Wan 2.7: AI Video Model, Features, Prompts & Guide

Wan 2.7 is the core AI video model from Alibaba's Tongyi Wanxiang family — the foundational release powering text-to-video and image-to-video generation with strong quality, prompt adherence, and multi-style output.

How is Wan 2.7 different from Wan 2.7 Videoedit?

Wan 2.7 is the base generation model (text/image → new video). Videoedit is a specialized variant focused on editing and post-processing existing footage. Both share the Wan 2.7 foundation and prompt syntax.

Does Wan 2.7 support image-to-video?

Yes — upload a starting frame with motion instructions, and Wan 2.7 animates it with standard rendering quality while preserving the subject.

How do I write good Wan 2.7 prompts?

Use the standard structure: [subject] + [action] + [setting] + [camera/mood]. Wan 2.7 rewards detailed, multi-step prompts. Example: "a traditional Chinese ink-painting style crane flying over misty mountains at sunrise, slow dolly shot, cinematic."

What styles can Wan 2.7 handle?

Realistic, cinematic, animated, and stylized looks all work well. Wan 2.7 is especially strong at Chinese cultural and ink-painting aesthetics, plus general cinematic output.

Is Wan 2.7 free?

Alibaba typically offers a free tier with limited credits plus paid plans. Check current Tongyi/Alibaba Cloud pricing for exact free allowances.

What resolutions does Wan 2.7 support?

Wan 2.7 supports multiple resolutions up to HD/2K depending on the plan and settings, with higher resolutions requiring more render time and credits.

When should I use Wan 2.7 instead of specialized variants?

Use base Wan 2.7 for standard generation from prompts or images. Use Videoedit when you need to modify, extend, or restyle existing footage — the two complement each other in a full production pipeline.

Ready to work with Wan 2.7?

Keep the selected model, original brief, settings, and review notes together before sharing a result.

Open generation workspace

What Wan 2.7 is designed to handle

mixed input video
cinematic travel shots
continuation planning

Useful workflows for Wan 2.7

mixed input video
cinematic travel shots
continuation planning

How to use Wan 2.7

Choose the Wan 2.7 taskDecide whether the brief is video creation and planning and identify the single visual or planning outcome that matters most.
Prepare inputs and constraintsUse the current workspace controls, keep references authorized, and record the input role described in the Wan 2.7 case.
Run a small comparisonKeep the prompt and settings together, then change one decision at a time so the comparison remains explainable.
Review before reuseInspect the full result or structured text, proofread details, and complete rights, disclosure, and factual review before sharing.

Capabilities and target notes

Modality: Video
Best for: mixed input video, cinematic travel shots
Creative task: video creation and planning
Available workflow: auto-routed text-to-video, first/last-frame image-to-video, or continuationUse the workspace above to confirm the controls currently available for this model.

Limits and review checks for Wan 2.7

Auto-routing does not guarantee the same behavior for every input.
Video source rights and final factual review remain necessary.

What Wan 2.7 is designed to handle

Wan 2.7 is a video model available in PerchanceAI. a routing-aware brief that declares the intended input mode before the scene. Use the model picker in the workspace to confirm the currently available option and controls.

• Best suited to: video creation and planning.
• Supported workflow: auto-routed text-to-video, first/last-frame image-to-video, or continuation.

Inputs and settings for Wan 2.7

Build a Wan 2.7 request from the input notes rather than from a neighboring model's schema. The shared workspace exposes the current controls; preserve the source role and record the setting used for each comparison.

• Choose text, frame, or continuation mode explicitly in the workspace.
• Keep custom audio and prompt-extension choices separate from the visual brief.

Where Wan 2.7 fits a creative workflow

The five cases below use distinct briefs for Wan 2.7. Media, when present, states its provenance and is not presented as a PerchanceAI-generated benchmark.

• mixed input video
• cinematic travel shots
• continuation planning

Review limits for Wan 2.7

Review a Wan 2.7 result at its intended size, compare it with the original brief, and check identity, text, audio, factual detail, and source rights before sharing. Auto-routing does not guarantee the same behavior for every input.

• Auto-routing does not guarantee the same behavior for every input.
• Video source rights and final factual review remain necessary.

A repeatable Wan 2.7 evaluation

A useful comparison keeps the prompt, input role, selected settings, and output review together. Change one variable at a time so a later decision can be traced back to the Wan 2.7 brief.

• Keep the original prompt beside the revised prompt.
• Save the selected settings with the output review.
• Do not infer an unsupported capability from a visual result.

Prompt examples for Wan 2.7

1. coastal sunset drive

Wan 2.7: coastal sunset drive; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.

2. first-frame journey start

Wan 2.7: first-frame journey start; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.

3. last-frame destination

Wan 2.7: last-frame destination; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.

4. short continuation clip

Wan 2.7: short continuation clip; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.

5. custom-audio scene

Wan 2.7: custom-audio scene; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.