Wan 2.2

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github.com
Wan 2.2 website first screen screenshot

Wan 2.2 is an open and advanced large-scale video model family from Wan-Video. The official repository provides inference code and model weights for T2V-A14B, I2V-A14B, TI2V-5B, S2V-14B, and Animate-14B workflows.

Added on:
2026-07-18
Best for:
Best for developers, researchers, and technical creators who need open weights and controllable self-hosted video generation.
Platform:
Web
Pricing:
Free/Freemium
video generation open video model wan 2.2 Free/Freemium

Overview

What is Wan 2.2?

Wan 2.2 is a model release rather than a single subscription editor. Its official repository documents a mixture-of-experts architecture, open weights, inference code, and multiple tasks: text-to-video, image-to-video, text-image-to-video, speech-to-video, and character animation. T2V-A14B and I2V-A14B support 480p and 720p, while TI2V-5B supports 720p at 24 FPS. Running the models requires compatible hardware and setup, so total cost depends on compute or the hosted surface you choose.

Good fit

Who should use Wan 2.2?

  • Developers and researchers who need open model weights and local inference control.
  • Technical creators building custom text-to-video or image-to-video pipelines.
  • Teams experimenting with speech-driven video, character animation, or replacement workflows.
  • Engineers comfortable with PyTorch, model downloads, GPU memory requirements, and inference setup.

Compare first

Who should compare alternatives first?

  • Compare hosted APIs first if you do not have access to a high-memory GPU or do not want to maintain model dependencies.
  • Budget compute, storage, and setup time separately from the zero license price of the published weights.
  • Choose the model variant by task: T2V-A14B, I2V-A14B, TI2V-5B, S2V-14B, and Animate-14B have different inputs and hardware profiles.
  • Check project terms and provider policies before using downloaded weights in a commercial production pipeline.

Use cases

Wan 2.2 use cases

  • Generate video from text prompts with T2V-A14B.
  • Animate reference images with I2V-A14B.
  • Use TI2V-5B for combined text/image-to-video generation at 720p.
  • Create speech-driven video with S2V-14B.
  • Animate or replace characters with Animate-14B workflows.
  • Integrate Wan2.2 into Diffusers, ComfyUI, ModelScope, or custom inference systems.

Capabilities

Wan 2.2 features

  • Mixture-of-experts architecture for the 14B video models.
  • Open weights and inference code in the official Wan-Video repository.
  • Text-to-video, image-to-video, and text-image-to-video tasks.
  • Speech-to-video and character animation/replacement variants.
  • 480p and 720p support for A14B workflows; TI2V-5B supports 720p at 24 FPS.
  • Integrations and community paths through Diffusers, ComfyUI, Hugging Face, and ModelScope.

Pricing

Wan 2.2 pricing, plans, and credits

The official Wan2.2 repository provides downloadable weights and code rather than a consumer subscription price. Self-hosted cost is determined by GPU hardware, cloud runtime, storage, and inference time. Hosted demos and APIs can add provider-specific per-second or compute charges, so verify the selected deployment surface before budgeting.

Open weightsFreeself-hosted

Downloadable model weights and inference code

Researchers and developers running local or private inference.
Hosted inferenceVariableprovider-based

Provider-specific quotas or per-second pricing

Teams that want Wan2.2 without maintaining GPUs.
Cloud GPU deploymentVariablecompute-based

GPU runtime, storage, and bandwidth

Production teams operating their own endpoint.

Free plan and limits

The repository makes model code and weights available under its published project terms. “Free” still requires compatible compute and may incur cloud GPU, storage, or hosted-inference costs.

Credits and billing checks

  • Confirm whether usage is metered by credits, minutes, generations, seats, or exports.
  • Check whether free usage renews monthly or is a one-time allowance.
  • Verify whether team seats share one usage pool or receive separate allowances.
  • Review export limits, watermark rules, commercial rights, and cancellation terms before paying.

Verification notes

What to verify before choosing it

  • Confirm current pricing and free plan limits on the official site.
  • Test the output quality against your real workflow before scaling usage.
  • Compare the tool against close competitors for pricing, features, and workflow fit.

Tradeoffs

Wan 2.2 strengths and tradeoffs

Pros

  • Open weights provide more deployment and experimentation control than closed video APIs.
  • The model family covers more than one video-generation task.
  • Official documentation includes inference commands, model links, and hardware guidance.

Cons

  • Local inference has substantial GPU, memory, storage, and setup requirements.
  • There is no single hosted subscription price for all Wan2.2 deployments.
  • Technical maintenance and provider policy review remain the buyer’s responsibility.

Recommendation

Should you use Wan 2.2?

Choose Wan 2.2 when open weights, local control, and custom pipelines justify the engineering work. If you need a turnkey browser editor, predictable per-clip billing, or managed scaling, compare hosted video platforms and confirm their Wan2.2 model version and pricing.

Questions

FAQs of Wan 2.2

What is Wan 2.2?

Wan 2.2 is an open video generation model family with text-to-video, image-to-video, text-image-to-video, speech-to-video, and character animation variants.

Is Wan 2.2 free?

The official repository publishes model weights and inference code, but running them still requires compatible GPU compute, storage, and setup. Hosted providers may charge separately.

What resolutions does Wan 2.2 support?

The official repository lists 480p and 720p support for the A14B models and 720p support for TI2V-5B at 24 FPS.

Can Wan 2.2 make video from an image?

Yes. The I2V-A14B and TI2V-5B workflows support image-based video generation.

Who should use Wan 2.2?

It is best for developers, researchers, and technical creators who can manage open-model inference and want more deployment control.