Create longer AI videos with MAGI-1’s chunk-by-chunk generation and steady resource use. Turn your ideas into smooth, continuous scenes with no editing skills required.
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See how MAGI-1 AI generates video as a continuous pipeline, rather than creating the whole clip at once. Compare its control, scalability, and motion handling in real use.
Video is generated in 24-frame chunks, equal to about one second at 24 FPS. With MAGI-1, each chunk is denoised as a complete unit, while up to four chunks can run at the same time. This pipeline helps generation continue without treating the full video as one single process.
Peak inference cost stays steady as video length increases, which makes longer generation more predictable. In its largest version, MAGI-1 reaches 24 billion parameters and supports context lengths of up to 4 million tokens for extended sequences.
Adjust instructions as the video progresses instead of locking one prompt to the entire clip. Chunk-wise prompting in MAGI-1 lets later segments follow new direction, giving creators finer control over action, camera movement, and scene changes across the timeline.
Carry motion forward from earlier video context so each new segment responds to what came before. This causal temporal design helped MAGI-1 perform strongly on the Physics-IQ video continuation benchmark in its original evaluation.
Explore why creators choose MAGI-1 for open-model workflows. Review its technical access, native continuation support, and architecture before adding it to your stack.
Study more than a model card before you integrate it. Sand AI released the MAGI-1 technical report, pretrained weights, and inference code, giving developers direct access to the architecture, implementation, and deployment details.
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MAGI-1 generates video in sequential chunks rather than producing the entire sequence at once. This approach supports continuous generation and makes longer video extension a more natural part of its workflow.
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MAGI-1 predicts video chunks progressively instead of waiting for a complete sequence. This causal approach makes the model suitable for workflows where generation and playback can happen incrementally.
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Compare MAGI-1: autoregressive video generation at scale with other video models by length, context, and control. See how each option performs as video generation scales.
| Model | Generation Method | Length Limit | Best Input | Users |
|---|---|---|---|---|
| MAGI-1 | Autoregressive, 24-frame chunks | None, cost stays flat | Text + video | Researchers and developers |
| Magiclight AI | Script to storyboard | Up to 50 minutes | Scripts | Long-form stories and creator workflows |
| LongCat Avatar 1.5 | Audio-driven diffusion | Long speaking shots | Audio + image | ML engineers |
| Wan 2.1 | Whole-clip diffusion | Fixed clip length | Text + image | Open-source community |
Get MAGI-1 running on your own hardware in 4 simple steps. Choose the model, set it up, pick how you want to generate, and start creating.
Upload a first frame, last frame, or reference image to guide the scene. Use these inputs when you want tighter control over the subject, composition, or final look.
Describe what should happen in the video. Keep the subject, action, camera movement, and scene details clear so the model has a direct instruction to follow.
Choose Video, select the aspect ratio, set the clip length, and adjust any extra generation settings. Match these options to the format and platform you plan to use.
Review the generated video, then adjust the prompt, reference frames, or settings when needed. Refine the result until the motion and scene match your intent.
Research Engineer
Flat inference cost mattered most to us. MAGI-1 let us generate a 2-minute sequence on the same hardware budget as a 5-second one.
Independent Animator
Every other model made me stitch clips and hide the seams. Continuation is native in MAGI-1, so the motion carries across without me faking a transition.
VFX Creator
Physical motion is where most models give themselves away. MAGI-1 continues a falling object the way it should fall, which saved us a round of manual cleanup.
Developer
The technical report answered questions the README did not. Creating on MAGI-1 felt safer than generating on a model whose architecture nobody publishes.
MAGI-1 is an open-source video generation model from Sand AI. Released in April 2025, it generates video autoregressively in 24-frame chunks instead of processing the full clip at once.
Each 24-frame chunk is generated from earlier video context. This causal setup lets motion, scene details, and temporal information carry forward as the sequence grows.
There is no fixed ceiling, since peak inference cost stays constant regardless of length. Extending is the same operation as generating, so a clip continues rather than being stitched onto another one afterward.
Yes. Sand AI provides model weights and inference code for 24B and 4.5B versions, including distilled and quantized versions. MAGI-1.1 24B weights were also open-sourced in June 2026.
The 24B model is built for multi-GPU systems, with Sand AI recommending eight H100 or H800 GPUs. The 4.5B version can run on a single RTX 4090 or another GPU with at least 24GB of VRAM.
The main drawback is accessibility. MAGI-1 needs capable GPU hardware, local setup, and some technical knowledge, so it is less convenient than hosted video tools for casual creators.
Carry one clip past the point where other models give up. Study the paper afterward to see why the cost never moved.