The Nano Banana 2 AI Image Generator: Cost and API (2026)

If you searched for the Nano Banana 2 AI image generator, you probably want one of two things: to make a few images right now, or to build the model into an app, a script or a content pipeline. This post covers both. It shows where you can try the model for free, how it compares with Nano Banana Pro, Lite and the newer 2.1, what each image costs in credits, and complete code that generates and edits images through a single API.

What is the Nano Banana 2 AI image generator?

Nano Banana 2 is Google’s image model in the Gemini family, released on 26 February 2026. “Nano Banana” started as a nickname for Gemini’s image generation. It caught on, and Google now uses it as the product name. The model does two jobs. The first is text-to-image: you describe a picture and it renders one. The second is image editing: you give it one or more photos plus an instruction, and it changes them.

What it does well:

• Realism. Photographic scenes, product shots and portraits look natural instead of overly glossy.
• Text in images. It handles signs, labels, posters and UI mockups well. Image generators have historically been weak at this.
• Edits with references. A reference image is a picture you give the model to copy or follow. You can pass up to 14 of them, which is how you keep a product, character or brand style consistent across a set of images.
• Resolution choice. It outputs at 1K, 2K or 4K, in aspect ratios 16:9, 1:1, 9:16, 21:9, 4:3, 3:2 and 2:3.

“Google Nano Banana 2” and “Gemini Nano Banana 2” are the same model. The Gemini app is the consumer front end, and the API is how developers reach it. The sibling models matter too, because choosing the wrong one is the most common way people overpay. The comparison below covers them.

Is Nano Banana 2 free?

Partly. Google offers the Nano Banana 2 AI image generator in the Gemini app and in Google AI Studio, and both have a free tier. That’s the right place to start if you only need a handful of images or want to get a feel for the model before you build anything. The catches:

• Daily limits. Free usage is capped, and Google changes the caps over time, so don’t plan a project around them.
• No automation. A chat window is fine for ten images. It’s painful for two hundred product shots that all need the same framing.
• Watermarking. Google embeds SynthID, an invisible watermark that marks content as AI-generated, in images from its Gemini image models. Assume it’s there however you access the model.

Then there are the “Nano Banana 2 free” sites that rank for this search. Most are resellers that wrap the same model, give you a few free credits, then ask for a subscription. Some are fine. Others never say which model actually runs when you press the button. The same caution applies to “Nano Banana 2 AI image generator APK” downloads. Google ships the model through the official Gemini app, so treat standalone APKs as unofficial and don’t install them.

If you need volume, consistency or code access, you’ll be paying per image somewhere. On Unicorn API a 1K image costs 8 credits, and you don’t need a separate Google Cloud account or billing setup. You can try the model in the browser playground before writing any code.

Nano Banana 2 vs Nano Banana Pro, Lite and 2.1

Google’s lineup keeps growing, and the names don’t make the trade-offs obvious. Before you commit the Nano Banana 2 AI image generator to a project, compare it with the alternatives. The table shows the Gemini image models and three other options on Unicorn API. Credits are per output image, and on a monthly plan one credit costs $0.01.

Model	Released	Output	Reference images	Credits per image	Best for
Nano Banana 2	Feb 2026	1K / 2K / 4K	Up to 14	8 / 12 / 18	The default for realism, text and edits
Nano Banana 2.1	Oct 2026	1K / 2K / 4K	Via image_input	4 / 6 / 9	The newest version. Test it on your own prompts
Nano Banana 2 Lite	Jul 2026	1K	Up to 10	4	Drafts, thumbnails, bulk edits
Nano Banana Pro	Nov 2025	1K / 2K / 4K	Up to 8	18 / 18 / 24	High-fidelity images with dense text
Nano Banana	Aug 2025	Set by image_size	Up to 3	4	Quick, casual images
GPT Image 1.5	Dec 2025	Low / medium / high quality	Up to 4	3 / 4 / 22	Transparent backgrounds
Flux 3 Klein	Oct 2026	1K / 2K / 4K	Edit version takes image_urls	1.6 / 2.5 / 6.5 (rounded up)	Lowest cost per image

Nano Banana Pro vs Nano Banana 2

Nano Banana Pro is the older model, released in November 2025. It’s built for high-fidelity images and accurate text rendering. Nano Banana 2 arrived three months later as the general-purpose default. These are the differences that should drive your choice:

• Price. Pro costs 18 credits at 1K or 2K and 24 at 4K. Nano Banana 2 costs 8, 12 and 18. At 1K, Nano Banana 2 costs less than half as much.
• References. Nano Banana 2 accepts up to 14 reference images. Pro accepts up to 8.
• Aspect ratios. Nano Banana 2 adds 3:2 and 2:3, which matter for print and portrait photography.

