AutoMedBench - Segmentation: leaderboard
The segmentation track of AutoMedBench (2026), which asks an LLM agent to complete end-to-end medical-AI research tasks built from public challenge datasets: eight CT, MRI and ultrasound segmentation tasks. Each run is scored 0 to 100 as the mean of the held-out result (macro Dice) and a five-stage workflow score (plan, setup, validate, inference, submit), averaged over the Lite and Standard tiers. Higher is better.
Metric: Overall run score (0-100) on the segmentation track (8 CT, MRI and ultrasound tasks): the mean of the held-out task score (macro Dice, scaled to 0-100) and the workflow agentic score (Plan, Setup, Validate, Inference and Submit stages weighted 25/15/35/15/10, LLM judge for the first three); each model is the single agent of the benchmark harness (shared code-execution interface, system prompt, tool schema and default decoding; no vendor agent framework), runs averaged over the Lite and Standard tiers; higher is better. Source: arxiv.org. Saturation forecast: Around February 2027. 6 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Opus 4.6 | 67.2 | #102 |
| 2 | GPT-5.4 | 59.4 | #123 |
| 3 | GLM-5 | 58 | #260 |
| 4 | Gemini 3.1 Pro (Preview) | 54.7 | #94 |
| 5 | MiniMax-M2.5 | 46.5 | #582 |
| 6 | Qwen 3.5 397B A17B | 42.8 | #246 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=automedbench-segmentation · How It Works · Data refreshed daily, snapshot 2026-11-13.