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Magi(Sand.ai)

Autoregressive video model with open weights

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What Magi is

Magi is a video generation model from Sand.ai aimed at creators and developers. It generates coherent shots from text or reference material, with emphasis on detail consistency, temporal continuity and stable inference efficiency.

Beyond the hosted experience, Sand.ai publishes open inference code and model weights and offers an API, so teams can deploy the model in their own environment. The company also ships MagiAttention, an inference optimization for its toolchain.

What you can do with it

  • Generate video from text prompts or supplied material
  • Self-host the model using the published weights and code
  • Integrate generation into a product through the API
  • Tune deployments for production workloads
  • Test the model against your own shot requirements

Who it is for

  • AI video development teams
  • Creative studios needing controllable generation
  • Enterprises that require private deployment
  • Developers evaluating model extensibility

What to watch out for

  • Self-hosting assumes real engineering capacity for serving and maintenance
  • Generated footage still needs rights checks before commercial use
  • Model behavior on your material should be tested rather than assumed
  • Compute costs at scale depend on your own deployment

Pros & cons

✓ What we like

  • Open weights and code enable private deployment
  • API available for product integration
  • Focus on temporal consistency in output

! What to watch out for

  • Self-hosting requires infrastructure work
  • Rights on generated footage remain with the user
  • Quality on specific material must be verified

FAQ

What is Magi designed for?

High-quality video generation and integration into business workflows, including private deployments.

Is the model open?

The public description cites open inference code and model weights alongside the hosted API.

Who is it aimed at?

Developers, creative studios and enterprises rather than only casual web users.

Last reviewed: 2026-09-16

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