Sora 2 Pro

Key features

• Multimodal generation (video + audio) — Sora-2-Pro generates video frames together with synchronized audio (dialogue, ambient sound, SFX) rather than producing video and audio separately.
• Higher fidelity / “Pro” tier — tuned for higher visual fidelity, tougher shots (complex motion, occlusion, and physical interactions), and longer per-scene consistency than Sora-2 (non-Pro). It may take longer to render than the standard Sora-2 model.
• Input versatility — supports pure text prompts, and can accept image input frames or reference images to guide composition (input_reference workflows).
• Cameos / likeness injection — can insert a user’s captured likeness into generated scenes with consent workflows in the app.
• Physical plausibility: improved object permanence and motion fidelity (e.g., momentum, buoyancy), reducing unrealistic “teleporting” artifacts common in earlier systems.
• Controllability: supports structured prompts and shot-level directions so creators can specify camera, lighting, and multi-shot sequences.

Technical details & integration surface

Model family: Sora 2 (base) and Sora 2 Pro (high-quality variant).
Input modalities: text prompts, image reference, and short recorded cameo-video/audio for likeness.
Output modalities: encoded video (with audio) — parameters exposed through /v1/videos endpoints (model selection via model: "sora-2-pro"). API surface follows OpenAI’s videos endpoint family for create/retrieve/list/delete operations.

Training & architecture (public summary): OpenAI describes Sora 2 as trained on large-scale video data with post-training to improve world simulation; specifics (model size, exact datasets, and tokenization) are not publicly enumerated in line-by-line detail. Expect heavy compute, specialized video tokenizers/architectures and multi-modal alignment components.

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API endpoints & workflow: show a job-based workflow: submit a POST creation request (model="sora-2-pro"), receive a job id or location, then poll or wait for completion and download the resulting file(s). Common parameters in published examples include prompt, seconds/duration, size/resolution, and input_reference for image-guided starts.

Typical parameters :

• model: "sora-2-pro"
• prompt: natural language scene description, optionally with dialogue cues
• seconds / duration: target clip length ( Pro supports the highest quality in available durations)
• size / resolution: community reports indicate Pro supports up to 1080p in many use cases.

Content inputs: image files (JPEG/PNG/WEBP) can be supplied as a frame or reference; when used, the image should match the target resolution and act as a composition anchor.

Rendering behavior: Pro is tuned to prioritize frame-to-frame coherence and realistic physics; this typically implies longer compute time and higher cost per clip than non-Pro variants.

Benchmark performance

Qualitative strengths: OpenAI improved realism, physics consistency, and synchronized audio** versus prior video models. Other VBench results indicate Sora-2 and derivatives sit at or near the top of contemporary closed-source and temporal coherence.

Independent timing/throughput (example bench): Sora-2-Pro averaged ~2.1 minutes for 20-second 1080p clips in one comparison, while a competitor (Runway Gen-3 Alpha Turbo) was faster (~1.7 minutes) on the same task — tradeoffs are quality vs render latency and platform optimization.

Limitations (practical & safety)

• Not perfect physics/consistency — improved but not flawless; artifacts, unnatural motion, or audio sync errors can still occur.
• Duration & compute constraints — long clips are compute-intensive; many practical workflows limit clips to short durations (e.g., single-digit to low-tens of seconds for high-quality outputs).
• Privacy / consent risks — likeness injection (“cameos”) raises consent and mis-/disinformation risks; OpenAI has explicit safety controls and revocation mechanisms in the app, but responsible integration is required.
• Cost & latency — Pro-quality renders can be more expensive and slower than lighter models or competitors; factor in per-second/per-render billing and queuing.
• Safety content filtering — generation of harmful or copyrighted content is restricted; the model and platform include safety layers and moderation.

Typical and recommended use cases

Use cases:

• Marketing & ads prototypes — rapidly create cinematic proofs of concept.
• Previsualization — storyboards, camera blocking, shot visualization.
• Short social content — stylized clips with synchronized dialogue and SFX.
• Internal training / simulation — generate scenario visuals for RL or robotics research (with care).
• Creative production — when combined with human editing (stitching short clips, grade, replace audio).

When not to