SIS-Bench - Spatial Consistency: leaderboard

SIS-Bench (2026) tests multimodal LLMs on UAV self-awareness and spatial cognition with four-option questions about drone videos. The spatial consistency task asks what lies in a given direction from a stated position and heading in a temporally shuffled flight video; accuracy in percent, higher is better.

Metric: Accuracy (%; 195 spatial consistency questions; four-option multiple choice on UAV videos (at most 32 frames), zero-shot, open models through vLLM with at most 128 generated tokens and proprietary models through their APIs; higher is better). Source: arxiv.org. Saturation forecast: Around May 2028. 26 models tracked.

Top models

#ModelScoreOverall rank
1Kimi K2.576.4#256
2Qwen 3.5 Plus75.9#232
3Qwen 3 VL 4B Instruct73.3#1279
4Gemini 3 Flash (Preview)71.8#144
5Qwen 2.5 VL 7B Instruct67.7#1833
6InternVL3-14B67.7#1182
7Qwen 3 VL 30B A3B Instruct67.7#871
8Seed 1.866.2#242
9Qwen 3 VL 8B Instruct64.6#981
10Qwen 3 VL 8B (Thinking)64.6#887
11GPT-5.464.1#123
12GLM-4.1V-9B (Thinking)63.6#1060 (GLM-4.1V-9B)
13Qwen 2 VL 7B Instruct63.6#2553

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

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