Nano Banana 2 Explained (2026)

What is Nano Banana 2?

Nano Banana 2 is the public nickname for Gemini 3.1 Flash Image, Google DeepMind’s fast image generation and editing model released on February 26, 2026. It is an image-only model — not video — with one capability that sets it apart: identity preservation across multiple images. You can lock a character’s face, a product’s appearance, or a brand’s visual elements, then change everything else around them (clothing, lighting, art style, setting) across a sequence of images.

The model handles up to 5 characters and 14 objects simultaneously in the identity-locked set. That is the highest number any public image model offers as of May 2026. Google DeepMind built it on top of the Gemini 3.1 model family, which is why the technical name carries the Gemini prefix.

Nano Banana 2 is a closed-weights foundation model. It runs inside the Gemini app, the Gemini API, and Vertex AI. There is no downloadable version.

What makes Nano Banana 2 different?

Four capabilities set Nano Banana 2 apart from other image models.

Identity is decoupled from style. Most image models entangle the two — tell them to change a character’s outfit and the face shifts too. Nano Banana 2 separates the identity layer from the style layer. You can take the same character and render them in photoreal, anime, watercolor, line art, and 3D render styles across a sequence, and the face stays the same. For storyboarding, illustrated brand work, and serialized content, this is the single biggest workflow unlock since AI image models became usable.

Real-time web-search grounding. Nano Banana 2 can search the web during generation to render accurate references to real brands, public figures, and architectural locations. Ask for “the Empire State Building viewed from Bryant Park at sunset” and the model renders the building correctly, not a generic skyscraper. This works through Google Search integration, which is something only a Google-built image model can do natively.

Precision text rendering. Text inside generated images has been the chronic failure of image AI — misspellings, deformed letters, font drift. Nano Banana 2 renders text with controllable fonts and sizes, accurately enough for ad mockups, product packaging concepts, and meme work where the words have to be right.

512px to 4K output. The model handles a wide resolution range, from quick thumbnails to print-grade output, in a single product. Earlier image models often required separate workflows or upscalers for different resolutions.

What is the storyboarding workflow?

Google’s launch post explicitly demonstrates Nano Banana 2’s storyboarding use case. The pattern looks like this:

1. Establish identity. Generate or upload an image of the character (or product, or location) you want to maintain.
2. Lock identity in the workflow. Tell Nano Banana 2 this image is the reference.
3. Generate the sequence. Prompt the next panel describing what changes — new pose, new setting, new outfit, new lighting. The face stays the same.
4. Iterate. Each panel can also reference earlier panels for continuity.

The launch-day example Google used was a treehouse build — a sequence of panels showing the same character building a treehouse, with expressions and camera angles varying across panels but identity staying locked. The pattern works the same way for:

• Comics and graphic novels — the same character across panels.
• Advertising shot lists — the same model across an ad campaign.
• Product photography sequences — the same SKU shot from multiple angles, in different settings.
• Educational illustration — the same character demonstrating multiple steps in a tutorial.
• Pre-production storyboards — for film, animation, or game development.

How much does Nano Banana 2 cost?

Pricing splits three ways.

Free via the Gemini app. Nano Banana 2 is available in Gemini’s consumer apps (web, iOS, Android) on the free tier with daily generation quotas. For casual use, this is the entry point.

Google AI Pro — $19.99 per month. Higher generation quotas and faster generation. Includes access to Veo 3.1 for video alongside Nano Banana 2 for images.

Google AI Ultra — $249.99 per month. Maximum quotas. Designed for creator-professionals and small teams.

API access via Gemini API and Vertex AI. Per-token pricing rather than a flat per-image rate. The token cost depends on resolution and prompt complexity. Google does not publish a fixed per-image rate, so expected cost varies. For most production workflows under 1,000 images per day, the AI Pro or Ultra subscription is the better deal than per-token API billing.

How do you actually use Nano Banana 2?

Three entry points, in order of how most people start:

Open the Gemini app and prompt. Type a prompt, get an image. For one-off generation and quick testing, this is the simplest path. The identity-lock workflow takes a bit of practice in the chat interface — you have to remind the model which earlier image is the reference.

Use the Vertex AI dashboard for serialized work. The dashboard exposes the identity-lock controls more cleanly than the consumer Gemini app. If you are working through a 12-panel storyboard or a 30-image product sequence, the Vertex interface is worth the slightly more technical setup.

