CheXpercept: leaderboard
The evaluation of vision-language models (VLMs) for chest X-ray (CXR) analysis has largely been limited to disease-presence classification without visual grounding. Such evaluations fail to verify the expert-level lesion perception necessary to ensure the clinical reliability of VLMs. To address these limitations, we introduce CheXpercept, a sequential, multi-level perception benchmark that mirror
Metric: Stage 1 (End-to-End) (self-reported). Source: benchmarklist.com. Saturation forecast: Around July 2027. 14 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen 3.6 27B | 92.2 | #170 |
| 2 | Qwen 3.5 122B A10B | 91.3 | #173 |
| 3 | Gemini 3.1 Pro (Preview) | 90 | #58 |
| 4 | Gemma 4 31B | 88.5 | #200 |
| 5 | Gemini 3.1 Flash Lite (Preview) | 87.1 | #194 |
| 6 | GPT-5.4 | 84.3 | #79 |
| 7 | GPT-5.4 Nano | 76.9 | #328 |
| 8 | GLM-4.6V | 69.3 | #316 |
| 9 | MedGemma 1.5 4B | 66.8 | #789 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=chexpercept · How It Works · Data refreshed daily, snapshot 2026-10-20.