image to text benchmark
VQAv2
VQAv2 is a balanced Visual Question Answering dataset that addresses language bias by providing complementary images for each question, forcing models to rely on visual understanding rather than language priors. It contains approximately twice the number of image-question pairs compared to the original VQA dataset.
Updated Aug 11, 2026
Models3
Model coverage3
MetricScore
EvidenceB
VQAv2 Ranking
Higher score ranks better on this benchmark.
| 01 | MA | 80.9% | 100.0% | 3 | C | |
| 02 | MA | 78.6% | 50.0% | 3 | C | |
| 03 | ME | 78.1% | 0.0% | 3 | C |
VQAv2 Score Distribution
A closer view of the leading scores on this benchmark.
VQAv2 Highlights
The leading models and scores on this benchmark.
What is VQAv2?
What VQAv2 measures and how its scores work.
VQAv2 is a balanced Visual Question Answering dataset that addresses language bias by providing complementary images for each question, forcing models to rely on visual understanding rather than language priors. It contains approximately twice the number of image-question pairs compared to the original VQA dataset.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- VQAv2
- Modality
- multimodal
- Primary category
- image to text
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- vqav2|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about VQAv2.
Which model scores highest on VQAv2?
Pixtral Large is currently ranked first with 80.9%.
What does VQAv2 measure?
VQAv2 is a balanced Visual Question Answering dataset that addresses language bias by providing complementary images for each question, forcing models to rely on visual understanding rather than language priors. It contains approximately twice the number of image-question pairs compared to the original VQA dataset.
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.