RailVQA (Dynamic Multi-Frame): leaderboard
Metric: Choice-question accuracy (%) on RailVQA-bench dynamic multi-frame cab-view video clips (one multiple-choice question per clip), where the model answers alone from the cab-view input (no detector or framework), in the benchmark's structured chain-of-thought format; higher is better. Source: arxiv.org. Saturation forecast: Around January 2027. 2 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen 3 VL 8B Instruct | 84.36 | #401 |
No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.
Interactive version: theaggregate.ai/benchmark?slug=railvqa-dynamic-multi-frame · How It Works · Data refreshed daily, snapshot 2026-10-11.