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multimodal benchmark

TempCompass Leaderboard

TempCompass is a comprehensive benchmark for evaluating temporal perception capabilities of Video Large Language Models (Video LLMs). It constructs conflicting videos that share identical static content but differ in specific temporal aspects to prevent models from exploiting single-frame bias. The benchmark evaluates multiple temporal aspects including action, motion, speed, temporal order, and attribute changes across diverse task formats including multi-choice QA, yes/no QA, caption matching, and caption generation.

Updated Aug 17, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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TempCompass Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelACQwen2.5 VL 72B InstructAlibaba Cloud / Qwen TeamScore74.8%Percentile100.0%Participants2EvidenceCEvaluatedAug 17, 2026
Rank02ModelACQwen2.5 VL 7B InstructAlibaba Cloud / Qwen TeamScore71.7%Percentile0.0%Participants2EvidenceCEvaluatedAug 17, 2026

TempCompass Highlights

The leading models and scores on this benchmark.

Rank #1Qwen2.5 VL 72B Instruct74.8%Rank #2Qwen2.5 VL 7B Instruct71.7%

TempCompass Score Distribution

A closer view of the leading scores on this benchmark.

TempCompass

The Top AI Models for TempCompass

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

Ranking basisThis tempcompass AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    AC
    Qwen2.5 VL 72B InstructAlibaba Cloud / Qwen Team
    Score
    74.8%

    Strengths

    • Ranks #1 of 2 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TempCompass, not total model capability
  2. 02
    AC
    Qwen2.5 VL 7B InstructAlibaba Cloud / Qwen Team
    Score
    71.7%
    Price
    $0.35 input / $1.1 output per 1M tokens

    Strengths

    • Ranks #2 of 2 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TempCompass, not total model capability

Selection summary

Best AI Models for TempCompass

Qwen2.5 VL 72B Instruct currently leads TempCompass with 74.8%. 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.

Benchmark rank #1Qwen2.5 VL 72B Instruct74.8%Benchmark rank #2Qwen2.5 VL 7B Instruct71.7% · $0.35 input / $1.1 output per 1M tokens

What is TempCompass?

What TempCompass measures and how its scores work.

TempCompass is a comprehensive benchmark for evaluating temporal perception capabilities of Video Large Language Models (Video LLMs). It constructs conflicting videos that share identical static content but differ in specific temporal aspects to prevent models from exploiting single-frame bias. The benchmark evaluates multiple temporal aspects including action, motion, speed, temporal order, and attribute changes across diverse task formats including multi-choice QA, yes/no QA, caption matching, and caption generation.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
TempCompass
Modality
multimodal
Primary category
multimodal
Score direction
higher
LLMBoard eligible
No
Evaluation key
tempcompass|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about TempCompass.

Which model scores highest on TempCompass?

Qwen2.5 VL 72B Instruct is currently ranked first with 74.8%.

What does TempCompass measure?

TempCompass is a comprehensive benchmark for evaluating temporal perception capabilities of Video Large Language Models (Video LLMs). It constructs conflicting videos that share identical static content but differ in specific temporal aspects to prevent models from exploiting single-frame bias. The benchmark evaluates multiple temporal aspects including action, motion, speed, temporal order, and attribute changes across diverse task formats including multi-choice QA, yes/no QA, caption matching, and caption generation.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

2 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.