video benchmark
ActivityNet is a video benchmark for human activity understanding with 203 activity classes, 849 video hours, and untrimmed videos containing activity instances.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score61.90% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis activitynet AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
GPT-4o currently leads ActivityNet with 61.90%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What ActivityNet measures and how its scores work.
ActivityNet is a large-scale video benchmark covering 203 activity classes and providing samples from untrimmed videos.
It supports comparison of algorithms for untrimmed video classification, trimmed activity classification, and activity detection.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about ActivityNet.
GPT-4o is currently ranked first with 61.90%.
The current leaders are GPT-4o (61.90%).
GPT-4o has the lowest matched official input price at $2.5 input / $10 output per 1M tokens.
The fastest matched records are GPT-4o (132.00 tok/s via OpenAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It supports comparison of algorithms for untrimmed video classification, trimmed activity classification, and activity detection.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.