image to text benchmark
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.
Updated Aug 11, 2026
Models3
Model coverage3
MetricScore
EvidenceB
VQAv2 (val) Ranking
Higher score ranks better on this benchmark.
| 1 | GO | 71.6% | 100.0% | 3 | C | |
| 2 | GO | 71.0% | 50.0% | 3 | C | |
| 3 | GO | 62.4% | 0.0% | 3 | C |
VQAv2 (val) Score Distribution
A closer view of the leading scores on this benchmark.
VQAv2 (val) Highlights
The leading models and scores on this benchmark.
What is VQAv2 (val)?
What VQAv2 (val) measures and how its scores work.
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.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- VQAv2 (val)
- Modality
- multimodal
- Primary category
- image to text
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- vqav2-(val)|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about VQAv2 (val).
Which model scores highest on VQAv2 (val)?
Gemma 3 12B is currently ranked first with 71.6%.
What does VQAv2 (val) measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.