LongCat-Video and Wan 2.2: Two Open-Weight Video Generation Models Compared

A source-based comparison of LongCat-Video (13.6B DiT, 720p/30fps, up to 5-minute generations) and Wan 2.2 (Alibaba's open-weight MoE video model, 27B total/14B active, plus 5B consumer-GPU model). Both open licenses; covers architecture, duration, resolution, deployment, and licensing.

Independent third-party resource. Not affiliated with or endorsed by LongCat, Meituan, DeepSeek, or any other publisher discussed on this page.

Published: 2026-08-24 · Author: LongCat Community Hub editorial team

This is an independent, source-based comparison of LongCat-Video (Meituan) and Wan 2.2 (Alibaba) — an open-weight versus open-weight comparison, the video-line companion to the LongCat-Image vs Qwen-Image 2.0 comparison on this site. It compares documented architecture, duration and resolution limits, deployment requirements, and licensing. It does not report performance as a verdict — for publisher-reported scores, see the individual model pages and benchmark index linked below. Every claim on this page is sourced from the linked publisher documentation or attributed third-party coverage.

DimensionLongCat-VideoWan 2.2
DeveloperMeituan (LongCat team)Alibaba (Tongyi / Wan team)
Release dateOctober 2025July 2025 (2.2 open release)
Parameters13.6B (dense DiT)27B total / ~14B active (MoE); plus 5B unified model
ArchitectureDiT + Block Sparse Attention, documented (arXiv:2510.22200)MoE video diffusion (high-noise + low-noise experts), documented
Generation modesText-to-video, image-to-video, video continuationT2V-A14B, I2V-A14B, unified TI2V-5B (T2V + I2V in one)
Max durationUp to 5 minutes (~9,000 frames)Documented clips on the order of 5 seconds (open weights)
Resolution / FPS720p at 30fps720p at 24fps (5B model); 480p/720p (14B models)
Consumer-GPU pathServer-grade GPU recommended (13.6B)Yes — TI2V-5B on ≥24GB consumer GPU (~22GB VRAM, 5s clip in under 9 min)
Audio generationNot documented in the video modelNot in the open 2.2 weights (audio arrives in hosted 2.5+/3.0 lines)
LicenseOpen source (weights + inference code public)Apache 2.0 (open weights, commercial use permitted)
DeploymentSelf-hosted; GitHub/HuggingFace/ModelScopeSelf-hosted (GitHub/HuggingFace/ModelScope); Alibaba Cloud API for newer lines

Two Open-Weight Video Models, Two Architectures

Wan 2.2, released open-source by Alibaba's Tongyi lab in July 2025, is notable for being the first video-generation model family to use a Mixture-of-Experts diffusion architecture: the 14B-class models (T2V-A14B, I2V-A14B) route denoising between a high-noise expert for composition and a low-noise expert for detail, totaling ~27B parameters while activating roughly 14B per step. A unified TI2V-5B model pairs a dense 5B backbone with a high-compression video VAE, enabling 720p/24fps generation on a single consumer GPU.

LongCat-Video, released October 2025, is a 13.6B dense Diffusion Transformer with Block Sparse Attention, documented in its technical report, with weights and inference code public. Its headline difference is duration: temporal consistency across scenes up to 5 minutes (~9,000 frames), which is a different capability class from Wan 2.2's documented ~5-second open clips. Both are Chinese-developed open-weight models, making this a structural comparison of two open approaches rather than an open-vs-closed contrast.

Deployment: Consumer GPU vs Long-Form

Wan 2.2's 5B model is the more accessible deployment story: official guidance puts it at roughly 22–24GB VRAM on a single consumer GPU, generating a 5-second 720p clip in under nine minutes without specialized optimization. The 14B MoE models are heavier (officially ~80GB VRAM in the reference implementation), and quantization variants lower that floor.

LongCat-Video's 13.6B model documents 720p/30fps generation but recommends server-grade GPU hardware. Its differentiator is not hardware efficiency but generation span: 5-minute continuous scenes versus Wan's short clips. Teams choose between a consumer-GPU short-clip tool and a long-form model that needs a bigger machine — different production profiles rather than a simple quality ranking.

