Independent Benchmarks for AI Video Generation Models

Objective analysis and head-to-head comparisons of top video synthesis engines, helping creators find the right tools for realistic, anime, and motion graphics workflows.

Overview

Global AI Video Model Leaderboard

Leaderboard

Models ranked by composite score (out of 100).

Image to Video (I2V) Ranking

Timeline

Evolutionary History of Video Generation Model

Explore the development and evolution of AI video generation models over time.

AI Video Model User Base Evolution

Version nodes show cumulative user totals at each release time. Interpolation is used where actual values are unavailable.

Wan (Alibaba)
Sora (OpenAI)
Veo (Google)
Kling (Kuaishou)
PixVerse
Hailuo (MiniMax)

Note: Some user figures are best-effort estimates based on multi-source inference (official announcements, industry analysis, app store data). Estimated values are marked with "*" notation. This data is for reference only and is not intended to serve as investment advice.

Dimensional Leaderboards

Performance Rankings by Technical Metric

Detailed performance analysis across multiple technical dimensions.

Optical Flow

1
0.900
2
0.880
3
0.880
4
0.875
5
0.875

Multi-Object

1
0.862
2
0.860
3
0.855
4
0.850
5
0.835

PSNR

1
33.5 dB
2
33.0 dB
3
32.0 dB
4
32.0 dB
5
31.8 dB

SSIM

1
0.900
2
0.890
3
0.890
4
0.880
5
0.875
FAQs

Frequently Asked Questions About Video Models

There is no single "best" model. Rankings vary by use case. Currently, models like Veo and Kling often lead in photorealism, while others may excel in anime styles. Check our Overview for category-specific leaders.

Many platforms, including Hailuo and certain versions of Pixverse, offer daily free credits or beta access. Our individual model review pages detail the current pricing and trial availability for every listed brand.

Commercial rights depend on the specific platform's terms of service and your subscription tier. We highlight the licensing status (Commercial/Non-Commercial) clearly in the details section of each model we track.

We use a hybrid system combining blind user voting in our Arena with distinct technical evaluations for prompt adherence, physics consistency, and artifact rates conducted by our internal expert team.

Text-to-Video creates content from scratch using a written prompt. Image-to-Video animates an existing static reference image, providing greater control over character appearance and composition.

This is a consistency issue often caused by the model failing to maintain object permanence over time. Our "Temporal Coherence" metric specifically measures a model's ability to reduce these unwanted morphing artifacts.

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