multimodal benchmark
VLADBench
VLADBench is a vision-language autonomous-driving benchmark evaluating understanding of dynamic traffic scenes and participants.
Updated Aug 11, 2026
Models1
Model coverage1
MetricScore
EvidenceB
VLADBench Ranking
Higher score ranks better on this benchmark.
| 01 | AC | 77.2% | 100.0% | 1 | C |
VLADBench Highlights
The leading models and scores on this benchmark.
What is VLADBench?
What VLADBench measures and how its scores work.
VLADBench is a vision-language autonomous-driving benchmark evaluating understanding of dynamic traffic scenes and participants.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- VLADBench
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- vladbench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about VLADBench.
Which model scores highest on VLADBench?
Qwen3.7-Plus is currently ranked first with 77.2%.
What does VLADBench measure?
VLADBench is a vision-language autonomous-driving benchmark evaluating understanding of dynamic traffic scenes and participants.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.