Build into a product via the Gemini API. For teams integrating Nano Banana 2 into a tool, the API documentation is at ai.google.dev. The customer roster Google has named publicly — Emergent, HubX, Klipy, and Whering — all integrate the API rather than using consumer Gemini.

Prompting practice that pays off:

• Front-load identity description. Spend the first part of your prompt describing the character or product in detail. Once Nano Banana 2 has a clear identity, the style variations land more reliably.
• Be explicit about what stays and what changes. “Same character, same face, same hair color. New outfit: navy suit. New setting: rooftop bar at night.” Tells the model what to lock and what to vary.
• Use the web-search grounding when accuracy matters. If you need a recognizable real-world object (a specific product, a famous landmark), tell the model to use search grounding. The result is much more accurate than a generic guess.

How does Nano Banana 2 compare to other image models?

Model	Strength	Best for
Nano Banana 2 (Gemini 3.1 Flash Image)	Identity preservation across sequence; web grounding; text rendering	Storyboards, comics, multi-panel campaigns
Midjourney v8	Stylistic range, photoreal quality	Single-image art direction, mood boards
DALL-E 3 (ChatGPT)	Wide reach, ChatGPT integration	Casual generation inside ChatGPT workflows
Stable Diffusion (open-weights)	Local control, fine-tuning	Custom-trained models, on-prem workflows
Flux (Black Forest Labs)	High-quality photoreal output	Realistic portraits, product photography

There is no head-to-head winner for image generation overall — each model is better for different jobs. The honest framing: pick Nano Banana 2 when consistency across a sequence is the unlock. For single hero images, Midjourney’s output still has a slight aesthetic edge. For full local control, open-weights Stable Diffusion variants remain the choice.

What are Nano Banana 2’s limitations?

Four practical limitations.

Image only, no video. If you need motion, use Veo 3.1 alongside. The two products are designed to work together in Google’s ecosystem but they are separate models.

Identity preservation is best-in-class but not perfect. Across long sequences (12+ images), small drift can still occur — subtle changes to face structure, hair, or product details. The model is the strongest at this task today, but human review is still required for production-grade brand consistency.

Closed weights, Google-only hosting. Nano Banana 2 cannot be downloaded or run on your own infrastructure. If you need a model that runs inside your own private network or on your own GPUs, an open-weights image model is the only option.

Web-search grounding adds latency. When you enable real-time search grounding, generation takes longer. For batch workflows where you are producing dozens of images, this can be noticeable. Most users find the accuracy improvement worth the wait, but it is a trade-off to be aware of.

Frequently Asked Questions

Why is it called Nano Banana 2?
It is the public nickname. The technical product name is Gemini 3.1 Flash Image. Google has used playful internal nicknames for several models in the Gemini family. Nano Banana 2 is the one Google leans on for marketing because it is easier to remember and search for than the formal name.

Is Nano Banana 2 free?
Yes, with daily quotas, via the Gemini consumer app. For heavier use, Google AI Pro at $19.99/month or Ultra at $249.99/month removes the daily limits and adds Veo 3.1 for video.

Can I use Nano Banana 2 images commercially?
Yes, on paid plans. Google’s terms permit commercial use of generated content from Gemini Pro and Ultra subscriptions, the Gemini API, and Vertex AI. Free-tier output has more restrictive terms — read them before commercial use.

Does Nano Banana 2 add a watermark?
Generated images include SynthID, Google’s invisible watermark that identifies content as AI-generated. There is no visible watermark on paid-tier output.

How does this work with Veo 3.1?
The two models pair well. Use Nano Banana 2 to generate or refine a still image (a character pose, a product shot, a key frame), then feed it into Veo 3.1 as an image-to-video starting point. Many production workflows use the two in this order — image first to lock the look, then video.

What is the difference between Nano Banana 1 and Nano Banana 2?
Nano Banana 1 (also called Gemini 2.5 Flash Image) released in mid-2025 was Google’s first identity-preserving image model. Nano Banana 2 (Gemini 3.1 Flash Image, February 2026) increased the identity capacity from a single character to up to 5 characters and 14 objects, added web-search grounding, improved text rendering precision, and extended the resolution range to 4K.

Sources

• Google Blog — Nano Banana 2 launch (February 26, 2026)
• Google DeepMind — Gemini 3.1 Flash Image model page
• Gemini API documentation

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