LaViSA - Per-Sentence Accuracy: leaderboard
Metric: Per-sentence accuracy (%): share of the 700 ambiguous sentences for which the model picks the correct reading for every one of its images, averaged over the seven ambiguity categories; task: pick which disambiguated reading of a structurally ambiguous sentence a generated image depicts (2 or 3 readings per sentence, 100 sentences per category); higher is better. Source: arxiv.org. Saturation forecast: Around December 2026. 13 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Gemini 3.1 Pro (Preview) | 77.7 |
| 2 | Gemini 3.1 Flash Lite | 69 |
| 3 | GPT-5.2 | 67.1 |
| 4 | Qwen 3 VL 32B Instruct | 64.3 |
| 5 | Qwen 3 VL 32B (Thinking) | 61.1 |
| 6 | Qwen 3 VL 4B Instruct | 57 |
| 7 | Qwen 3 VL 8B (Thinking) | 50.3 |
| 8 | Qwen 3 VL 8B Instruct | 49.4 |
| 9 | Qwen 3 VL 4B (Thinking) | 44 |
| 10 | Gemma 3 27B (IT) | 43.9 |
| 11 | Gemma 3 12B (IT) | 42.3 |
| 12 | Gemma 3 4B (IT) | 9.1 |
Interactive version: theaggregate.ai/benchmark?slug=lavisa-per-sentence-accuracy · How It Works · Data refreshed daily, snapshot 2026-09-29.