SiT-Bench - Logic & Anomaly Detection: leaderboard

SiT-Bench category (Beijing Institute of Technology, 2026): consistency of the internal world model, with object permanence across disjoint viewpoints and cardinal direction judgement (450 items). Item-weighted accuracy in percent, higher is better; scenes are given only as coordinate-aware text descriptions, never as images.

Metric: Accuracy (%; item-weighted over the category's subtasks). Source: arxiv.org. Saturation forecast: Around November 2027. 31 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3 Flash (Preview)59.11#141
2GPT-4o45.33#741
3Qwen 3 VL 8B Instruct41.33#982
4Qwen 3 VL 32B Instruct40.22#582
5InternVL3-14B36.89#1184
6Qwen 3 VL 4B Instruct35.56#1281
7Qwen 2.5 VL 72B Instruct34.89#845
8Qwen 2.5 72B Instruct33.33#1093
9Qwen 3 4B 2507 Instruct33.11#2174
10Qwen 2.5 7B Instruct31.33#2583
11InternVL3-8B30.22#1695
12Qwen 3 8B (Non-reasoning)30#1838 (Qwen 3 8B)
13Qwen 3 30B A3B 2507 Instruct29.11#1078
14Qwen 2.5 3B Instruct27.11#5206
15Qwen 3 4B (Non-reasoning)26.67#2297 (Qwen 3 4B)

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

Interactive version: theaggregate.ai/benchmark?slug=sit-bench-logic-anomaly-detection · How It Works · Data refreshed daily, snapshot 2026-11-12.