TomaBench - Symptom Identification: leaderboard
TomaBench (Truong et al., 2026) asks vision-language models zero-shot multiple-choice questions about real tomato leaf images from the TomaMMU dataset. This board covers identifying the visible symptoms. Accuracy in percent; higher is better.
Metric: Accuracy (%; zero-shot multiple-choice questions on real tomato leaf images, visible symptoms; option matched against the answer key). Source: arxiv.org. Saturation forecast: Around April 2027. 9 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 2.0 Flash | 44 | #702 |
| 2 | LFM2.5-VL-1.6B | 33 | #3931 |
| 3 | Gemma 4 E4B | 25.3 | #2217 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=tomabench-symptom-identification · How It Works · Data refreshed daily, snapshot 2026-11-12.