OpenAI’s text-to-video AI. Type a description — get a video. The tool that made filmmakers, marketers, and content creators simultaneously excited and anxious. Full history, how to use it, 15 prompts, and technical depth. Three reading levels. Official sources only.
Sora is an AI that creates videos from text descriptions. You type what you want to see — “a golden retriever puppy playing in autumn leaves in a park, cinematic, warm afternoon light” — and Sora generates a short video of it. No camera. No actors. No filming. Just words, turned into moving images.
When OpenAI released example videos from Sora in February 2024, many people watching them could not immediately tell they were AI-generated. A video of people walking in Tokyo. A woolly mammoth in a snowy field. A woman in sunglasses walking down a street. The quality was unlike anything seen from AI video before. Professional filmmakers, YouTubers, and advertisers immediately understood the implications.
On 15 February 2024, OpenAI released a blog post and technical overview describing Sora. They did not release the tool to the public — they released example videos and a technical description. The response was extraordinary. The videos showed: a woman walking through Tokyo, a drone shot of a coastal city, a close-up of a dog, ocean waves, a cat waking someone up — all AI-generated, all high-quality, all up to one minute long.
The reactions ranged from wonder to alarm. Hollywood began discussing the implications for visual effects and stock footage. Advertisers saw potential. Regulators began asking questions about synthetic media and misinformation.
OpenAI gave early access to filmmakers, visual artists, and researchers to test Sora and provide feedback before public release. Several short films created with Sora were released publicly — demonstrating the tool’s capabilities and limitations.
Sora became available to ChatGPT Plus and Pro subscribers in December 2024, with a standalone interface at sora.com. ChatGPT Plus users received a limited allocation of video generations; Pro users received more. The videos could be up to 20 seconds long at 1080p resolution.
Sora continued developing — longer video durations, better physics simulation, improved character consistency across frames, and the ability to extend existing videos or blend multiple scenes. Integration with other OpenAI tools allowed video creation from images and vice versa.
Sora is available to ChatGPT Plus ($20/month) and Pro ($200/month) subscribers. Plus users receive a limited monthly allocation of video generations; Pro users receive significantly more. Videos can be up to 20 seconds at 1080p.
Source: sora.com — April 2026
Sora responds best to prompts that describe the scene like a cinematographer’s brief: the subject, action, setting, camera movement, lighting, and mood. Be specific about motion — Sora’s strength is generating realistic movement.
Sora is a diffusion transformer model that operates on spacetime patches — a generalisation of the image patch approach used in vision transformers. Rather than processing video as a sequence of frames, Sora encodes video into compressed patches of spacetime (temporal and spatial dimensions together) and performs diffusion in this compressed representation.
This unified representation allows Sora to generate videos of variable durations, resolutions, and aspect ratios from a single model — a significant departure from earlier video generation approaches that were constrained to fixed dimensions.
OpenAI (2024). “Video generation models as world simulators.” OpenAI Technical Report. openai.com/research/video-generation-models-as-world-simulators
Note: Sora’s full architecture is not published. The technical report provides an overview of the approach without full implementation details.
OpenAI’s technical report describes Sora not just as a video generator but as a “world simulator” — a model that has learned emergent properties of 3D consistency, object persistence, and physical interaction from video data alone, without any explicit 3D supervision. This framing is significant: it suggests that video generation models may develop a form of world modelling as a consequence of learning to predict coherent video sequences.