SQBench: leaderboard

SQBench (Shaqiu Community, 2026) evaluates language-model agents on 220 production-oriented delivery tasks that require processing input assets, using tools and producing a specified deliverable. A task passes only when the deliverable is functionally complete and triggers no penalty in a 10-dimension risk matrix; the primary score weights the pass rates of the three task layers 0.2, 0.6 and 0.2, in percent, higher is better.

Metric: Weighted Strict Pass@1 (%; 0.2 times L1 plus 0.6 times L2 plus 0.2 times L3 strict pass rates, the prespecified primary aggregate on SQBench v1.0, 220 production-oriented delivery tasks in which an agent processes input assets, uses tools and produces a specified deliverable; a task passes only with functional Completion 1 and no triggered 10-dimension risk penalty; one run per configuration and task; higher is better). Source: arxiv.org. Saturation forecast: Around February 2027. 27 models tracked.

Top models

#ModelScoreOverall rank
1Kimi K360.5#54
2Claude Opus 4.8 (Max)57.6#56 (Claude Opus 4.8)
3GLM-5.2 (Max)52.7#138 (GLM-5.2)
4Qwen 3.7 Max51.1#145
5Kimi K2.7 Code50.4#185
6DeepSeek V4 Pro (Max)49.5#188 (DeepSeek V4 Pro)
7MiMo-V2.548.5#354
8DeepSeek V4 Pro (High)48.3#188 (DeepSeek V4 Pro)
9Doubao-Seed-2.1-Pro (High)48.1
10Kimi K2.647.3#178
11DeepSeek V4 Flash (High)47.1#258 (DeepSeek V4 Flash)
12GLM-5.146.7#179
13MiniMax-M346.7#269
14Qwen 3.6 Plus45.4#222
15Qwen 3.7 Plus45.3#207

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=sqbench · How It Works · Data refreshed daily, snapshot 2026-11-13.