VQAv2 (val)
VQAv2 is a balanced Visual Question Answering dataset containing open-ended questions about images that require understanding of vision, language, and commonsense knowledge to answer. VQAv2 addresses bias issues from the original VQA dataset by collecting complementary images such that every question is associated with similar images that result in different answers, forcing models to actually understand visual content rather than relying on language priors.
Gemma 3 12B from Google currently leads the VQAv2 (val) leaderboard with a score of 0.716 across 3 evaluated AI models.
Gemma 3 12B leads with 71.6%, followed by Gemma 3 27B at 71.0% and Gemma 3 4B at 62.4%.
Progress Over Time
Interactive timeline showing model performance evolution on VQAv2 (val)
VQAv2 (val) Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Google | 12B | 131K | $0.05 / $0.10 | ||
| 2 | Google | 27B | 131K | $0.10 / $0.20 | ||
| 3 | Google | 4B | 131K | $0.02 / $0.04 |
FAQ
Common questions about VQAv2 (val).
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