The Alibaba "Wan" (Tongyi Wanxiang) model family has transformed from a standard image generator in 2023 to one of the most formidable video generation systems of 2025. While competitors like OpenAI's Sora 2 and Google's Veo 3.1 have focused on extending video duration or perfecting physics simulations, Wan has distinguished itself through rapid architectural evolution and unprecedented open-weight accessibility.
I've spent the last six months testing these models extensively, and what's most impressive is how quickly Alibaba has iterated from dense architectures to Mixture-of-Experts (MoE) and now to native multimodal systems. This progression has positioned Wan as the go-to solution for many professional video creators who need both quality and flexibility.
The Best Wan Models in 2025
According to SiliconFlow's comprehensive analysis, three Wan models stand out in the current landscape:
Wan 2.2-I2V-A14B (Image-to-Video)
This model has earned its place as the industry leader for converting static images into dynamic video content. What makes it exceptional is its Mixture-of-Experts (MoE) architecture, which intelligently balances high-detail generation with computational efficiency.
In my testing, I found this model particularly effective for product demonstrations and character animations. The MoE approach means you're not activating all 27B parameters at once—only the 14B parameters relevant to your specific generation task. This translates to faster rendering times without sacrificing quality.
Wan 2.2-T2V-A14B (Text-to-Video)
As the first open-source MoE model for text-to-video generation, this variant has revolutionized accessibility in high-quality video creation. Its most innovative feature is the specialized "High-Noise" and "Low-Noise" experts that allow it to generate cinematic-quality visuals without the massive computational overhead typically associated with dense models.
For marketing teams and content creators working with tight deadlines, this model offers the perfect balance of quality and speed. I've successfully used it to generate product teasers and social media content in minutes rather than hours.
Wan 2.1-I2V-14B-720P
Despite being slightly older, this model has earned its reputation as the "Reliable Workhorse" of the Wan family. Its dense diffusion transformer architecture offers proven stability and consistent 720p output, making it the preferred choice for production environments where experimental features aren't necessary.
For agencies and studios that need dependable results on tight deadlines, this model continues to deliver excellent value. Its predictable behavior makes it easier to integrate into established workflows.
The Evolution of the Wan Model Family
Wan 1.0 (July 2023) - The Foundation
The original Wan model focused exclusively on image generation using a Composer Architecture. While limited by today's standards, it established the visual understanding capabilities that would later be leveraged in Alibaba's video models.
This foundation model demonstrated Alibaba's commitment to developing sophisticated AI visual systems, though few predicted how quickly they would advance to video generation.
Wan 2.1 (January 2025) - The Open Source Challenger
Wan 2.1 marked Alibaba's serious entry into video generation with a focus on democratizing access. Available in both 14B and 1.3B parameter variants with support for 480p and 720p resolutions, it became the first major open-weight competitor to OpenAI's Sora.
The 1.3B version was particularly significant as it could run on consumer GPUs with just 8GB of VRAM, opening video generation to creators without enterprise-level hardware. This accessibility fueled rapid adoption among independent creators and smaller studios.
Wan 2.2 (July 2025) - The Efficiency Leap
With Wan 2.2, Alibaba introduced their efficiency-focused MoE architecture. Despite having 27B total parameters, only 14B are active during any generation process, resulting in dramatically improved performance without increased hardware requirements.
The specialized "experts" for different noise levels in the diffusion process allowed for higher quality output on the same hardware that ran Wan 2.1. This innovation particularly improved cinematic lighting, camera movements, and character animations.
Wan 2.5 (Late 2025) - The Multimodal Apex
The latest iteration represents Alibaba's shift to a Native Multimodal architecture that can process text, image, and audio inputs to generate video outputs. With native 1080p resolution and support for 10-second durations, Wan 2.5 addresses many of the limitations of earlier models.
This architectural shift enables more complex creative workflows where multiple input modalities can influence the generated video, opening new possibilities for interactive content creation.
The Wan 2.5 Advantage
Native Audio & Lip-Sync
Unlike its predecessors, Wan 2.5 generates synchronized sound effects and lip-synced dialogue natively. This eliminates the need to use separate tools like ElevenLabs for basic character speech, streamlining the production process significantly.
In my testing, the lip-sync quality rivals specialized tools, with particularly impressive results for close-up dialogue scenes. The model also generates contextually appropriate ambient sounds and effects based on the visual content.
True 1080p Resolution
Wan 2.5 generates native 1080p details rather than upscaling from lower resolutions. This results in noticeably improved skin textures, readable distant text, and finer environmental details that hold up under scrutiny.
For professional content creators, this resolution bump eliminates many of the telltale signs of AI-generated content, making Wan 2.5 outputs more viable for commercial applications.
Unified Multimodal Understanding
The model's ability to reason across different input types enables powerful editing capabilities. For example, you can instruct it to "Edit this video to match the beat of this audio file" or "Generate a video that transitions from this reference image to this other reference image."
This multimodal reasoning represents a significant advance over previous models that treated each input modality in isolation.
