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Alibaba

Wan 3.0

Wan 3.0 is a video generation model from Alibaba, the latest numbered iteration of the Wan family of text- and image-conditioned video synthesis models.

From
$0.060 / second
across 1 provider

API Pricing

ProviderPrice /secAlt /sec
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$0.060/sec$0.060/sec

Prices updated daily. Last check: Oct 29, 2026

Model Details

General

Creator
Alibaba
Modalities
Text

Capabilities

Strengths & Limitations

Strengths

  • Purpose-built for video generation rather than adapted from a text or image model
  • Part of Alibaba's established Wan line, so prompting conventions carry over from earlier generations
  • Available through hosted API providers, removing the need to provision and manage high-memory GPU instances
  • Asynchronous job-based inference fits batch pipelines that render many clips without blocking
  • Output is a finished video file, usable directly in editing and social publishing workflows
  • Multiple providers may host the model, allowing cost and latency comparison through the pricing table on this page

Limitations

  • Our catalog does not track confirmed resolution, frame rate, or maximum clip duration for Wan 3.0
  • Video generation latency is measured in tens of seconds to minutes, making real-time or interactive use impractical
  • Billing is typically per second of output or per job, which makes cost harder to forecast than token-based pricing
  • Fine-grained control over camera movement, timing, and continuity is generally weaker than conventional animation or 3D pipelines
  • Longer clips tend to accumulate drift in subject identity and scene consistency, a common constraint across diffusion-based video models

Key Features

•Text-to-video generation from natural language prompts
•Part of the Wan model family from Alibaba
•Asynchronous generation workflow with job submission and result retrieval
•Video file output suitable for direct use in editing software
•Hosted API access across one or more inference providers
•Duration- or job-based billing model rather than token metering

About Wan 3.0

Wan 3.0 is a video generation model developed by Alibaba. It sits in the company's Wan line of generative video systems, which are offered through API endpoints rather than as a chat or text-completion model. As a version 3.0 release, it follows earlier Wan generations, and inference access is typically brokered through cloud or aggregator providers rather than consumed directly as a local model by most users. Unlike text models, Wan 3.0 is evaluated on the dimensions that matter for video synthesis: prompt adherence, motion coherence across frames, subject and identity consistency over a clip, output resolution, clip duration, and generation latency. Because video generation is compute-intensive, requests are usually asynchronous — a job is submitted, polled, and then a rendered video file is retrieved — and billing is commonly tied to seconds of video or per-generation job rather than to tokens. Our catalog does not track a confirmed specification sheet for Wan 3.0's supported resolutions, maximum clip length, or conditioning modes, so prospective users should confirm those details with the specific provider listed in the pricing table on this page. In practice, models in this category are used for short-form social video, marketing and product b-roll, concept previsualization, animated assets for games and apps, and rapid iteration on storyboards before committing to live-action or 3D production. Wan 3.0 competes with other hosted video generators in the same workflow slot, and the practical differentiators between them tend to be provider availability, queue times, per-second cost, and how faithfully each handles the specific motion and style a given project needs.

Common Use Cases

Wan 3.0 suits teams producing short video clips at volume where production cost and turnaround matter more than frame-exact creative control: social and advertising creative tests, product and lifestyle b-roll, animated backgrounds and loops, concept previsualization and storyboard animatics, and synthetic footage for prototyping video pipelines. It is a reasonable fit for generating many prompt variations cheaply and selecting the best take, and for augmenting an existing editing workflow with filler or transition shots. It is a poor fit for work demanding precise continuity across long sequences, exact brand or character likeness, legible on-screen text, or interactive latency — those cases still call for traditional production, 3D rendering, or heavy post-processing of generated output.

Frequently Asked Questions

What is Wan 3.0 best used for?

Short-form video generation from prompts — social creative, marketing b-roll, animated concept work, and previsualization. It is best suited to workflows that generate several variations and pick the strongest result, rather than to shots requiring exact continuity or precise art direction.

How much does Wan 3.0 cost?

Pricing varies by provider and by pricing type, and video models are usually billed per second of generated video or per generation job rather than per token. Check the pricing table on this page for current rates from the providers we track.

Who created Wan 3.0?

Wan 3.0 was created by Alibaba, as part of its Wan family of video generation models.

Is Wan 3.0 a chat model?

No. Wan 3.0 is a video generation model, so it does not have a context window, token throughput, or conversational interface in the way a text LLM does. It takes a prompt and returns a rendered video file.

What resolution and clip length does Wan 3.0 support?

We do not track confirmed resolution, frame rate, or maximum duration figures for Wan 3.0 in our database. These limits are usually set by the hosting provider, so verify them with the specific provider you plan to use from the pricing table above.

How does generation latency compare to a text model?

Substantially slower. Video synthesis typically takes tens of seconds to several minutes per clip and is served through an asynchronous submit-and-poll API, so it should be designed into batch or background pipelines rather than user-facing real-time features.