DDR-Bench - MIMIC - Item-Averaged Message-Wise: leaderboard

Evaluates autonomous database exploration with SQL/Python tools on MIMIC, GLOBEM and 10-K. GPT-5 mini checks coverage of 2,058 checklist items across 291 task entities. Message-wise and trajectory-wise insights use sample- or item-averaging. Paper v1 scores are preserved; current repository defaults include a 100-turn safety cap. Released financial-trajectory labels resolve eight Qwen instruct/checkpoint identities; ambiguous Qwen3-30B-A3B and Kimi K2 snapshots remain explicit source-local rows. This series reports DDR-Bench - MIMIC - Item-Averaged Message-Wise using Checklist Coverage Accuracy (%).

Metric: Checklist Coverage Accuracy (%). Source: arxiv.org. Saturation forecast: Around 2029. 22 models tracked.

Top models

#ModelScoreOverall rank
1Claude Sonnet 4.534.37#148
2GPT-5 Mini28.81#193
3GPT-5.227.26#115
4DeepSeek V3.227#210
5GPT-5.126.61#145
6Gemini 3 Flash24.94#103
7MiniMax-M223.9#327
8GLM-4.623.26#265
9Gemini 2.5 Pro19.51#159
10Qwen 3 Next 80B A3B Instruct16.8#381
11Qwen 3 4B 2507 Instruct16.67#768
12Gemini 2.5 Flash Lite16.1#432
13Qwen2.5-14B-Instruct-1M15.5#581
14Gemini 2.5 Flash14.99#255
15Qwen 2.5 14B Instruct14.86#654

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=ddr-bench-mimic-item-averaged-message-wise · How It Works · Data refreshed daily, snapshot 2026-11-05.