DDR-Bench - 10-K - 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 - 10-K - Item-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 4.577.27#148
2DeepSeek V3.260.66#210
3GLM-4.660.42#265
4GPT-5 Mini46.35#193
5Qwen 3 Next 80B A3B Instruct45.58#381
6GPT-5.244.99#115
7MiniMax-M244.66#327
8Gemini 3 Flash44.41#103
9Qwen 3 4B 2507 Instruct40.94#768
10GPT-5.137.69#145
11Qwen 2.5 72B Instruct27.56#469
12Qwen 2.5 32B Instruct27.53#509
13Gemini 2.5 Pro25.68#159
14Qwen2.5-14B-Instruct-1M23.56#581
15Gemini 2.5 Flash Lite19.32#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-item-averaged-message-wise · How It Works · Data refreshed daily, snapshot 2026-11-05.