What is Sora and what changed
Sora is OpenAI's text-to-video generation model. Compared to predecessors (Runway Gen-2, Pika), it offers three qualitative improvements: longer duration (up to 60s), physical coherence (objects don't deform inconsistently between frames), and controllability via natural language prompts.
How it works internally
Sora uses a diffusion transformer architecture: combines diffusion models (used in image generators like Stable Diffusion) with transformer architecture (used in language models). The output is sequences of patches representing pixels over time.
The key technical insight: Sora trains on simulated physics, not just video. The model develops an emergent intuition of how objects move and interact, even though it wasn't explicitly programmed with physical laws.
Capabilities and limitations
What it does well: short scenes with one subject, predictable camera movements, well-defined styles (cinema, animation, documentary). What still fails: complex physical interactions (objects breaking, water in detail), close human faces with realistic emotion, perfect temporal coherence over 60s.
duration
resolution
generation time
vs. Runway, Kling and the rest
Sora: the best for long sequences with coherence. Highest cost.
Runway Gen-3: the best for creative tools (specific style controls). Solid.
Kling: Chinese competitor that surprised by physical realism, especially in subject movement.
Veo (Google): announced with strong specs, limited public access.
Real use cases
Advertising and marketing: the main case. Generating product visualizations, brand concepts, ad variations without expensive shoots. Storyboarding: directors using Sora as fast pre-visualization. Content creators: short videos for social media. Education: simulations of historical, scientific, abstract events.
Conclusion
Sora isn't replacing professional video production for high-budget projects — but it's redefining "what is possible" in middle-budget. For small/medium brands, agencies and content creators, it's a productivity multiplier. The next 18-24 months are going to see widespread adoption.