KorMedMCQA-V - ECG: leaderboard

Metric: Accuracy (%) on the questions carrying the 164 electrocardiogram 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 Pro95.9#77
2Gemini 3 Flash93.9#93
3GPT-5.292.9#105
4GPT-589.8#91
5Qwen 3 VL 32B (Thinking)86.4#287 (Qwen 3 VL 32B)
6GPT-5 Mini (2025-08-07)85.7#165
7Qwen 3 VL 32B Instruct83.3#276
8Qwen 3 VL 30B A3B (Thinking)82.3#338 (Qwen 3 VL 30B A3B)
9GLM-4.6V80.3#309
10Qwen 3 VL 30B A3B Instruct75.5#365
11Qwen 3 VL 8B (Thinking)74.8
12Qwen 2.5 VL 32B Instruct71.8#443
13Ministral-3-14B-Reasoning-251271.1#519
14Qwen 3 VL 4B (Thinking)70.4#471 (Qwen 3 VL 4B)
15Ministral-3-8B-Reasoning-251266.7#694

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-ecg · How It Works · Data refreshed daily, snapshot 2026-10-11.