Wan 3.0 Features — Everything the Model Can Do

Native 1080P Video Output — Full HD from the Generation Task

Wan 3.0 exposes native 1920×1080 output directly in the generation form. The result is suitable for YouTube, landing pages, product demos, presentations, and most social advertising workflows without presenting a lower-resolution upscale as a native master.

Resolution is only one part of delivery quality. Review moving subjects, faces, labels, small objects, and the final frame before publishing. A finishing workflow can still add captions, color work, or an optional upscale when a particular distribution channel requires it.

Supported output configurations:

• 1080P (1920×1080) as the highest native output exposed by the current form
• 480P and 720P options for lower-cost drafts where available
• Aspect ratios: 16:9 (landscape), 9:16 (vertical/mobile), 1:1 (square)
• Export format: downloadable MP4

The downloaded MP4 can move directly into a normal editing, captioning, review, or publishing workflow. Always inspect the actual file properties when a delivery platform has strict technical requirements.

Try native 1080P generation with the Wan 3.0 AI Video Generator

Reference-Guided Motion — Give Important Details a Clear Source

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 lets you add image, video, and audio references to a generation brief. Each reference can be identified with an @ tag so the prompt can explain whether it controls a character, product, location, camera move, voice, or sound direction.

In practice, this means:

• Liquids — inspect trajectory, splash timing, surface detail, and whether the sound matches
• Cloth — check folds, contact points, and continuity as the subject moves
• Hair — check silhouette and direction through fast turns or camera changes
• Rigid objects — check collision timing, direction changes, and the final resting state
• Smoke, steam, and particles — check whether they remain attached to the scene instead of drifting

Reference-guided prompting is particularly useful for product video, food and beverage content, and any scenario where viewers already know how an object should look or move. Use the source video for motion direction and the prompt for the intended camera, lighting, and timing.

Useful motion tests: coffee pours, perfume sprays, fabric drapes, crashing waves, candle flames, falling leaves, and product collisions.

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Complete Short-Form Scenes — Up to 30 Seconds per Generation

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 exposes durations from 4 to 30 seconds. A longer timeline can hold an establishing beat, subject action, transition, and closing frame in one generation. Use focused references and ordered prompt beats when identity or product continuity matters.

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 live duration control stops at 30 seconds. Longer clips still require careful review for faces, wardrobe, labels, environment continuity, dialogue, and the closing frame.

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:

In the official sample above, a violin performance is staged in heavy rain. Listen for how the musical phrasing, bow movement, rainfall texture, and dramatic lighting work as one audiovisual beat.

• 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 availability and credit cost are shown by the active generation workflow

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Three Ways to Start — Text, References, or First / Last Frames

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.

First / Last Frame
Define the opening frame and an optional destination frame when a transition, reveal, or product movement needs a controlled start and finish. The prompt describes how the scene should travel between them.

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

Browser Production Workflow — Prepare, Generate, Review, and Download

The current product is documented as a hosted browser workflow. It does not promise downloadable model weights or a public developer API. The generator focuses on preparing references and submitting a task without requiring local GPU infrastructure.

Images can be cropped before upload, while video and audio references can be trimmed to the available duration budget. Upload and task progress remain visible throughout the workflow.

Workspace capabilities:

• Text-to-video, reference generation, and first / last frame modes
• Configurable resolution, aspect ratio, duration, and audio
• Image crop plus video and audio trimming
• Visible upload and generation progress
• Credit estimate before task submission

Before publishing:

• Review identity, labels, motion, and the closing frame
• Check audio timing, dialogue, captions, and licensed music
• Confirm the active plan's commercial-use terms

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.