Gemini Flash Omni: Google's Multimodal Image and Video Generation Explained
Wanderson Jackson
Updated July 2026. 8-min read. Gemini Flash is Google DeepMind's unified model that handles text, images, audio, and video in a single system. Here is what "omni" means, how image generation works, and when to use it over specialist models.
Gemini Flash is Google DeepMind's multimodal model family. The "Omni" label refers to the fact that a single model natively handles text understanding, image generation, audio processing, video analysis, and code generation. It is not separate specialist models bolted together. One model, multiple modalities.
For image generation specifically, the current workhorse is Gemini 3.1 Flash Image (model ID: gemini-3.1-flash-image), available as "Nano Banana 2" in Google's naming convention. The premium tier is Gemini 3 Pro Image (gemini-3-pro-image), known as "Nano Banana Pro."
These models are available through the Google AI API, and are also accessible on Avocado AI as nano-banana-2 (1 credit) and nano-banana-pro (2 credits).
What "Omni" Actually Means
In practice, "omni" means the model can:
Understand images you upload (analyze content, read text, describe scenes)
Generate images from text (text-to-image)
Edit images conversationally (upload an image, ask for changes in natural language)
Handle multiple reference images (up to 14 reference images per generation)
Ground generation in real-time data (Google Search integration can verify facts before generating)
Reason about visual content (configurable "thinking" mode that processes the prompt logic before generating)
The practical advantage: you can have a conversation about images. Upload a product photo, ask Gemini to change the background to a beach scene, then ask it to adjust the lighting to golden hour. Each edit happens conversationally without regenerating from scratch.
Key Capabilities for Image Generation
Generation modes:
Text-to-image: describe what you want in natural language
Image-to-image editing: upload an image and ask for specific changes
Multi-turn conversational editing: iterate through a conversation
Video-to-image: generate images from video context (YouTube URLs or uploaded video)
Reference images:
Nano Banana 2: up to 14 references (10 objects + 4 characters)
Nano Banana Pro: up to 14 references (6 objects + 5 characters + 3 style references)
Resolutions:
Flash: 0.5K, 1K, 2K, 4K
Flash Lite: 1K only
Pro: 1K, 2K, 4K
Other features:
Text rendering in generated images (infographics, menus, diagrams)
Interleaved text and images (generate stories with text blocks and illustrations)
SynthID watermark on all generated images (cannot be removed)
Content safety filters
The Gemini Image Model Family
Model
ID
Best For
Resolution
Nano Banana 2 Lite
gemini-3.1-flash-lite-image
Speed, cost, simple tasks
1K only
Nano Banana 2
gemini-3.1-flash-image
Generalist workhorse
Up to 4K
Nano Banana Pro
gemini-3-pro-image
Complex tasks, brand consistency
1K/2K/4K
On Avocado AI:
nano-banana-2 (1 credit): the default, fast and good for most tasks
nano-banana-pro (2 credits): premium quality for complex visual work
The Lite variant is the cheapest and fastest but limited to 1K resolution, no multi-reference support, no search grounding, and no multi-turn editing.
Prompt Engineering Guide
General approach
Gemini Flash responds best to natural language descriptions. You do not need to use keyword-heavy prompt styles. Describe what you want as if you are briefing a photographer or designer.
Photorealistic images:
A ceramic coffee mug on a wooden desk by a window. Morning sunlight
creates soft shadows. The laptop screen shows a blurred code editor.
Shallow depth of field, warm color temperature, casual lifestyle photography.
Product mockup:
A sleek black smartphone lying flat on white marble. The screen displays
a weather app showing sunny conditions. Single overhead soft light.
Minimal product photography style, centered composition, no props.
Infographic with text:
A horizontal infographic for a blog post about renewable energy growth.
Clean flat design, green and white color palette, clear data labels
Modern sans-serif typography
Multi-turn editing
This is where Gemini Flash genuinely excels over specialist models:
Generate: "Create a product photo of wireless headphones on a gradient background"
Refine: "Make the background darker and add a subtle blue tint"
Adjust: "Move the headphones slightly to the left and add a reflection below"
Finalize: "Convert this to a square crop and add the text 'SHURE AONIC 50' at the bottom"
Each step builds on the previous output. The model maintains context across turns.
Tips for better output
Be specific about text. If you want text in the image, state exactly what it should say, the font style (bold, italic, serif, sans-serif), and the placement.
Mention lighting explicitly. The model responds well to lighting descriptions: "natural daylight," "studio softbox," "dramatic side lighting," "backlit silhouette."
Use the thinking mode for complex prompts. Set the thinking configuration to "high" for prompts with spatial relationships, multiple elements, or text-heavy layouts. This makes the model reason through the layout before generating.
