Wan (Tongyi Wanxiang)

Wan 2.1 is Alibaba's open-source text-to-video model, released in February 2025 under Apache 2.0. Available in 14B and 1.3B parameter versions, it competes directly with Tencent's Hunyuan Video and closed models like Sora on generation quality. Free to self-host, accessible via Alibaba Cloud API for managed inference.

In February 2025, Alibaba released Wan 2.1 as open-source weights under an Apache 2.0 license. The timing was deliberate, three months after Tencent dropped Hunyuan Video weights and set the benchmark for what open-source video generation could look like. Two major Chinese technology companies releasing competitive video generation models with commercial-use open weights within a few months of each other marked a genuine shift in the open-source AI video landscape.

Wan 2.1 competes with Hunyuan Video on raw generation quality at the 14B parameter scale. But it has something Hunyuan doesn't: a 1.3B parameter model that runs on consumer GPU hardware. That detail changes who can use it.

Quick verdict

Wan 2.1 is one of two serious options for open-weights text-to-video generation at production quality, the other being Hunyuan Video. For teams evaluating which open model to build on, the 14B versions are closely matched in generation quality, and the decision comes down to community maturity (Hunyuan), hardware accessibility (Wan's 1.3B), and your existing cloud infrastructure preferences. If you're on Alibaba Cloud already, the managed inference integration is straightforward. If you want to experiment with video generation on a consumer GPU without cloud costs, Wan 2.1's 1.3B model is uniquely accessible. For a polished consumer video generation product, Kling and Hailuo AI are more practical choices.

What Alibaba built and why

Alibaba's AI research effort spans multiple model families under the Tongyi umbrella, Qwen for language, Tongyi Wanxiang for visual generation, and the broader suite of services running on Alibaba Cloud. Wan 2.1 is the video generation component of that stack.

The release strategy mirrors Tencent's approach with Hunyuan Video: open weights under a permissive license to drive community adoption, with managed cloud inference as the commercial path. Both companies are betting that giving away model weights builds a developer ecosystem and positions their cloud platforms as the natural home for production inference.

What distinguishes Wan's approach is the dual model strategy. Releasing a 1.3B parameter version alongside the 14B model acknowledges that open-source adoption depends on accessibility, not just peak quality. A developer with a gaming laptop can experiment with Wan 2.1's 1.3B model. They need data center hardware to run Hunyuan Video's base model. That accessibility difference matters for community growth.

Generation quality: the 14B model

The 14B Wan 2.1 model produces video generation quality that benchmarks competitively against Hunyuan Video and holds up against closed alternatives like Kling on many prompt types.

Human motion is strong. Walking, running, gesture, and simple action sequences generate with believable weight and movement quality. This is the dimension where previous open-source video models struggled most, and both Wan 2.1 and Hunyuan Video have closed that gap with the closed field.

Image-to-video performance is where Wan 2.1 has attracted particular attention. Starting from a still image and generating a coherent short video that maintains the content and style of the input while adding natural motion is a distinct challenge from pure text-to-video. Wan 2.1's image-to-video output is strong enough that it's used as a primary benchmark comparison point for open models in this capability.

Scene consistency within a clip is solid. Objects and environments hold together across frames without the kind of visual drift that was common in earlier open-source video models. Complex backgrounds with multiple distinct elements are handled better than many closed models at the same generation length.

The 14B model's main limitation is the same as Hunyuan Video's: it needs substantial GPU hardware to run at practical speeds. For teams evaluating open models on cloud GPU rentals, the per-hour cost structure means you'll want to benchmark inference time carefully before committing to a generation pipeline.

The 1.3B model: consumer hardware video generation

The 1.3B parameter version is the part of Wan 2.1 that doesn't have a direct equivalent in the competitive field. At that parameter scale, the model runs on consumer GPUs, an RTX 3080 with 12GB VRAM can generate video clips at practical speeds without quantization compromises.

The trade-off is quality. The 1.3B model produces noticeably lower generation quality than the 14B. Motion is less fluid, scene complexity is more limited, and fine detail in human faces and hands is less accurate. You're getting video generation on consumer hardware, not the best video generation available.

For what it's designed for, local experimentation, rapid prototyping, use cases where generation cost and hardware accessibility matter more than maximum quality, the 1.3B model is genuinely useful. A developer building a video generation feature who wants to test their pipeline locally before deploying cloud inference doesn't need 14B-quality output for that testing. A creator exploring video generation concepts on their existing hardware before investing in cloud credits has a real path with the 1.3B model.

No other model in the current open-source video generation field offers this kind of tiered access. Hunyuan Video is a single 13B model. Genmo Mochi at 10B has no lightweight variant. Wan 2.1's dual model strategy is a genuine differentiator.

Self-hosting setup

The GitHub repository at Wan-Video/Wan2.1 contains setup instructions, inference scripts, and links to model weights on Hugging Face. Installation follows the same general pattern as other large video generation models: clone the repo, install Python dependencies, download the model weights (approximately 28GB for the 14B model, roughly 2.5GB for 1.3B), and configure the inference environment.

For the 14B model on cloud GPU hardware, RunPod, Lambda Labs, and vast.ai all offer hardware at the required VRAM spec. Community deployment templates exist on RunPod for Wan 2.1 that reduce setup time significantly compared to manual configuration.

For the 1.3B model on consumer hardware, the setup is lighter and the model downloads quickly. The inference scripts in the official repository include example generation commands that work on consumer hardware without modification.

One practical note: Wan 2.1 is younger than Hunyuan Video, and the community documentation and third-party tooling around it is less developed as of mid-2026. If you encounter setup issues, the community resources around Hunyuan Video are currently more extensive, though Wan's community is growing.