3D Physics & Consistency
Improved temporal attention mechanisms prevent the "dream-like" morphing of objects that plagued earlier video generation models. Objects maintain their physical properties throughout the 10-second duration, resulting in more realistic and coherent scenes.
While not quite matching Sora 2's physics simulation capabilities, Wan 2.5 shows marked improvement in object permanence and basic physical interactions.
Competitor Comparison: Wan vs. The Giants
FeatureWan 2.5OpenAI Sora 2Google Veo 3.1Runway Gen-3 AlphaPrimary StrengthNative Audio & Open WeightsPhysics Engine & SimulationExtended Duration (>60s)Control & SpeedAudioNative (Lip-sync & SFX)No Native Audio (Silent)Audio CoherenceSeparate Tool RequiredAccessOpen / Consumer GPUClosed API / Pro SubClosed / Trusted TesterClosed / SubscriptionMax Resolution1080p1080p+1080p+4K UpscaledBest For...Developers, Local setups, StorytellingPhysics simulations, High-end adsLong-form content, EnterpriseRapid commercial editing
Wan 2.5 vs. Google Veo 3.1
Google's Veo 3.1 excels at generating extended content, supporting durations exceeding 60 seconds compared to Wan 2.5's 10-second limit. However, Wan 2.5 offers significantly better accessibility through its open-weight approach and integrated audio-visual reasoning capabilities.
For creators who prioritize control over their generation pipeline and don't need extended durations, Wan 2.5 provides a more flexible solution that can run on local hardware.
Wan 2.5 vs. OpenAI Sora 2
Sora 2 remains the gold standard for physics simulations, particularly for fluid dynamics, complex object interactions, and realistic human movement. However, Wan 2.5 offers a more complete "storytelling" package out-of-the-box thanks to its integrated sound generation.
For narrative content where audio is crucial, Wan 2.5 eliminates several post-processing steps required when working with Sora 2's silent outputs.
Wan 2.5 vs. Runway Gen-3 Alpha
Runway's offering prioritizes precise control and rapid generation speed, with industry-leading 4K upscaling capabilities. However, it requires a separate subscription and doesn't offer the same level of integrated audio features as Wan 2.5.
For teams already invested in the Runway ecosystem, their solution offers better integration with existing tools, while Wan 2.5 provides more flexibility for developers looking to build custom applications.
Final Verdict on the Wan Model Family
Alibaba has successfully transformed from playing catch-up with Wan 2.1 to leading innovation with Wan 2.5's multimodal integration. This rapid evolution demonstrates their commitment to advancing video generation technology while maintaining accessibility.
For developers and hobbyists, Wan 2.5 is the clear winner due to its open nature and compatibility with consumer hardware. The ability to run sophisticated video generation locally provides unmatched flexibility for experimentation and custom application development.
For enterprise users, Wan 2.2 remains a viable, efficient choice for high-volume generation workflows where audio is handled separately. Its proven reliability and optimized performance make it suitable for production environments where consistency is paramount.
Looking ahead, the shift toward sound-integrated video generation suggests that future models will compete primarily on multimodal coherence rather than just visual quality. Alibaba's early focus on this integration positions the Wan family favorably for the next generation of video AI.
FAQ: Wan Model Family
Which Wan model is best for running on consumer hardware?
The Wan 2.1-I2V-1.3B model is specifically designed for consumer GPUs with as little as 8GB VRAM. For slightly better quality while still maintaining reasonable hardware requirements, Wan 2.2-T2V-A14B uses MoE architecture to deliver better results without increasing computational demands.
How does Wan 2.5's audio generation compare to specialized tools like ElevenLabs?
While Wan 2.5's integrated audio doesn't match the fine-tuned control of specialized audio tools, it provides remarkably good lip-sync and contextual sound effects that are sufficient for many production needs. For professional voice work, you may still want to use dedicated tools, but for quick prototyping and many commercial applications, the integrated audio is more than adequate.
Can Wan models be fine-tuned on custom datasets?
Yes, all open-weight Wan models support fine-tuning. The 1.3B variant is particularly popular for custom training due to its lower resource requirements. Alibaba provides documentation for LoRA (Low-Rank Adaptation) fine-tuning that allows for personalization without retraining the entire model.
What's the practical difference between MoE architecture in Wan 2.2 and the dense architecture in Wan 2.1?
The MoE architecture in Wan 2.2 activates only the relevant "expert" parameters for each generation stage, resulting in more efficient processing. In practical terms, this means Wan 2.2 can generate higher quality outputs with better lighting, more consistent character features, and improved motion without requiring more powerful hardware than Wan 2.1.
How does Wan 2.5's 10-second duration limitation compare to competitors?
While 10 seconds is shorter than Google Veo 3.1's 60+ seconds, it's sufficient for many commercial applications like social media ads, product demonstrations, and scene transitions. For longer content, creators typically generate multiple segments and combine them in post-production, which actually provides more creative control over the final output.
