Google launches Gemini Omni 1.1 Flash video model

Gemini Omni 1.1 Flash, which Google DeepMind shipped on August 27, 2026, reframes what a generative video model is for: rather than a single short clip, it is built around continuity and control. Scene extension now reads up to 10 seconds of prior context per chained segment instead of only the last frame, letting a 10-second increment stack into a continuous scene up to 40 seconds long ^[1]- a change early testers on Reddit's r/singularity described as a genuine leap over the old last-frame-only chaining approach, not just an incremental tweak. First- and last-frame control lets a developer pin the start and end images of a shot so the model fills in camera orbits, zooms, or seamless loop transitions between them ^[1]. A separate reference-footage feature lets developers upload up to three seconds of existing video as a style or character anchor, carrying a character's look or a specific motion pattern into new generations ^[2]. On the production side, a 360p draft mode renders up to 60% faster and at roughly a third of the cost of the standard 720p tier, intended purely for fast iteration before a creator commits to a final, more expensive render at 1080p or native 4K ^[3]. Individually these are incremental controls; together they push the product from 'type a prompt, get a clip' toward an actual editing and directing surface.

The release lands against a specific competitive backdrop. OpenAI shut down its Sora consumer app on April 26, 2026, with the Sora API itself set to sunset on September 24, 2026 ^[10]. Google's video line has moved in the opposite direction: Gemini Omni was announced at I/O on May 19, 2026 as DeepMind's first natively multimodal, any-to-any generative media model, explicitly unifying what had been separate video (Veo) and image (Imagen/Nano Banana) pipelines into one architecture ^[9], reached public developer preview on June 30, 2026 ^[11], and has now received its first point update barely two months later. That cadence - announce, ship a preview, then iterate quickly on the same model family right as a well-funded competitor exits the category - reads as Google using Sora's retreat as room to consolidate rather than merely keep pace. The rollout also isn't confined to Google's own surfaces: Omni 1.1 Flash shipped simultaneously into Adobe Firefly, Figma Weave, Runway, GMI Cloud, and a dedicated ComfyUI Partner Node covering text-to-video, image-to-video, reference-to-video, editing, and scene extension from one node ^[5], plus developer and enterprise access through the Gemini API, Google AI Studio, and the Gemini Enterprise Agent Platform ^[4]. Google's own product account also amplified the launch on X, and even a competing video-gen product, Pika, publicly acknowledged the release and called out overlapping capabilities of its own, like extension, first/last-frame control, multi-reference video, and 4K output - a rival tacitly conceding that Omni 1.1 Flash's feature set is now a reference point. Altogether that is a deliberate embed-everywhere strategy - Google is competing for the professional creative pipeline itself, not just for traffic to its own apps.

Two very different pictures of the model coexist. On the benchmark side, a third-party benchmark tracker placed Omni 1.1 Flash at #1 in its Text-to-Video ranking, more than 20 points ahead of the #3-ranked model, and #2 in its Image-to-Video ranking, a roughly 25-point improvement over the prior Omni Flash's score in that same category - a strong showing, though not an outright #1 sweep, and Google's own positioning leans on that best-in-class editing framing. On the hands-on side, the picture is messier and splits by who is testing and how. Developer commentary flagged that the cheap 360p draft mode is undercut by the model's non-determinism: because generation isn't deterministic, a prompt approved at a fast, cheap draft resolution can come out meaningfully different when re-run at full resolution, which erodes the point of drafting cheap before paying for a final render ^[6]. Independent hands-on testing that compared Omni 1.1 Flash against rival models Seedance 2.0 Mini and VO3.1 Fast found simple, localized edits were nearly flawless, but more ambitious edits - identity swaps, multi-era timelapses - still produced visible warping and inconsistent identity, framing Omni 1.1 Flash as a lightweight tier ahead of an expected flagship 'Omni' model rather than a do-anything tool. That skeptical, comparative style of coverage sits alongside a separate strand of YouTube content built as polished, agency-style tutorials showcasing creative workflows like native audio-sync generation and conversational iterative editing (for example, prompting the model to 'make it golden hour instead of night') - the split between produced how-to content and independent stress-testing is itself a signal that the model's real-world reliability is still being worked out in public. A third friction point came from real-world users on Reddit hitting overzealous safety-filter rejections on legitimate, non-graphic prompts (one commenter reported a 'two people fighting in space' clip rejected as misrepresenting current events), with some of them saying they preferred a different model, Minimax H3, for production work instead. None of this contradicts the benchmark wins, but it does mean the model's headline capabilities and its day-to-day reliability for complex or sensitive edits are being reported very differently depending on who is doing the testing.

Pricing is tiered strictly by resolution: $0.03 per second for 360p drafts, $0.10 for 720p, $0.15 for 1080p, and $0.30 for 4K ^[7], with the 720p rate deliberately matched to Google's separate Veo 3.1 Fast editing price so developers aren't penalized for choosing Omni over Veo for editing tasks ^[7]. That per-second structure sits on top of the original Omni Flash's token-based rate card - roughly $1.50 per million input tokens and $17.50 per million video output tokens, which works out to about $0.10 per second of 720p video at roughly 5,792 tokens per second ^[3]. On safety, DeepMind's model card confirms SynthID watermarking is applied to every output and that the model's ability to alter a person's recorded speech is currently restricted, a direct acknowledgment of deepfake-adjacent misuse risk in a model this capable of controllable, continuous video ^[8]. Put the pricing, the partner integrations, and the restriction together and the emerging shape isn't 'one video model that does everything' - it's a cheap, fast, controllable video layer that Google expects to be chained with its other generative models and slotted into other companies' creative tools rather than to stand alone as a single flagship product.