Wan 3.0 Features — Everything the Model Can Do

Native 4K Video Output at 60fps — No Upscaling Required

Most AI video models generate at a lower resolution and then run the output through a separate upscaling pass before you see the final result. This introduces artifacts — particularly around fine textures, hair, and edge detail — that are easy to spot on a large display. Wan 3.0 generates video natively at 3840×2160 (4K UHD), meaning the detail level in the output comes directly from the model itself, not from an interpolation step applied afterward.

The 60fps support changes how motion reads in the final video. At 24fps, fast-moving subjects can look choppy or motion-smeared, especially in high-motion scenes like sports, action sequences, or mechanical close-ups. At 60fps, those same subjects appear fluid and sharp throughout. For creators producing content that will be viewed on modern displays — or embedded on product pages where perceived quality directly affects engagement — the difference is immediately visible.

Supported output configurations:

• 4K (3840×2160) at 24fps, 30fps, or 60fps
• 1080p (1920×1080) at 24fps, 30fps, or 60fps (available on all plans including Free)
• Aspect ratios: 16:9 (landscape), 9:16 (vertical/mobile), 1:1 (square)
• Export format: MP4 container with H.264 or H.265 codec options

H.265 encoding delivers 4K files at file sizes suitable for direct upload to YouTube, Instagram, and ad platforms without additional compression passes. H.264 offers maximum compatibility with older playback environments.

Try 4K generation with the Wan 3.0 AI Video Generator

Neural Physics Engine — Objects That Move Like They Should

The most common tell in AI-generated video isn't the overall visual style — it's the behavior of objects within the scene. Liquid that pools oddly. Cloth that stays impossibly rigid. Hair that moves like a single solid mass. These are symptoms of physics being approximated by pattern-matching rather than understood as a real system.

Wan 3.0 includes a neural physics engine: a component of the model specifically trained on the physical behavior of objects in three-dimensional space. This isn't a rigid simulation layer bolted on top of the generation — it's integrated into how the model produces each frame of the output.

In practice, this means:

• Liquids pour with realistic surface tension, splash naturally on impact, and settle with the viscosity you'd expect
• Cloth drapes and folds based on gravity and the movement of whatever is underneath it
• Hair responds to air currents, subject motion, and contact with other surfaces
• Rigid objects collide, bounce, and come to rest with physically plausible trajectories and deformation
• Smoke, steam, and particles disperse based on air movement and heat visible in the scene

The physics engine is particularly valuable for product video, food and beverage content, and any scenario where viewers have strong subconscious expectations about how things move. A pour shot that looks physically wrong is immediately noticed and breaks trust. One that looks right becomes invisible — and that invisibility is the goal.

Scenarios where the physics engine performs best: coffee pours, perfume sprays, fabric drapes, crashing waves, candle flames, falling leaves, and product collisions.

See the physics engine in action

Multi-Shot Consistency — Same Character, Every Cut, Up to 60 Seconds

Character drift is the single most frustrating limitation of AI video generation for narrative work. You generate a 10-second clip featuring a character in a red jacket. You generate the next shot — and suddenly the jacket is blue, the face is subtly different, and the room has changed. This happens because most video models treat each generation as a fresh inference with no persistent memory of the character or scene established in previous clips.

Wan 3.0 addresses this directly through a structural identity preservation mechanism built into the generation process. When you generate a multi-shot sequence, the model actively tracks and maintains key visual attributes — facial structure, clothing details, hair color and style, and environment — across the full sequence. You can define a character or scene once, and the model carries that identity through wide shots, medium shots, and close-ups without drift.

What this enables in practice:

• Narrative short films with a consistent cast, generated entirely within Wan 3.0
• Product demonstration sequences showing the same product in multiple settings
• Training videos and explainers featuring a recurring presenter
• Advertising campaigns where visual consistency across multiple clips is a requirement
• Social media series where character or brand identity must be maintained across episodes

Current limits: The 60-second ceiling is a hard limit on this version. Within that window, identity consistency is maintained at a structural level — face shape, clothing, hair color, environment palette. Micro-details like exact hair strand placement can still vary between shots, consistent with how real-world cinematography works.

Explore all Wan 3.0 features in the generator

Synchronized Audio Generation — Sound That Matches What You See

Audio in Wan 3.0 is generated alongside the video, conditioned on what's happening visually in each frame. The model was trained on paired audio-visual data, giving it an understanding of the acoustic relationship between visual events and the sounds they produce.

When audio generation is enabled, the output includes:

• Ambient environment audio — the background sound of the visible space (indoor vs. outdoor, crowd density, weather conditions)
• Event-triggered sounds — impact audio, surface interaction sounds, and movement audio synchronized to the corresponding visual events
• Atmospheric texture — subtle sonic details that make a scene feel inhabited: wind, distant traffic, room tone, natural resonance

In most cases, no additional prompting is needed for audio — the model infers appropriate sound from the visual content. For more specific results (e.g., "no background music, rain only" or "mechanical ambience, no voices"), audio descriptors can be added directly to the text prompt.

Audio output specs:

• Format: AAC, 48kHz stereo, embedded in the MP4 container
• Audio-only export is not currently supported
• Audio generation is available on Pro and API plans; Free plan generates silent video

Try audio generation

Three Ways to Start — Text, Image, or Video

Wan 3.0 supports three distinct input modes, each suited to a different type of creative starting point.

Text-to-Video
Write a description of the scene you want, and Wan 3.0 generates the video. The model accepts natural language — no special syntax required. Describe camera movement, lighting, subject behavior, and atmosphere for best results. Text-to-video is the default mode and works well for original content creation where you're starting from a blank canvas.

Image-to-Video
Upload a reference image and the model animates it into video. The generated output maintains the visual identity of your reference — color palette, subject appearance, composition — while adding natural motion. Effective for product animation, character work from illustrated references, and brand-consistent content creation.

Video Extension
Upload an existing video clip and Wan 3.0 generates additional frames that flow naturally from where your clip ends. The model analyzes the motion patterns, style, and content of your input and continues the sequence. Useful for extending short captures, adding B-roll that matches existing footage, or building longer sequences iteratively.

All three modes can be combined. Mixed inputs (image + text, video + text) are supported in a single generation request.

Open-Source Weights & Developer API — Build What You Need

The Wan 3.0 model weights are publicly available on Hugging Face and GitHub under a license that permits commercial use with attribution. You can download the full model, run it on your own GPU infrastructure, fine-tune it on custom datasets, and integrate it into your own products.

For teams that don't want to manage infrastructure, the Wan 3.0 REST API provides programmatic access to the same model running on managed cloud hardware.

API capabilities:

• Text-to-video, image-to-video, and video extension endpoints
• Configurable resolution, fps, video length, and audio toggle
• Webhook support for async generation callbacks
• SDKs for Python and Node.js
• Usage-based pricing — pay per generation, no monthly minimum

Minimum self-hosting requirements:

• NVIDIA GPU with 24GB+ VRAM (1080p output)
• 40GB+ VRAM recommended for 4K (NVIDIA A100 / H100)
• Docker image available for simplified deployment

Wan 3.0 vs Wan 2.7 vs Wan 2.6 — Complete Feature Comparison

Wan 3.0 is not an iterative improvement over Wan 2.7 — it's a different generation of the model. If you've been working around the limitations of earlier versions, most of those workarounds are no longer necessary.