multimodal benchmark
TempCompass is a multimodal benchmark for evaluating the temporal perception capabilities of Video Large Language Models (Video LLMs).
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score74.80% | Percentile100.00% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score71.70% | Percentile0.00% | Participants2 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading 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.
What TempCompass measures and how its scores work.
TempCompass is a benchmark that uses conflicting videos with identical static content but differing temporal aspects to evaluate Video Large Language Models (Video LLMs).
It measures temporal perception of action, motion, speed, temporal order, and attribute changes through multi-choice QA, yes/no QA, caption matching, and caption generation, using the Score metric as a ratio.
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 TempCompass.
Qwen2.5 VL 72B Instruct is currently ranked first with 74.80%.
The current leaders are Qwen2.5 VL 72B Instruct (74.80%) and Qwen2.5 VL 7B Instruct (71.70%).
Qwen2.5 VL 7B Instruct has the lowest matched official input price at $0.35 input / $1.1 output per 1M tokens.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures temporal perception of action, motion, speed, temporal order, and attribute changes through multi-choice QA, yes/no QA, caption matching, and caption generation, using the Score metric as a ratio.
Yes. Higher values rank better for this benchmark.
2 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis tempcompass 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
Qwen2.5 VL 72B Instruct currently leads TempCompass with 74.80%. 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.