Start with Nano Banana 2. Move a job to Pro only if your own side-by-side test shows Pro handling your text or detail better, for example on a dense infographic or packaging with small print. “Nano Banana 2 Pro” and “Nano Banana Pro 2” are common searches, but there’s no separate model by either name in our catalog. People searching for them almost always mean one of these two.

When Lite or 2.1 is the better pick

Nano Banana 2 Lite is the cheapest Gemini image model, at 4 credits per image. It only outputs at 1K, but it still takes up to 10 reference images. That makes it a good fit for drafts, thumbnails and bulk edits where 1K is enough.

Nano Banana 2.1 came out on 7 October 2026. It costs 4, 6 or 9 credits at 1K, 2K and 4K, which is about half the price of Nano Banana 2 at each resolution. Because it’s so new, test it on your own prompts before you switch a production pipeline to it. Its request shape is also different. It takes reference images through an image_input field instead of ref_images, and it has no n field, so you request one image per job.

The original Nano Banana is still fine for quick, casual images. Outside Google’s lineup, GPT Image 1.5 is worth knowing about for its optional transparent background. Flux 3 Klein Text to Image and Flux 3 Klein Edit Image are the cheapest options in the catalog. For a broader look at photo-style output, see our guide to the realistic AI image generator options.

How much does the Nano Banana 2 AI image generator cost?

On Unicorn API, the Nano Banana 2 AI image generator is priced per output image based on resolution. If you leave resolution out, you get the 1K price. Setting n to request several images multiplies the cost by that number.

Resolution	Credits	On a monthly plan ($0.01/credit)	With top-up credits ($0.015/credit)
1K	8	$0.08	$0.12
2K	12	$0.12	$0.18
4K	18	$0.18	$0.27

Monthly plans give you credits at $0.01 each: Starter is $25, Creator $50, Studio $100 and Pro $200 per month. Top-up credits cost $0.015 each and never expire, which suits occasional use. The 2,500 credits in a Starter plan cover about 312 images at 1K, 208 at 2K or 138 at 4K. Full plan details are on the pricing page.

Worked example: 50 product images

Say you run a small online shop and need hero images for 50 products, each at 2K in 4:3, with four options per product to choose from. Here are three ways to do it:

1. All finals at 2K. Request n: 4 at 2K for each product: 50 × 4 × 12 = 2,400 credits, or $24 on a plan.
2. Draft at 1K, finish at 2K. Generate four drafts at 1K (32 credits), then render the chosen one at 2K, passing the draft as a reference (12 credits). That’s 44 × 50 = 2,200 credits, or $22.
3. Draft on Lite, finish on Nano Banana 2. Four Lite drafts (16 credits) plus one 2K final (12 credits) is 28 × 50 = 1,400 credits, or $14.

All three fit within a Starter month. The trade-off with options 2 and 3 is that the 2K final is a new generation guided by the draft, not an upscale of it, so small details can shift. That’s usually fine for lifestyle shots. If you need the exact draft at high resolution, use option 1.

You don’t have to do this arithmetic by hand. POST /v1/models/price returns the exact cost of a request before you run it, which is useful as a guard in batch scripts.

How to use the Nano Banana 2 AI image generator through the API

Every media model on Unicorn API works the same way. You submit a job, poll until it finishes, then download the result. Here’s the full flow:

1. Create a key on the API keys page and export it as UNICORN_API_KEY.
2. Optionally, send your request body to /v1/models/price to see the cost.
3. POST /v1/jobs with the model ID and an input object.
4. Poll GET /v1/jobs/{id} until status is "succeeded".
5. Read the image URL from outputs[0].url, or from every item in outputs if you set n.

Python: text-to-image

import os
import time
import requests

API = "https://api.unicornapi.net"
HEADERS = {"Authorization": f"Bearer {os.environ['UNICORN_API_KEY']}"}

body = {
    "model": "nano-banana-2",
    "input": {
        "prompt": (
            "Product photo of a matte black ceramic coffee mug on a light oak table, "
            "soft morning window light from the left, shallow depth of field, "
            "a small paper tag on the handle that reads \"SLOW BREW\""
        ),
        "resolution": "2K",
        "aspect_ratio": "4:3",
        "n": 2,
    },
}

# Check the cost first (2 images at 2K = 24 credits)
price = requests.post(f"{API}/v1/models/price", json=body, headers=HEADERS)
price.raise_for_status()
print("Price:", price.json())

# Submit the job
resp = requests.post(f"{API}/v1/jobs", json=body, headers=HEADERS)
resp.raise_for_status()
job_id = resp.json()["id"]

