multimodal benchmark
QVHighlights
QVHighlights is a video moment retrieval benchmark for detecting moments and highlights in videos via natural language queries. Given a query, the model must localize the start and end times of relevant moments in the video, evaluated using metrics such as Recall@1 at a 0.5 IoU threshold.
Updated Aug 11, 2026
Models3
Model coverage3
MetricScore
EvidenceB
QVHighlights Ranking
Higher score ranks better on this benchmark.
| 1 | AM Nova 2 LiteAmazon | 77.2% | 100.0% | 3 | C | |
| 2 | AMNova 2 OmniAmazon | 76.7% | 50.0% | 3 | C | |
| 3 | AMNova 2 ProAmazon | 76.7% | 0.0% | 3 | C |
QVHighlights Score Distribution
A closer view of the leading scores on this benchmark.
QVHighlights Highlights
The leading models and scores on this benchmark.
What is QVHighlights?
What QVHighlights measures and how its scores work.
QVHighlights is a video moment retrieval benchmark for detecting moments and highlights in videos via natural language queries. Given a query, the model must localize the start and end times of relevant moments in the video, evaluated using metrics such as Recall@1 at a 0.5 IoU threshold.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- QVHighlights
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- qvhighlights|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about QVHighlights.
Which model scores highest on QVHighlights?
Nova 2 Lite is currently ranked first with 77.2%.
What does QVHighlights measure?
QVHighlights is a video moment retrieval benchmark for detecting moments and highlights in videos via natural language queries. Given a query, the model must localize the start and end times of relevant moments in the video, evaluated using metrics such as Recall@1 at a 0.5 IoU threshold.
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.