Gemini Omni Flash: A Complete Guide to AI Video Generation and Editing

Gemini Omni Flash (gemini-omni-1.1-flash) is a high-performance multimodal model built for high-speed video generation, editing, and cinematic control. Accessible through the Gemini API, it lets developers turn text prompts and reference images into video with audio, and refine those videos through natural language. This guide walks through the model's core capabilities and shows how to generate and customize video in several programming languages.

Gemini Omni differs from earlier video models in three key ways. First, it is natively multimodal: it processes text, images, audio, and video at the same time, producing more cohesive, consistent, and controllable output. Second, it supports conversational editing through the Interactions API. You describe the change you want, and the model applies it while preserving the parts of the video you want to keep. Third, it has world knowledge. The model combines an understanding of physics with knowledge of history, science, and cultural context, helping move video from photorealism toward meaningful storytelling.

Text-to-video generation takes a text prompt and returns a video with audio. For the best results, include details such as the scene description, camera movement, lighting, and mood. For example, a prompt describing a marble rolling down a chain-reaction track with a continuous, smooth shot gives the model clear direction. The model returns base64-encoded video data, which you decode and write to an MP4 file. Python, JavaScript, Java, and Go examples all follow the same pattern: create an interaction with the model and input, then save the decoded `output_video` data.

The output aspect ratio is set through the `aspect_ratio` field in `response_format`. Landscape at 16:9 is the default. To create portrait videos for mobile-first platforms, set `aspect_ratio` to "9:16". The example below requests a cyberpunk city scene in portrait orientation. Only "9:16" and "16:9" are supported values.

The `resolution` parameter in `response_format` controls output quality. The available values are 360p, 720p (the default), 1080p (upscaled), and 4k (upscaled). Higher resolutions produce sharper footage but may require more processing. For example, a drone shot of a mountain landscape at sunrise can be requested at 1080p for a high-quality result.

Gemini Omni can also use a reference image alongside a text prompt, which is useful for bringing product shots, illustrations, or photographs to life. The model decides how to use the image based on your instructions. For instance, a drawing of a fish leaping out of water can serve as a movement guide, with a prompt asking the model to turn it into realistic footage without showing the drawing in the final video. Include the image as base64-encoded data with its MIME type, followed by the text instruction in the same request.

The `interaction.output_video` field is a convenience available only in the SDKs. When calling the REST API directly, retrieve the video from the `steps` array. Look for the step of type `model_output`, which contains a content item of type `video` with `mime_type` set to "video/mp4" and base64-encoded `data`. The response also includes the interaction `id`, `status`, `model`, and `object` fields, which are useful for tracking and debugging requests.

To get strong results from Gemini Omni Flash, write detailed prompts that cover the scene, camera movement, lighting, and mood. Choose aspect ratio and resolution to match your target platform and quality needs. Use reference images when you need a specific visual starting point, and use conversational editing to refine a video step by step. With these building blocks, developers can add AI-driven video generation to creative tools, marketing pipelines, and storytelling applications.