Sora 2
What is Sora 2?
Sora 2 is OpenAI's second AI video model, generating longer and more realistic video clips from text descriptions with a strong understanding of how the physical world looks and behaves.
At a glance
- Type of model
- Text-to-video, image-to-video, and video-to-video generation model
- Developed by
- OpenAI
- Key capability
- Generates long, physically coherent, cinematic video clips from text prompts with strong world simulation fidelity
- How it fits in AI workflow
- Used as a high-quality video generation engine for content creation, film pre-visualisation, and AI filmmaking; accessible via ChatGPT and the Sora platform
- Related terms
- SoraRunway gen-4.5Text-to-videoWorld modelKlingMiniMax video
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How it compares
Compared with related concepts
Sora 2 vs Runway Gen-4. 5: Both are leading commercial text-to-video models targeting professional and creative users. Sora 2 emphasises world simulation depth and long-form video coherence, while Runway Gen-4. 5 is deeply integrated into a production workflow platform with tools for character performance, multi-shot editing, and reference-based consistency. Sora 2 tends to be cited for raw generation quality; Runway for workflow completeness.
Pro tip
Sora 2 responds well to detailed scene descriptions that include environmental context, lighting conditions, and physical dynamics: the model's world simulation strengths are most apparent when your prompt gives it a rich physical scenario to reason about rather than a simple visual description.
Types and variations
- Sora 2 builds on the original Sora model's architecture with improvements across duration, resolution, and physical coherence.
- Within the Sora platform, users can generate clips using text prompts, use images as starting frames, or transform existing video through re-generation.
- The platform supports multiple aspect ratios.
- Scene-level tools allow users to work with multiple generated shots and arrange them into sequences, moving Sora beyond single-clip generation toward multi-shot composition.
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- Sora 2 is used by filmmakers for high-quality pre-visualisation, by advertising and marketing teams for campaign video production, by music video directors for stylised visual generation, and by content creators producing AI-generated video art for digital platforms.
- Its strong physical realism makes it particularly useful for scenes involving complex environments, fluid dynamics, or realistic human and animal motion.
- It is also used as a benchmarking reference when evaluating other video generation models.
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FAQs
Sora 2 is OpenAI's second-generation AI video generation model, capable of producing long, photorealistic, physically coherent video clips from text descriptions and image inputs.
Sora is accessible through OpenAI's Sora platform and is available to ChatGPT Plus and Pro subscribers. Access and feature availability may vary: check OpenAI's website for current subscription details and regional availability.
Sora 2 can generate video clips of up to approximately 20 seconds in a single generation, which is notably longer than many competing models. Exact duration limits depend on resolution and current platform settings.
Sora 2 improves on the original with longer video duration, better physical realism, stronger temporal coherence, and more refined control over generation. It also includes expanded platform features for multi-shot composition and scene management.
Yes, Sora 2 supports image-to-video generation, allowing a provided image to serve as the starting frame of the generated video. This is useful for animating existing artwork, photographs, or AI-generated images.
Sora 2 is developed with a world simulation philosophy: the model is trained to understand physical relationships, not just visual patterns. This results in generated video where physics, lighting, and object interactions are more coherent than in models trained purely on appearance.
Sora 2 is used by professional filmmakers for pre-visualisation and high-quality shot generation. While it does not yet replace traditional production for most feature-level work, its output quality is sufficient for many commercial, short-form, and experimental production contexts.
