KorMedMCQA-V - Two Images: leaderboard

Metric: Accuracy (%) on the 426 questions with two images (KorMedMCQA-V: image-based five-option questions from the Korean Medical Licensing Examination 2012-2023 (doctor track, R-type items excluded), each with one or more clinical images; zero-shot, closed-book, answer label parsed from JSON; open-source models averaged over three seeds); higher is better. Source: arxiv.org. Saturation forecast: Estimated already saturated. 51 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3 Pro96#77
2Gemini 3 Flash95.5#93
3GPT-5.292.7#105
4GPT-592#91
5GPT-5 Mini (2025-08-07)87.3#165
6Qwen 3 VL 32B (Thinking)80.5#287 (Qwen 3 VL 32B)
7Qwen 3 VL 30B A3B (Thinking)78.3#338 (Qwen 3 VL 30B A3B)
8GLM-4.6V75.1#309
9Qwen 3 VL 32B Instruct74.5#276
10Qwen 3 VL 8B (Thinking)71.4
11Ministral-3-14B-Reasoning-251271.4#519
12Qwen 3 VL 30B A3B Instruct71#365
13Ministral-3-8B-Reasoning-251262.9#694
14Qwen 3 VL 4B (Thinking)61.5#471 (Qwen 3 VL 4B)
15Qwen 2.5 VL 32B Instruct61.4#443

No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.

Interactive version: theaggregate.ai/benchmark?slug=kormedmcqa-v-two-images · How It Works · Data refreshed daily, snapshot 2026-10-11.