# Poll until it finishes
while True:
    job = requests.get(f"{API}/v1/jobs/{job_id}", headers=HEADERS).json()
    if job["status"] == "succeeded":
        break
    if job["status"] == "failed":
        raise SystemExit(f"Job failed: {job}")
    time.sleep(3)

for i, output in enumerate(job["outputs"]):
    image = requests.get(output["url"])
    with open(f"mug_{i}.png", "wb") as f:
        f.write(image.content)
    print("Saved", f"mug_{i}.png", "from", output["url"])

Polling every few seconds is plenty. Image jobs don’t need tight loops, and sleeping between checks keeps your script polite.

curl: editing with reference images

Editing uses the same endpoint. Add ref_images with up to 14 image URLs and describe the change you want. This example puts a real product photo into a new scene:

curl -s https://api.unicornapi.net/v1/jobs \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nano-banana-2",
    "input": {
      "prompt": "Place the mug from the reference photos on a walnut desk next to an open notebook, warm afternoon light. Keep the mug shape, colour and logo exactly as in the references.",
      "ref_images": [
        "https://example.com/mug-front.png",
        "https://example.com/mug-side.png"
      ],
      "resolution": "2K",
      "aspect_ratio": "3:2"
    }
  }'

# Then poll with the returned id until status is "succeeded"
curl -s https://api.unicornapi.net/v1/jobs/JOB_ID \
  -H "Authorization: Bearer $UNICORN_API_KEY"

To try a cheaper model, change the model value. Watch the field names, though: Nano Banana 2.1 uses image_input, and the Flux 3 Klein models use image_urls and num_images. The API documentation lists the inputs for every model, and our Nano Banana API guide covers the whole Gemini image family. If you work in Cursor or Claude Code, you can also call these models from your editor through MCP.

Prompting tips for better Nano Banana 2 images

The Nano Banana 2 AI image generator follows plain descriptive language well. You don’t need keyword lists or “trending on” tags. These habits make the biggest difference:

• Write a sentence, not a tag cloud. Cover subject, setting, light, camera and mood: “a close-up of a ceramic mug on oak, soft side light, shallow depth of field” beats “mug, oak, 8k, photorealistic, masterpiece”.
• Put the exact text in quotes. If the image needs words on a sign or label, write them in quotation marks and keep them short. Long paragraphs of in-image text are where every model starts to slip.
• Use parameters for format. Set aspect_ratio and resolution in the input instead of writing “wide 4K image” in the prompt.
• Say what must stay the same. In edits, name what to keep (“keep the face, hair and jacket unchanged”) as well as what to change.
• Make one change per edit. Changing the background, lighting and outfit in a single instruction gives the model more ways to drift. Chain small edits instead.
• Label your references. When you pass several images, say which is which in the prompt, for example “the product from the first image, the room style from the second”.

Common mistakes with Nano Banana 2

Most problems people hit with the Nano Banana 2 AI image generator come down to cost settings or request shapes, not image quality.

• Rendering everything at 4K. 4K costs more than twice as much as 1K. If the image goes into a 1200-pixel-wide blog layout or a social post, 1K or 2K is enough. Save 4K for print and large hero banners.
• Forgetting that n multiplies the price. n: 4 at 4K is 72 credits per request. Run the price endpoint in batch scripts so a typo can’t drain your balance.
• Paying Pro prices by default. Nano Banana Pro costs more than twice as much as Nano Banana 2 at 1K. Use Pro only when a test shows it’s better for your specific job.
• Copying request bodies between models. ref_images, image_input and image_urls are different fields on different models, and so are n and num_images. Check the model page before you switch.
• Hotlinking result URLs. Download outputs and store them in your own bucket or CDN. Treat the job URL as a delivery link, not permanent hosting.
• Trusting unofficial “free” apps. An APK or website that claims to run Nano Banana 2 for free may run something else entirely. If the model matters, use Google’s own apps or an API where you choose the model ID.

Is Nano Banana 2 a good image generator?

For most work, yes. The Nano Banana 2 AI image generator combines realistic output, reliable text in images and strong reference-based editing at a mid-range price. It’s the model to start with for product photos, marketing visuals, mockups and character-consistent sets.

It’s not the right pick for every job:

• High-volume drafts: Nano Banana 2 Lite at 4 credits, or Flux 3 Klein for the lowest cost per image.
• Lower price at higher resolution: Nano Banana 2.1, at 6 credits for 2K and 9 for 4K, is worth testing now.
• Dense typography: run a side-by-side test against Nano Banana Pro.
• Transparent PNGs: GPT Image 1.5 has a background option. Nano Banana 2 doesn’t.

One Unicorn API key covers all of these, so you can send the same prompt to three models, compare the results and keep the cheapest one that’s good enough. You can browse the full model catalog or start running the Nano Banana 2 AI image generator in the playground today.