Video generation

Available now

Wan 2.7

by Alibaba

Alibaba's flagship video model.
Plans the shot before generating, with reference‑locked characters and accurate in‑frame text.

Wan 2.7

by Alibaba

Key features

Thinking Mode prompt reasoning

Wan 2.7 reasons through the prompt, planning composition, motion, and scene logic before generating. Complex briefs hold together better.

Reference-to-video consistency

Lock subject identity across shots using image or video references. Faces, costumes, and props stay coherent across multi-shot cuts.

Cinematic motion and lighting

Stronger camera language, dolly, tracking, orbital, with plausible lighting changes over time. Output reads as directed, not generated.

Long-text and color control inside frames

Renders typography and brand color inside the frame more reliably than earlier Wans. Useful for product, ad, and title-card work.

First-and-last-frame and multi-image input

Beyond reference video, Wan 2.7 supports first-and-last-frame generation, 9-grid multi-image input, and subject-plus-voice references.

Instruction-based editing

Edit an existing clip with plain-language instructions. Change wardrobe, swap backgrounds, adjust lighting, or restyle a scene in place.

Technical specifications

Up to 1080p

480p, 720p, 1080p tiers

27B MoE

Mixture-of-experts video DiT

T2V, I2V, Ref, Edit

Four core generation paths

Reasoning

Thinking-Mode prompt planning

Use cases

Cinematic short-form video

Direct multi-shot scenes with consistent characters, camera language, and lighting. Ad creative, brand films, and narrative shorts.

Character-consistent campaigns

Lock a subject from a reference image or clip, then generate multiple variations and shots without losing identity across the campaign.

Product and brand video

Use Wan 2.7's in-frame text and color control for product reveals, title cards, and ad creative that needs typography baked into the shot.

Iterative editing

Generate a base clip, then use instruction-based editing to refine wardrobe, lighting, or background without re-rolling the full generation.

Prompt examples

Cinematic shot

Slow dolly forward through a fog-bound forest at dawn, golden rays cutting between trees, a lone figure ahead, cinematic, 1080p

Edit prompt

Reference-led

Generate a video of this character walking through a rain-soaked Tokyo alley, neon reflections, mid shot, cinematic

Edit prompt

Product reveal

Studio-lit rotating reveal of a sleek minimalist watch on a marble slab, brand title appearing in clean typography, 5 seconds

Edit prompt

Simple pricing

Get started for free today, with the option to upgrade or cancel anytime.

Basic

/ month

billed as $0 per year

900 monthly credits

1 user only

All models

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Standard

/ month

billed as $0 per year

3200 monthly credits

1 user only

All models

Workflows

Pro

/ month

billed as $0 per year

6200 shared monthly credits

1 user

+ up to 4 more at extra cost

All models

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Pro Max

/ month

billed as $0 per year

24000 shared monthly credits

1 user

+ up to 9 more at extra cost

All models

Workflows

Enterprise

For higher limits

Custom

pricing and billing terms

High-volume credits

Custom seat limits

All models

Workflows

Free

For playing around

forever free

Up to 20 credits

1 user only

Limited models

Workflows

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FAQs

What is Wan 2.7?

Wan 2.7 is Alibaba's flagship Tongyi Wanxiang video model. It introduces Thinking Mode, prompt reasoning before generation, on top of a 27B-parameter mixture-of-experts architecture, with cinematic 1080p output.

What is Thinking Mode?

Thinking Mode is a planning step before generation. Wan 2.7 reasons through prompt elements, composition, and motion before producing frames, which reduces artifacts and keeps complex briefs coherent.

How is Wan 2.7 different from Wan 2.6?

2.7 adds Thinking Mode, scales the architecture, improves reference-driven consistency, and renders in-frame text and color more reliably. Use 2.6 for fast generations, 2.7 when the prompt is complex or character consistency matters.

Does Wan 2.7 support reference-driven generation?

Yes. Pass in image or video references to lock subject identity across shots, useful for multi-shot campaigns and serialized content where the same character needs to appear consistently.

How do I use Wan 2.7 on Morphic?

Open Copilot, describe the shot or attach a reference, and pick Wan 2.7. For complex multi-element scenes, brand work with text in-frame, or character-consistent sequences, Wan 2.7 is the right pick over faster but less considered models.

How does Wan 2.7 compare with Veo 3.1, Seedance 2.0, and Kling 3.0?

Wan 2.7 leads on prompt reasoning and in-frame text/color control. Veo 3.1 leads on photorealism and 4K. Seedance 2.0 leads on native audio-visual generation and human motion. Kling 3.0 leads on multi-shot director-mode control. Pick by job.

Wan 2.7

Key features

Thinking Mode prompt reasoning

Reference-to-video consistency

Cinematic motion and lighting

Long-text and color control inside frames

First-and-last-frame and multi-image input

Instruction-based editing

Technical specifications

Use cases

Cinematic short-form video

Character-consistent campaigns

Product and brand video

Iterative editing

Prompt examples

Cinematic shot

Reference-led

Product reveal

Simple pricing

FAQs

Other models