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Gemini Omni 1.1 Flash: Features, Pricing, Availability, and Production Workflow
Released as a generally available Gemini API model on August 27, 2026, Gemini Omni 1.1 Flash is best understood as a controllability upgrade rather than a proven universal quality leader: it extends scenes in 10 secon... Google lists video output at $17.50 per million video tokens—about $0.10 per second at the docum...
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Key takeaways
- Released as a generally available Gemini API model on August 27, 2026, Gemini Omni 1.1 Flash is best understood as a controllability upgrade rather than a proven universal quality leader: it extends scenes in 10 secon...
- Google lists video output at $17.50 per million video tokens—about $0.10 per second at the documented 5,792 token rate—with 360p output using fewer tokens; actual project costs also depend on inputs, text output, reso...
- The model can make AI video workflows more iterative and predictable, but it still requires conventional editing, rights review, sound work, and quality control—and there is not yet a representative independent benchm...
AI Prompt
openai.comCreate a landscape editorial hero image for this Studio Global article: What is Google’s Gemini Omni 1.1 Flash video model, and how do its new capabilities—including scene extension using up to 10 seconds of prio. Article summary: Gemini Omni 1.1 Flash is Google’s fast, multimodal video-generation and editing model. Its significance for professional teams is less “one-click finished film” than a more controllable production loop: draft cheaply, di. Topic tags: general, general web, documentation, user generated. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks,
Gemini Omni 1.1 Flash is Google’s fast, multimodal model for generating and editing video. Its most important change is not a claim that every frame is better than competing systems. It is a shift toward a more directed production loop: create inexpensive drafts, preserve more context when extending a shot, define how a transition should begin and end, then render a higher-resolution version for delivery. 4
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What Gemini Omni 1.1 Flash does
The model accepts text, image, and video inputs and produces video output. Google’s API documentation lists 3–10-second outputs at 24 frames per second, with support for 360p, 720p, 1080p, and 4K output options. Video inputs used for editing and extension can be up to 10 seconds in the API documentation. 1
That makes Omni 1.1 Flash more relevant to editors, motion designers, agencies, and product teams than a purely prompt-based video generator. The model can work from approved visual material and can be incorporated into a repeatable process rather than treated as a one-off creative experiment.
The four changes that matter most for production
1. Scene extension uses more prior context
When extending footage, Gemini Omni 1.1 Flash can analyze up to 10 seconds of preceding video context. Earlier Omni material described a much narrower context window, so the new approach gives the model more information about the subject, lighting, camera movement, and action before it generates the continuation. 5
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Extensions are generated in 10-second increments and can reach a cumulative 40-second clip length. A single generation remains limited to 3–10 seconds, so the feature is best understood as staged continuation—not as a single prompt that produces an unrestricted long-form scene. 1
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For a production team, that distinction matters. A director can approve a short take, continue it, and branch from that point while keeping the workflow organized around manageable shot-length outputs. The longer context may reduce continuity drift, but it does not guarantee perfect character, prop, lighting, or motion consistency.
2. First-and-last-frame control directs the motion
The model can generate video between two supplied images: a starting frame and an ending frame. Google’s API changelog describes this as interpolation through the image-to-video workflow. 33
This gives teams a clearer way to specify a shot’s visual destination. For example, an editor can provide a product’s opening composition and its required reveal position, then ask the model to create the movement between them. Similar workflows could support camera-move bridges, loops, and transitions, although the quality and reliability of each result still need to be tested against the actual shot.
The practical advantage is control. Prompt-only generation asks the model to infer both the beginning and the end of a shot. Endpoint frames let the team lock those two creative decisions before asking the model to fill in the motion.
3. 360p creates a cheaper iteration stage
Google says 360p previews can render up to 60% faster and cost one-third as much as standard 720p output. 5
That creates a useful cost ladder for storyboarding and approval:
- Generate several 360p versions to test prompts, timing, framing, and motion.
- Select the strongest take and refine its references or endpoint frames.
- Render the approved version at a higher resolution.
- Complete compositing, color, sound, rights review, and quality control outside the generation step.
Google’s pricing documentation lists 1,931 output tokens per second for 360p video and 5,792 tokens per second for 720p video. At the published $17.50-per-million-token video-output rate, that works out to roughly $0.034 per second for 360p and $0.101 per second for 720p, before input and text-output charges. 41
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The exact bill is therefore not simply a flat per-clip price. Input media, text responses, resolution, duration, and the platform used can all affect the final amount.
4. Higher-resolution delivery is part of the same workflow
The API supports 1080p and 4K options alongside 360p and 720p. Google’s launch materials position the higher-resolution modes, including 4K, as a way to finish selected work after lower-cost experimentation. 1
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That is useful for teams that need to separate creative iteration from delivery rendering. It also prevents a common workflow mistake: spending high-resolution generation budget on ideas that have not yet passed storyboard or motion approval.
