen3.5-397B-A17B from Alibaba Cloud / Qwen Team.

How many models are evaluated on MMLU-ProX?

29 models have been evaluated on the MMLU-ProX benchmark, with 0 verified results and 29 self-reported results.

Where can I find the MMLU-ProX paper?

The MMLU-ProX paper is available at https://arxiv.org/abs/2406.01574. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does MMLU-ProX cover?

MMLU-ProX is categorized under finance, general, healthcare, language, legal, math, and reasoning. The benchmark evaluates text models.

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