DDR-Bench - 10-K - Sample-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 - 10-K - Sample-Averaged Message-Wise using Checklist Coverage Accuracy (%).

Metric: Checklist Coverage Accuracy (%). Source: arxiv.org. Saturation forecast: Around February 2027. 22 models tracked.

Top models

#ModelScoreOverall rank
1Claude Sonnet 4.577.61#148
2GLM-4.660.31#265
3DeepSeek V3.260.08#210
4GPT-5 Mini46.82#193
5GPT-5.244.89#115
6Gemini 3 Flash44.82#103
7Qwen 3 Next 80B A3B Instruct44.76#381
8MiniMax-M244.17#327
9Qwen 3 4B 2507 Instruct41.13#768
10GPT-5.137.12#145
11Qwen 2.5 72B Instruct27.13#469
12Qwen 2.5 32B Instruct27.07#509
13Gemini 2.5 Pro24.48#159
14Qwen2.5-14B-Instruct-1M22.69#581
15Gemini 2.5 Flash Lite19.45#432

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-10-k-sample-averaged-message-wise · How It Works · Data refreshed daily, snapshot 2026-11-05.