Sora 2

Sora 2 is OpenAI’s video generation model, known for physical plausibility. What it handles well, where it struggles, and how to prompt it.

Last verified 2026-08-26

Sora 2 is OpenAI’s video generation model and one of the most recognised names in the category — it draws substantial search interest by name alone. Higgsfield exposes it as one of its video model options.

What it is known for

• Physical plausibility. Sora’s notable strength has been motion that obeys intuitive physics — weight, momentum, how objects contact and occlude one another. Motion tends to look like it has mass rather than sliding.
• Scene coherence. It holds a consistent space over a shot reasonably well, so the setting does not reinvent itself as the camera moves.
• Prompt interpretation. It handles descriptive, natural-language prompts without demanding rigid syntax.

Where it still struggles

Shared with the category rather than unique to Sora:

• Hands and fine manipulation — anything gripping or operating an object.
• Readable text — signage and packaging warp between frames.
• Long-duration coherence — quality degrades with length; short clips edited together beat one long generation.
• Specific likeness from text — describing a person does not reproduce that person.

Prompting

Sora responds well to prompts written like a shot description rather than a keyword list. State the subject and its action, then the camera, then the light. Cinematic vocabulary is understood — shot sizes, lens character, time of day — and is more useful than quality adjectives, which do close to nothing.

Keep it to one action. The most common failure is a prompt asking for a sequence of events inside a few seconds; the model compresses them into something incoherent rather than picking one.