Reference real-world products by name. Gemini can pull real product data through Search grounding, which helps with accuracy for product mockups and marketing imagery.
Conversational editing is the strongest workflow. Instead of trying to get everything perfect in one prompt, generate a base image and refine it through conversation. Each edit maintains the style and composition of the previous version.
Note: Gemini's API pricing is token-based, which means costs vary by resolution and prompt complexity. The costs above are approximate.
Strengths and Trade-offs
Strengths
Conversational image editing. No other model matches Gemini's multi-turn editing workflow. Upload, adjust, refine, and iterate through natural language conversation. This is a fundamentally different workflow from one-shot generation.
True multimodal integration. The same model that generates your image can also write the copy, code the landing page, and analyze your analytics data. For teams that live in Google's ecosystem, this integration is valuable.
Up to 14 reference images. Supporting 10 objects plus 4 characters (or 6 objects + 5 characters + 3 styles on Pro) gives you more reference capacity than most competitors.
Google Search grounding. The model can verify facts and pull real-world data before generating. Useful for marketing content that references real products, people, or events.
Reasoning mode. The configurable "thinking" mode processes prompt logic before generation. This produces better spatial layouts, more accurate text placement, and more coherent multi-element compositions.
Cheap at low resolution. The Lite variant at ~$0.034/image is among the cheapest image generation available.
Trade-offs
SynthID watermark on every image. Google applies a visible or invisible watermark to all Gemini-generated images. This cannot be removed and may limit use in some contexts.
Not a specialist at anything. Gemini is the best generalist, but it loses to specialist models in their domains: Midjourney for artistic quality, Recraft for logos and vectors, Seedream for multilingual text, Seedance/Happy Horse for video generation.
Token-based pricing is confusing. Unlike flat per-image pricing, Gemini charges by input and output tokens. The actual cost depends on resolution, prompt length, and image complexity, making it harder to budget.
Strict content filters. Google's safety filters are among the most restrictive in the industry. Some creative or portrait-focused prompts are blocked.
1K cap on the Lite model. For anything above 1K resolution, you need Flash or Pro, which cost more.
Flash Image quality at 4K has limits. While resolution scales up to 4K, the quality of fine details at 4K does not match dedicated image models at 2K.
How It Compares
vs. Recraft v4.1:
Gemini is a generalist multimodal model with image generation as one feature. Recraft is purpose-built for design. Recraft is better for logos, vectors, brand consistency, and typography. Gemini is better for conversational editing, multi-modal workflows, and when you need image generation integrated with reasoning and search.
vs. GPT Image 2:
Both are multimodal models from major AI labs, but the workflows differ. Gemini offers conversational editing natively (chat with the model about the image). GPT Image tends toward stronger English typography. Gemini's multi-reference capability (up to 14 images) is larger. For general image generation, they are close competitors.
vs. Seedream 5.0 Pro:
Seedream is stronger on multilingual text rendering (15 languages vs Gemini's more limited text support), layer separation, and reference-based generation. Gemini has the conversational editing advantage and Google ecosystem integration. For multilingual marketing: Seedream. For iterative editing workflows: Gemini.
vs. Midjourney:
Midjourney still leads on pure aesthetic quality and artistic exploration. Gemini is more versatile, cheaper, and better integrated into productivity workflows. For art and creative direction: Midjourney. For pragmatic image generation in an existing workflow: Gemini.
FAQ
What does "Nano Banana" mean?
Nano Banana is Google's internal naming for Gemini's image generation capability. "Nano Banana 2" corresponds to Gemini 3.1 Flash Image. "Nano Banana Pro" corresponds to Gemini 3 Pro Image. The names are used in API model IDs and Avocado AI's model list.
Can I remove the SynthID watermark?
No. All images generated by Gemini include a SynthID watermark. This is either visible (a small mark) or encoded invisibly into the pixels. It is a permanent part of the output.
What is the difference between Imagen 4 and Gemini Image?
Imagen 4 is Google's standalone image generation model (text-to-image only, simpler, cheaper). Gemini Image is the multimodal model that generates images as one of many capabilities (includes conversational editing, multi-reference, video-to-image, and search grounding).
How many reference images can I use?
Up to 14 per generation. The allocation differs by model: Nano Banana 2 supports 10 objects + 4 characters. Pro supports 6 objects + 5 characters + 3 style references.
Is it available on Avocado AI?
Yes. nano-banana-2 at 1 credit and nano-banana-pro at 2 credits per image.
Can Gemini generate video?
Not through the image generation API. Video analysis and understanding are supported, but video generation requires other models. On Avocado AI, you would use Seedance 2.0, Happy Horse, Kling 3, or Sora 2 for video generation.
Try Gemini Flash on Avocado AI as Nano Banana 2 at 1 credit per image, alongside 14 other image models in one workspace.
