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Google Ships Gemini Omni 1.2 Flash With Native 15-Second Video and a 30% Price Cut

Google published Gemini Omni 1.2 Flash on its AI blog, built around native 15-second video generation and editing plus stronger consistency across clips. The update also cuts pricing by 30%, positioning speed and cost as the core pitch of this tier.

Published
谷歌发布 Gemini Omni 1.2 Flash:原生 15 秒视频生成与编辑,价格下调 30%
Image source: blog.google

Google published Gemini Omni 1.2 Flash on its AI blog, framing the release around two claims: native 15-second video generation and editing, and a 30% lower price.

The headline promise is longer clips. Gemini Omni 1.2 Flash generates and edits video natively at 15 seconds, and Google says the update improves consistency, so subjects and scenes hold together across a sequence instead of drifting from shot to shot.

Price is the second claim. A 30% reduction matters more for video than for text, because every generated second carries real compute cost and repeated takes are how most video work actually gets done.

The Flash name signals the positioning: a fast, cost-efficient tier for developers who need to generate and iterate at volume, not a flagship chasing maximum fidelity on a single hero shot.

Video generation has been one of the most expensive surfaces in generative AI, and capability gains usually arrive with a higher bill. Cutting the unit price by 30% changes what teams can afford to try.

The competitive axis is now threefold: clip length, cross-shot consistency, and price per second. Gemini Omni 1.2 Flash pushes on all three at once, which makes the release read as a volume play rather than a demo.

What to watch next is whether the consistency claim survives independent multi-shot editing tests, how the 15-second ceiling reshapes narrative and advertising workflows, and whether the 30% cut holds at scale or arrives with rate and quota limits. The public material so far covers length, consistency and price; the rest will come from developers putting it to work.

Why it matters

A one-third price cut on video generation lowers the cost of iteration, moving batch production closer to a routine workflow rather than an expensive one-off experiment. Advancing length, consistency and price together also shifts competition toward unit economics.

GoogleGeminiVideo GenerationMultimodal
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