The Wan Version Landscape

Wan 2.2 is the latest official open-weight release in the Wan family, but Alibaba's hosted lines have advanced further: 2.5/2.6/2.7 are hosted API generations with native audio and longer durations, and Wan 3.0 is the hosted flagship (up to 30 seconds, native audio, 20 multimodal references). Teams comparing against LongCat-Video should be careful about which "Wan" they mean: the open 2.2 weights compared here are older and lack audio, while the hosted lines are a different (closed) product class.

This matters for fair comparison: LongCat-Video's open weights are its shipping product, while Wan's most advanced capabilities sit behind Alibaba's API. The open-vs-open comparison on this page holds for the downloadable models; teams wanting Wan's audio and longer durations would be evaluating a hosted service, which is a different decision.

This comparison is based on publicly available documentation accessed on 2026-08-24. LongCat-Video specifications are from its technical report and GitHub repository. Wan 2.2 specifications are from the open-release coverage and attributed technical guides.

This is a feature-level comparison, not a performance evaluation. This site has not independently tested either model. Capability claims are publisher-reported or independently attributed as noted and have not been independently verified by this site.

Wan versions differ significantly between open weights and hosted lines; the comparison on this page covers the open 2.2 release only. Verify current terms, licenses, and availability on each vendor's official documentation before deployment.

Related pages

Related comparisons in this series

  • LongCat-Video and Seedance 2.5: Open-Weight vs Closed Video Generation Compared

    A source-based comparison of LongCat-Video (13.6B open-weight DiT, 720p/30fps, text/image-to-video, up to 5-minute generations, self-hostable) and Seedance 2.5 (ByteDance's closed video flagship, 30s native output, 50 multimodal references, built-in audio). Covers architecture, duration, resolution, pricing, licensing, and deployment.

  • LongCat-Video and Seedance 2.0: Open-Weight vs Closed Video Generation Compared

    A source-based comparison of LongCat-Video (13.6B open-weight DiT, 720p/30fps, up to 5-minute generations, self-hostable) and Seedance 2.0 (ByteDance's closed video model, 4-15s clips, native audio, 480p-1080p). Covers architecture, duration, audio, pricing, licensing, and deployment — the predecessor of Seedance 2.5.

Sources

  • LongCat-Video Technical Report (arXiv:2510.22200)

    Primary sourcePublished 2025-10-24Accessed 2026-08-24

    Technical report describing the 13.6B DiT architecture, coarse-to-fine generation, Block Sparse Attention, and multi-reward RLHF training.

  • LongCat-Video GitHub Repository

    Primary sourceAccessed 2026-08-24

    Model weights, inference code, and license for LongCat-Video; supports T2V, I2V, and video continuation.

  • Wan 2.2 open-source release (AIBase)

    Third-partyPublished 2025-07-29Accessed 2026-08-24

    Coverage of the Wan 2.2 open release: first MoE video diffusion models (T2V-A14B, I2V-A14B at 27B total/14B active), unified TI2V-5B consumer-GPU model (22GB VRAM, 720p 24fps), cinematic aesthetics control.

  • Wan 2.2 GPU requirements (Hivenet)

    Third-partyAccessed 2026-08-24

    Technical guide: Apache 2.0 license, 14B MoE models need ~80GB VRAM officially, TI2V-5B runs on ≥24GB consumer GPU (5s 720p in under 9 min), expanded family (S2V-14B, Animate-14B).

  • Wan model family overview (wan2-7.io)

    Third-partyAccessed 2026-08-24

    Clarifies Wan version landscape: Wan 2.2 is the latest official open-weight release; 2.5/2.6/2.7 are hosted API lines; 3.0 is the hosted flagship with native audio.

Independent third-party disclosure

This page is published by an independent third-party site. It is not affiliated with, endorsed by, sponsored by, or operated by Meituan, LongCat, or any of their affiliates. The content summarizes publicly-available primary documentation and does not represent the views of any referenced organization.

Last reviewed: 2026-08-24