Google’s Gemini Omni dropped at I/O 2026, and it’s a different kind of video AI. Not just a generator. Not just an editor. It’s a model that can do both, in the same conversation, using whatever input you throw at it.
This guide covers how to actually get good results out of it. What prompts work. How to use reference inputs. How the multi-turn editing flow behaves. And what’s worth your time depending on which tier you’re on.
What Gemini Omni actually is
Omni is Google’s multimodal creative model. It takes text, images, audio, and video as input and generates video output. The key differentiator from something like Veo 3 is that Omni reasons about what it creates. It understands physics, history, cultural context, and how scenes should flow from one moment to the next.
I reviewed Veo 3 last year and the biggest problem was consistency. Characters shifted. Physics broke. Every generation felt like a dice roll. Omni is built differently – each edit remembers what came before, and the underlying world model keeps things grounded.
The model available now is Gemini Omni Flash. It’s rolling out to the Gemini app, Google Flow, and YouTube Shorts.
Where you can use it
Gemini app – Available to Google AI Plus, Pro, and Ultra subscribers. This is where you get the full conversational editing experience. Generate a video, edit it with natural language, add references, iterate.
Google Flow – Same underlying model, but in Flow’s agentic environment. Better for multi-step creative workflows where you want to chain generations together with other tools.
YouTube Shorts and YouTube Create App – Free tier access. Limited compared to the app experience, but if you’re a creator making Shorts content, you can use Omni directly in your existing workflow. No subscription needed.
API access – Coming in the weeks after launch for developers and enterprise customers.
How to prompt for video generation
The biggest shift from text-to-image tools is that video has time. A good prompt for an image might work in one frame but break across sixty. Here’s what makes a difference.
Be specific about physics. Omni has improved understanding of gravity, kinetic energy, and fluid dynamics. Tell it what should happen. “A marble rolling fast on a chain reaction style track, continuous smooth shot” works better than “a marble on a track.” The model needs to know the motion matters.
Name a style and a duration. “Claymation explainer of protein folding, everything is made out of clay, no hands, stop motion, accurate” is a real example from Google’s demos that works. The style word (claymation) plus the constraint (no hands, stop motion) plus the subject (protein folding) gives the model multiple anchors.
Describe the camera. “Continuous smooth shot.” “Over the shoulder.” “Front-facing full-body walk cycle.” Omni responds to camera language. If you want editorial control, treat the prompt like a shot list.
Use negatives. “No hands. No text on objects. No cartoon elements.” The model respects explicit rejections in the prompt.
Using input references
This is Omni’s strongest feature. You can feed it multiple types of reference material and it blends them into a single output.
Image references – Use an image to define style, character appearance, or scene composition. “Turn this into realistic footage, using the drawing only as a guide for movement, do not show the drawing in the final video” is a real prompt that works. The model extracts the structure from the reference without copying it literally.
Video references – Use a video clip to transfer motion. “Apply the motion of the whale swimming from the provided video to the provided image of fluid reflective material.” Omni extracts the motion pattern and applies it to the new subject.
Audio references – Voice and music references are supported. “Add harp sounds synchronized to when I touch each fern leaf.” The audio syncs to the visual timeline.
The best results come from combining references. A style image plus a motion video plus a descriptive text prompt produces more coherent output than any single input type alone.
The multi-turn editing flow
This is where Omni separates from every other video AI I’ve seen. You don’t generate once and accept what you get. You keep editing.
Start with a base prompt. Something simple. “A video of a violinist playing a song.” Get the generation. Then edit. “Transport the violinist to the image environment.” Then edit again. “Make the violin invisible.” Then again. “Change the camera angle to be over the violinist’s shoulder.”
Each edit builds on the last. The violinist stays the same person. The environment remembers the previous change. The camera angle shifts without breaking the scene.
A few things that help the multi-turn flow:
Keep edits additive, not resetting. “Add animated motion effects coming out of the skateboard” preserves what’s there and layers on top. “Replace the skateboard with a hoverboard” resets more context. Additive edits are more reliable.
Stay in the same conversation. Omni tracks context within a session. If you start a new conversation, you lose the character and scene memory. Everything in one thread.
Confirm what you want to keep before changing something else. The model works best when only one variable changes per turn. Get the character right first, then the environment, then the lighting, then the camera.
Best use cases by access tier
YouTube Shorts (free) – Quick clips, style transfers, environment changes. Good for experimenting, limited for iterative editing. If you’re testing whether Omni works for your content, start here.
Gemini app (Plus/Pro/Ultra) – Full conversational editing. This is where multi-turn really shines. Pro and Ultra tiers get higher generation caps and faster processing. If you’re making anything longer than a few seconds, use the app.
Google Flow – Complex multi-step workflows. Chain Omni generations with other tools. Better for production pipelines than one-off creations.
What it can’t do yet
Image output isn’t supported yet (Omni generates video only), though Google says image and audio output are coming. Voice editing for changing what someone says in a video remains limited pending safety testing. Audio input references are limited to voice for now, with broader audio support rolling out.
Every generation includes SynthID watermarking, which is imperceptible but verifiable through the Gemini app, Chrome, and Google Search. That’s a good thing for transparency, but worth knowing if you’re evaluating output quality.
Bottom line
Gemini Omni is the first video AI model that treats generation and editing as the same thing. You don’t generate in one tool and edit in another. You do both in the same chat, with the same model, using whatever reference material you have.
The best results come from specific prompts, combined references, and additive multi-turn editing. Start simple, refine in layers, and stay in one conversation.
