Nano Banana 2 - Free AI Image Generator & Editor

Try Nano Banana 2 (Google Gemini 3.1 Flash) free - generate & edit AI images in seconds. 512px to 4K resolution, web search grounding, 14 reference images. No signup, no watermark. Start with 5 free credits.

Mar 4, 2026

Nano Banana 2 is the public name used for Gemini 3.1 Flash Image Preview. If you want the short version: on February 26, 2026, Google listed Nano Banana 2 in the Gemini API release notes as a high-efficiency image model for speed and high-volume use.

If you came here from older "nano banana" pages, start with this page first, then go deeper:

Model overview and naming history: Nano Banana
Prompt library for practical use: Nano Banana Prompts
Generate right now: AI Image Generator

Disclaimer: Nano Bannana is an independent service and is not affiliated with Google or Google DeepMind.

What is Nano Banana 2?

Nano Banana 2 refers to Gemini 3.1 Flash Image Preview in Google AI Studio and Gemini API docs.

Official model code: gemini-3.1-flash-image-preview
Positioning: high-efficiency image generation and conversational editing
Focus: low latency, high-volume use cases, practical quality

For developer use, this is the model ID you pass in API calls.

Release timeline (clear dates)

February 26, 2026: Gemini API release notes announced Nano Banana 2 (Gemini 3.1 Flash Image Preview).
March 3, 2026: Gemini 3.1 Flash-Lite Preview launched; Nano Banana 2 remains the image model track.

This is important because many pages still mix old and new model names without dates.

Nano Banana 2 vs previous Nano Banana model

Nano Banana (previous commonly referenced model) usually maps to Gemini 2.5 Flash Image. Nano Banana 2 maps to Gemini 3.1 Flash Image Preview.

Area	Nano Banana (older)	Nano Banana 2
API model family	Gemini 2.5 image track	Gemini 3.1 image track
Model code	`gemini-2.5-flash-image`	`gemini-3.1-flash-image-preview`
Resolution options	baseline options	adds 0.5K, 2K, 4K (default 1K)
Search grounding	basic workflows	image and text search grounding support
Aspect ratio options	standard	adds 1:4, 4:1, 1:8, 8:1
Intended use	fast image generation/editing	higher efficiency for scale and speed

What changed in Nano Banana 2 (practical take)

From the official model page and docs, the useful upgrades for real workflows are:

More output resolution control, including 2K and 4K when you need cleaner assets.
Better support for extreme aspect ratios, useful for modern ad placements.
Stronger consistency and quality behavior in repeat generation cycles.
Better multilingual text rendering for image text use cases.
Search grounding support to improve factual visual prompts.

If your team creates campaign variants, these changes matter more than "benchmark" claims.

Prompt templates for Nano Banana 2

Copy, replace bracket values, and keep everything else stable.

1) Product hero for landing page

"Studio photo of [PRODUCT], centered, clean [BACKGROUND COLOR], soft diffused light, subtle shadow, premium commercial look, no text, no watermark, leave 30% copy-safe space on the right."

2) Three ad variants with controlled change

"Create 3 ad image variants for [PRODUCT]. Keep lighting, palette, and composition stable. Change only [ONE VARIABLE] across variants. Clean background, no text."

3) Background swap with subject lock

"Edit uploaded image: keep the subject exactly the same, replace background with [NEW BACKGROUND], match light direction and shadows, realistic integration, no text."

4) Editorial visual with stable style

"Editorial image for [TOPIC], cinematic light, clean composition, high detail, consistent style, no text, no logos."

5) 9:16 short video cover image

"Vertical 9:16 hero image for [TOPIC], strong focal point, high contrast, clean copy-safe zone at top, no text."

6) Isometric visual for feature section

"Clean isometric scene of [SCENE], modern colors, sharp geometry, soft shadows, high clarity, no text."

A repeatable workflow for teams

Define one use case per batch (for example: landing hero, ad variants, product PDP).
Lock base style prompt and constraints first.
Generate a small preview set (3 to 6 outputs), then pick one anchor image.
Iterate by changing one variable only.
QA for artifacts, brand fit, and copy-safe layout.
Save winning prompt templates in your shared library.

If you skip steps 2 and 4, consistency usually breaks.

Common mistakes to avoid

Mixing model names without dates in the same page.
Treating this as a one-shot prompt tool with no template reuse.
Writing plain /path text instead of real internal links.
Publishing without a clear "what changed" section versus prior model.