Benchmark Almanac

KMMMU

EXAONE 4.5 33B 42.7% Fading

Models answer 3,466 college and exam level questions that combine images with Korean text. Questions come from Korean exam sources, some with several images.

A Korean counterpart of MMMU. The dataset repo was created in 2026-02 and the technical report followed in 2026-04.

In the authors’ words

Multimodal evaluation is strengthened when benchmarks reflect the languages, institutions, and disciplinary conventions that shape expert visual reasoning. We introduce KMMMU, a native Korean benchmark built from Korean-native examination materials. KMMMU contains 3,466 questions spanning nine disciplines and nine visual modality categories. For targeted evaluation on challenging questions and Korean-specific knowledge, we additionally provide KMMMU-Adv and KMMMU-Local, respectively. Experiments show that the strongest evaluated open-weight model reaches 42.05% on the KMMMU, while the best evaluated proprietary model achieves 52.42% on the KMMMU-Adv. Performance varies substantially across disciplines and answer formats, with Korean-specific questions showing gaps of up to 13.43%. Error analysis suggests that inspected failures often involve convention-to-label mapping, few-shot symbolic induction, localized knowledge recall, and domain-specific standards, not reasoning depth alone. KMMMU provides a testbed for native Korean expert-level multimodal understanding beyond English-centric or translation-based evaluation.

Abstract of KMMMU: Evaluation of Massive Multi-discipline Multimodal Understanding in Korean Language and Context (arXiv:2604.13058, CC0)
Made by HAE-RAE Released Apr 2026 General visual questions All tasks public Last in a launch post Apr 2026 , dropped before it saturated Last model added Apr 2026
Level
University Undergraduates and entry-level professionals
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Multiple choice
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
1
System card reports it, from 1 lab

EXAONE 4.5 33B leads with 42.7%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 EXAONE 4.5 33B 42.7%
#ModelScoreSettingDateSource
1 EXAONE 4.5 33B 42.7% Apr 2026 Model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) EXAONE 4.5 33B 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) EXAONE 4.5 33B

Labs headline it less often now, last in Apr 2026

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

LG AI Research
DateLaunch postScoreAs the post writes it
9 Apr 2026 EXAONE 4.5 33B 42.7 KMMMU

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
9 Apr 2026 EXAONE 4.5 33B technical report 42.7 KMMMU (Table 2, Reasoning mode) Capabilities

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