SIS-Bench - Spatio-Temporal Consistency: leaderboard

SIS-Bench (2026) tests multimodal LLMs on UAV self-awareness and spatial cognition with four-option questions about drone videos. The spatio-temporal consistency task asks for the landmark sequence of a flight reconstructed from a temporally shuffled video; accuracy in percent, higher is better.

Metric: Accuracy (%; 241 spatio-temporal 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 November 2028. 26 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3 Flash (Preview)57.7#144
2GPT-5.457.3#123
3Seed 1.857.3#242
4Qwen 3 VL 8B (Thinking)55.6#887
5Qwen 3.5 Plus54.8#232
6Kimi K2.553.1#256
7Qwen 3 VL 8B Instruct51#981
8GLM-4.1V-9B (Thinking)49.8#1060 (GLM-4.1V-9B)
9Qwen 3 VL 30B A3B Instruct47.7#871
10Qwen 3 VL 4B Instruct47.3#1279
11Qwen 2 VL 7B Instruct47.3#2553
12InternVL3-14B46.1#1182
13Qwen 2.5 VL 7B Instruct41.5#1833

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-spatio-temporal-consistency · How It Works · Data refreshed daily, snapshot 2026-11-13.