Together AI 上线 Wan 2.7 Text-to-Video，四模型视频套件将陆续推出

Summary

AI video is easy to generate and hard to steer. A team can get a promising clip from a prompt, but continuing it, matching a reference, or revising it without starting over usually means leaving the model that made it and patching the rest together somewhere else. The more control a project needs, the more the workflow turns into re-renders, handoffs, and manual cleanup. That is the gap Wan 2.7 is built to close across generation, continuation, reference-driven workflows, and editing.

On Together AI, that expanded control surface becomes one platform instead of another disconnected toolchain. Wan 2.7 comes to Together AI, the AI Native Cloud, as a four-model suite rolling out from text-to-video into image-to-video, reference-to-video, and video edit. That gives teams a clearer path from first generation to continuation, reference-driven control, and revision through the same APIs, authentication, and billing surface they already use across the rest of their multimodal stack.

Wan 2.7 Text-to-Video ( Wan-AI/wan2.7-t2v ) is available today on Together AI. It provides a stronger starting point for campaign content, product videos, and creative prototyping than a plain prompt-to-video surface by supporting:

WAN 2.7

Epic snowy train shot with cinematic scale.

$0.10/video

WAN 2.7

Sci-fi astronaut corridor with red emergency lighting.

$0.10/video

WAN 2.7

Luxury rooftop fashion film with flowing motion.

$0.10/video

WAN 2.7

Photoreal hummingbird macro with rainforest wonder.

$0.10/video

As the rest of the Wan 2.7 suite rolls out, developers will get more control over how video is driven and structured.

Image-to-Video ( Wan-AI/wan2.7-i2v ) supports:

Reference-to-Video ( Wan-AI/wan2.7-r2v ) supports:

Wan 2.7 Video Edit ( Wan-AI/wan2.7-edit ) gives teams a more direct way to modify footage without bouncing into separate editing systems for every pass. It extends the suite with:

Instead of splitting those jobs across separate tools, they stay inside one coordinated workflow, which reduces handoffs and makes iteration easier to manage.

The Wan 2.7 Text-to-Video model is available today on Together AI Serverless Inference starting at $0.10 per second of generated video through the endpoint Wan-AI/wan2.7-t2v .

If you are already using Together AI for text or image inference, adding video generation works the same way:

import time from together import Together

client = Together()

job = client.videos.create( model="Wan-AI/wan2.7-t2v", prompt="A cinematic product video of a running shoe on wet pavement, smooth camera arc, dramatic reflections", resolution="1080P", ratio="16:9", seconds="5" )

print(f"Job ID: {job.id}")

# poll until the video is completed while True: status = client.videos.retrieve(job.id)

if status.status == "completed": print(f"Video URL: {status.outputs.video_url}") break elif status.status == "failed": print(f"Error: {status.error}") break

time.sleep(5)

)

Production deployment Start with serverless endpoints for development and testing.

On Together AI, teams can move from experimentation to production on the same platform, with volume pricing and enterprise deployment options when they are ready for more control over production workloads.

→ Try Wan 2.7 T2V in the Playground

→ Read the Wan 2.7 T2V Quickstart

→ Contact Sales for volume pricing and enterprise deployment

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