Nano Banana 2 model guide
A practical guide to Nano Banana 2, the Gemini 3.1 Flash Image Preview model exposed in GPT Image Hub for 1K, 2K, and 4K generation with larger reference workflows.
Nano Banana 2 is the balanced Gemini choice on GPT Image Hub. It supports 1K, 2K, and 4K tiers in the app, keeps the fast Flash-style workflow, and allows up to 14 reference images. Choose it when Nano Banana is too limited but Nano Banana Pro is more than the task needs.
Visual examples
Visual examples
A polished, bright environment suitable for travel or SaaS campaigns.
Isometric travel scene
A polished, bright environment suitable for travel or SaaS campaigns.
2K isometric pool scene, turquoise water, tiny lounge chairs, no text.Brand board output
Four product lifestyle concepts aligned to one reference system.
Use references as a brand board; keep citrus palette and soft shadows.Resolution ladder
A clean path from draft to review to final export.
Generate 1K preview, 2K review, then 4K final campaign hero.Best use cases
- Campaign concept sets, UI mockup imagery, creator thumbnails, and product lifestyle scenes.
- Reference-heavy edits where 3 input images are not enough.
- Draft-to-final workflows where the same prompt needs 1K previews and 4K exports.
- Gemini-centered prompt experiments that need more detail than Nano Banana.
Where to be careful
- Tasks that need the strongest Gemini reasoning and text rendering; use Nano Banana Pro for that.
- Single quick drafts where Nano Banana is enough.
- OpenAI-specific workflows that require GPT Image 2 behavior or many non-Gemini ratios.
Strengths
Balanced model for higher-quality Gemini image generation without jumping straight to the Pro image model.
Supports 1K, 2K, and 4K tiers in GPT Image Hub.
Allows up to 14 reference images, enough for style boards, product angle sets, and visual direction packs.
Good fit for repeatable prompt templates that need a faster production loop.
Use cases
Balanced production drafts
A strong middle path for 1K reviews, 2K approvals, and 4K delivery.
Reference-rich scenes
Use larger visual boards without moving immediately to a Pro model.
Template repeatability
Run the same prompt structure across products, campaigns, or categories.
Workflow
Preview at 1K
Move fast while testing layout and campaign direction.
Review at 2K
Inspect detail, style consistency, and visual hierarchy.
Deliver at 4K
Use 4K when the prompt has already proven the right direction.
Prompting guidance
- Use explicit output intent: concept draft, final hero, edit, style transfer, or variant set.
- Separate reference-image roles: subject reference, style reference, material reference, layout reference.
- Ask for consistent camera and lighting when generating several assets for the same campaign.
- Use 2K as the review tier when 1K hides too much detail but 4K is not yet needed.
Copy-ready prompts
Create a 2K isometric pool scene for a travel booking app, bright ceramic tiles, turquoise water, tiny lounge chairs, clean ad-ready composition, no text.
Use the references as a brand board. Generate four matching product lifestyle concepts for a reusable bottle, citrus palette, morning kitchen counter, consistent soft shadows.
Generate a 4K landing page hero visual for an AI prompt library, tiled prompt cards floating around a central image canvas, polished SaaS style, dark background, orange accent.
Visual examples
Official sources
FAQ
How is Nano Banana 2 different from Nano Banana?
In GPT Image Hub, Nano Banana 2 adds 2K and 4K quality tiers and raises the reference-image limit from 3 to 14, making it better for serious production drafts.
Is Nano Banana 2 a Pro model?
No. Nano Banana 2 maps to Gemini 3.1 Flash Image Preview in this app. Use Nano Banana Pro when you specifically want the Gemini 3 Pro Image path.
What quality should I start with?
Start with 1K for composition, move to 2K for review, and reserve 4K for final output or close-up detail checks.
Use this model
Start from a prompt template or compare models first
Use the generator when you already know the model, or compare all supported models when the prompt needs a different balance of speed, detail, and reference inputs.