What Gemini Omni and 3.5 Are, In Plain Language
Gemini Omni and the Gemini 3.5 family are multimodal AI models announced at Google I/O 2026 that accept video, audio, images, and text as input and respond with grounded, context-aware output, enabling practical workflows that blend understanding, reasoning, and content creation across media. Gemini Omni focuses on creating and transforming rich media, starting from video, while Gemini 3.5 combines frontier intelligence with action for more capable agents. Together they form a toolkit that can understand complex scenes, carry context across long tasks, and connect different formats in a single conversation. Instead of being limited to static prompts, these multimodal AI models keep track of what happened, what changed, and what you ask for next, so users can move from idea to video, plan, or code with fewer steps.
Conversational Video Editing With Gemini Omni
One of the clearest Gemini Omni capabilities appears in its conversational video editing demo. You start with a recorded clip, then talk to the model as if you were directing a reshoot on the fly. Every instruction builds on the last: characters remain consistent, physics stay believable, and the scene remembers earlier changes. That means you can ask Omni to change a single object, shift lighting, or rebuild an entire environment while the video still feels like one continuous world. Because Omni combines images, audio, video, and text as input, it can align dialogue, visuals, and motion in the same session. The result is a editing process that feels closer to conversation than software menus, turning raw footage into a flexible starting point rather than a fixed recording.
From Any Input to New Video: Multimodal Creation
Beyond editing, Gemini Omni is designed to create new, high-quality videos grounded in its real-world knowledge. You can combine images, audio tracks, reference clips, and text descriptions as input, then ask Omni to build scenes that connect them. It can keep character designs coherent, maintain camera angles across shots, and preserve visual continuity as you refine your brief. According to Google’s Gemini blog, Omni is “our new model that can create anything from any input, starting with video.” In practice, that translates to workflows like turning rough storyboard sketches and a narrated outline into animated sequences, or expanding a short live-action shot into a longer scene that still fits the original style. For creatives and product teams, it reduces the gap between idea, prototype, and polished visual output.
Gemini 3.5 Flash: Frontier Intelligence for Agents
Gemini 3.5 introduces a family of models tuned for intelligent agents, with the first release branded as 3.5 Flash. These Gemini 3.5 features focus on complex, long-horizon tasks: the kinds of jobs where an AI must plan, take several steps, and keep track of progress over time. The model combines frontier-level reasoning with practical action, which makes it suitable for workflows like multi-step research, structured content creation, or orchestrating tools in sequence. Google describes 3.5 Flash as delivering “frontier performance for agents and coding, excelling at complex long-horizon tasks that deliver real-world utility.” In real terms, that might mean an assistant that understands a long brief, drafts code or documents, revises them based on feedback, and coordinates multiple subtasks without losing context.
Nine Demos, New Workflows: What These Models Enable Now
The nine video demonstrations of Gemini Omni and Gemini 3.5 show how multimodal AI models move beyond isolated prompts into connected workflows. Omni’s video-first design highlights cross-modal understanding: it can listen to your instructions, watch what is happening in a clip, and adjust visuals while keeping story logic intact. Gemini 3.5 Flash, meanwhile, displays how improved reasoning and persistence turn models into agents that can handle longer, more complex jobs instead of single-turn answers. Together, the demos hint at new categories of AI-assisted work: iterative creative direction, continuous software agents, and mixed-media collaboration where text, images, audio, and video stay in sync. For teams experimenting with AI, these models turn marketing claims about multimodal understanding into concrete, observable behavior in real-world tasks.