How reference video changes the workflow
Omni 1.1 Flash is designed for multimodal input, so a team can combine written direction with visual references. Depending on the task, those references may include style frames, product imagery, an existing plate, or a short movement example. 1
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A reference-led process is generally easier to review than a prose-only process because the creative brief contains visible anchors. Before generation, teams can decide which elements must remain fixed—such as a product design, logo treatment, character appearance, or opening composition—and which elements can be generated.
Reference video should not be treated as a substitute for permissions or a complete continuity system. Teams still need to check whether they have the right to use the supplied footage, whether generated results preserve protected brand elements correctly, and whether the output meets the project’s editorial and legal requirements.
Pricing and access
Google’s Gemini API pricing lists Gemini Omni 1.1 Flash at $1.50 per million input tokens, $9 per million text-output tokens, and $17.50 per million video-output tokens. Google documents video billing at 5,792 tokens per second for 720p output; that is approximately $0.10 per generated second at that rate, excluding other applicable charges. 41
The API changelog identifies gemini-omni-1.1-flash as the generally available model for developers on the paid Gemini API tier. 33
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Availability is not identical across every Google product. Google’s consumer announcements place Gemini Omni in the Gemini app and Google Flow for Google AI Plus, Pro, and Ultra subscribers, while Google’s Enterprise Agent Platform release notes list Gemini Omni 1.1 Flash in Public Preview there. Teams should verify the current model ID, region, quota, billing status, and product-specific availability before committing a production pipeline. 20
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Is it integrated with Adobe Firefly or Figma?
The supplied evidence does not establish a sufficiently clear, official announcement for a direct Figma integration, so that claim should not be treated as verified. It is safer to distinguish between confirmed model access through Google’s own developer and creative surfaces and third-party reports about integrations or customer use.
For professional buyers, the more important question is not whether a brand appears in a launch story. It is whether the model is available inside the team’s actual review, asset-management, editing, and approval process, with stable APIs and predictable billing.
Gemini Omni 1.1 Flash vs. OpenAI Sora
Gemini Omni 1.1 Flash and OpenAI Sora should not be compared through a single headline feature. The available material supports a narrower conclusion: Google is competing more aggressively in professional AI video by emphasizing scene continuation, endpoint control, multimodal references, rapid drafts, and API-based iteration. 4
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There is not enough independent, representative evidence in the supplied sources to claim that Omni 1.1 Flash consistently produces more realistic images, better physics, or stronger continuity than Sora. The sources also do not provide a unified benchmark that makes the two systems directly interchangeable.
A serious comparison should use the same prompts, references, shot lengths, output resolutions, and acceptance criteria. Measure:
- continuity of characters, products, lighting, and camera movement;
- success rate for first-and-last-frame transitions;
- render latency at draft and delivery resolutions;
- cost per approved second rather than cost per generated attempt;
- audio and dialogue requirements;
- safety, rights, and data-handling policies; and
- how easily the model fits the team’s existing editing and review tools.
A practical production workflow
A sensible Omni 1.1 Flash workflow is:
- Prepare approved style frames, product images, plates, or movement references.
- Generate multiple 360p drafts to test direction, framing, timing, and motion.
- Choose the strongest take and define its required opening and closing frames where endpoint control is useful.
- Extend the selected shot in 10-second stages, checking continuity after each generation.
- Render the approved sequence at 1080p or 4K when the delivery requirement justifies it.
- Finish compositing, color, sound, captions, rights review, and quality control in conventional production tools.
The model can reduce the cost of experimentation and the amount of manual work needed for bridging shots. It does not eliminate the editorial decisions that determine whether a video is coherent, legally usable, technically compliant, or ready for publication.
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Released as a generally available Gemini API model on August 27, 2026, Gemini Omni 1.1 Flash is best understood as a controllability upgrade rather than a proven universal quality leader: it extends scenes in 10 secon...
What are the key points to validate first?
Released as a generally available Gemini API model on August 27, 2026, Gemini Omni 1.1 Flash is best understood as a controllability upgrade rather than a proven universal quality leader: it extends scenes in 10 secon... Google lists video output at $17.50 per million video tokens—about $0.10 per second at the documented 5,792 token rate—with 360p output using fewer tokens; actual project costs also depend on inputs, text output, reso...
What should I do next in practice?
The model can make AI video workflows more iterative and predictable, but it still requires conventional editing, rights review, sound work, and quality control—and there is not yet a representative independent benchm...
Sources
developersdigest.techGemini Omni 1.1 Flash Goes GA: Scene Extension, Keyframe Control, and 4K in the Gemini API
blog.googleGemini Omni 1.1 Flash lets you build with more control
ai.google.devGemini API changelog - Google AI for Developers