Alibaba Cloud managed inference

For teams that want Wan 2.1's generation quality without the self-hosting overhead, Alibaba Cloud offers the model through its Tongyi Wanxiang API. The service is usage-priced, based on generation length and resolution.

The friction point for international users is documentation. Alibaba Cloud's primary audience is Chinese-market businesses, and the English documentation quality for the video API is inconsistent. The API works, and the underlying model quality is the same as the self-hosted version, but expect more setup effort than you'd face with a Western API like Runway's.

Teams already operating on Alibaba Cloud infrastructure will find the integration more natural, the authentication and billing work within existing Alibaba Cloud account structures. Teams without Alibaba Cloud presence who want managed inference might find Tencent Cloud's Hunyuan Video API similarly foreign but with better English docs, or might prefer a Western API provider that offers Wan 2.1 inference.

Wan 2.1 vs the alternatives

Wan vs Hunyuan Video. Hunyuan Video and Wan 2.1 14B are closely matched in generation quality and share similar licensing and deployment characteristics. Hunyuan has a larger community and more third-party integrations as of mid-2026. Wan 2.1 has the 1.3B lightweight variant and stronger image-to-video benchmarks. If you're choosing between the two for a production pipeline, the practical factors, which has better community support for your specific use case, which cloud platform you're already using, will drive the decision more than raw quality differences.

Wan vs Kling. Kling from Kuaishou is a closed product with a polished interface, credit-based pricing, and an API. For teams that want a ready-to-use video generation product without infrastructure work, Kling is more practical. Wan 2.1 is the choice when you need open weights, self-hosting, or commercial use without per-generation licensing.

Wan vs Sora. Sora is a closed model bundled with ChatGPT subscriptions. No weights, no API, no self-hosting. Wan 2.1 is the opposite on every dimension. For any use case that requires control over the model, Wan 2.1 or Hunyuan Video are the appropriate choices.

Wan vs Hailuo AI. Hailuo AI from MiniMax is a closed product with a consumer web interface. Strong generation quality, no open weights. Similar trade-off to Kling, polished product experience vs. open-model control.

Wan vs Genmo Mochi. Genmo Mochi is a Western open-source video generation model at 10B parameters under Apache 2.0. Wan 2.1 14B produces higher quality output in head-to-head comparisons as of mid-2026. Genmo Mochi has a Western community and English-first documentation. The choice between them depends partly on which community ecosystem you prefer to work within.

Who should use Wan 2.1

Developers building video generation pipelines who want an open alternative to Hunyuan Video. Wan 2.1 is the second strong open-weights option in the category. If you've evaluated Hunyuan Video and found reasons to prefer an alternative, or want to compare both before committing, Wan 2.1 is the appropriate comparison point.

Creators with consumer GPU hardware who want to experiment with local video generation. The 1.3B model is unique in the field for making this practical without enterprise hardware. The quality won't match the 14B model or closed commercial tools, but for learning, prototyping, and use cases where hardware accessibility matters, it's a real option.

Studios on Alibaba Cloud infrastructure. The managed inference API integrates naturally for teams already operating in the Alibaba Cloud ecosystem. For video generation workloads that fit within existing Alibaba Cloud contracts and infrastructure, the Wan 2.1 API avoids the need to add a new vendor relationship.

Research teams studying video generation. Open weights let you run controlled experiments, analyze model behavior, and publish findings in ways a closed API doesn't permit. The Apache 2.0 license removes any research use restrictions.

Wan 2.1 is not the right choice for teams that want a polished consumer product with a clean interface, Kling and Hailuo AI serve that need. It's also not the right choice for anyone who needs more than short clip generation without significant pipeline engineering.

Getting started

The GitHub repository at Wan-Video/Wan2.1 is the starting point for self-hosting. Model weights are available from the repository's linked Hugging Face pages. For the 1.3B model on consumer hardware, the setup is straightforward following the official inference script examples.

For Alibaba Cloud managed inference, start at the Tongyi Wanxiang section of the Alibaba Cloud console. The API endpoint documentation is available in the Alibaba Cloud developer portal, with more complete coverage in the Chinese-language version.

For third-party cloud inference without Alibaba Cloud, check platforms like Replicate and ComfyUI API services that have added Wan 2.1 model support. These typically offer more straightforward English-language setup than the official Alibaba Cloud path.

The model is recent enough that the community tooling continues to expand rapidly. Fine-tuned versions, optimization scripts, and integration guides on Hugging Face and GitHub are worth checking for updates before beginning a production integration, the ecosystem around Wan 2.1 in mid-2026 is meaningfully different from what it was at launch.

Key features

• Text-to-video generation with 14B and 1.3B parameter model variants
• Image-to-video animation from still images
• Apache 2.0 open-source license for commercial use
• Self-hostable on compatible GPU hardware
• Alibaba Cloud API for managed inference
• Multiple aspect ratio support
• Strong motion quality for human subjects
• Active community fine-tuning ecosystem

Pros and cons

Who is Wan (Tongyi Wanxiang) for?

• Developers building video generation pipelines who need a competitive open model alternative to Hunyuan Video
• Research teams studying video generation without API restrictions
• Studios with on-premises GPU infrastructure that need commercial-use open weights
• Creators using the 1.3B model for local generation on consumer hardware

Alternatives to Wan (Tongyi Wanxiang)

If Wan (Tongyi Wanxiang) isn't quite the right fit, the closest alternatives are hunyuan , kling , hailuo-ai , and sora . See our full Wan (Tongyi Wanxiang) alternatives page for side-by-side comparisons.