Imagen 4 vs Nano Banana: Quality, Price and API (2026)

The Imagen 4 vs Nano Banana question comes down to one difference: Imagen 4 is a text-to-image generator, while Nano Banana is a Gemini image model that can also take pictures as input and edit them. Both come from Google, both cost 4 credits per image on Unicorn API, and both turn a plain prompt into a finished image. Where they split is control. Imagen 4 Fast gives you a seed and a negative prompt. Nano Banana gives you reference images and edits. This post compares the two on how they work, what they cost, where each one is stronger, and how to test them side by side with a short script.

Imagen 4 vs Nano Banana at a glance

The naming causes most of the confusion, so start there. Google runs two separate image lines. Imagen is a dedicated text-to-image model family. A text-to-image model takes a natural-language prompt and returns an image that matches it. Imagen 4 comes in several tiers, and reviews online often discuss the top tier, Imagen 4 Ultra. The variant in the Unicorn API catalog is Imagen 4 Fast, released on 19 August 2025.

Nano Banana is the nickname for Google’s Gemini image models. The original Nano Banana arrived on 26 August 2025, a week after Imagen 4 Fast. It was followed by Nano Banana Pro on 20 November 2025 and Nano Banana 2 Lite on 1 July 2026. So when people search “Imagen 3 vs Nano Banana 2” or “Imagen 4 vs Nano Banana Pro”, they are comparing members of two different families, not versions of one product.

Here is how the four Google models on Unicorn API line up.

Model	Model id	Price per image	Reference images	Size options	Extra controls
Imagen 4 Fast	google-imagen4-fast	4 credits	None (text only)	aspect_ratio	seed, negative_prompt
Nano Banana	nano-banana	4 credits	Up to 3	image_size; 16:9, 1:1, 9:16, 21:9, 4:3	n, output_format
Nano Banana 2 Lite	nano-banana-2-lite	4 credits	Up to 10	1K; 16:9, 1:1, 9:16, 21:9, 4:3, 3:2, 2:3	n
Nano Banana Pro	nano-banana-pro	18 credits (1K, 2K), 24 credits (4K)	Up to 8	1K, 2K, 4K; 16:9, 1:1, 9:16, 21:9, 4:3	n, output_format, accurate text rendering

The short version: at the same price, Imagen 4 Fast is the better fit when you only have words and want repeatable output, and Nano Banana is the better fit when you have images you want to reuse, edit or combine. Nano Banana Pro is the step up when the image must contain readable text or needs 4K.

How Imagen 4 and Nano Banana work differently

Imagen 4: prompt in, picture out

The Imagen family grew out of Google’s diffusion research. A diffusion model starts from random noise and removes it step by step, guided by your prompt, until an image appears. You describe the scene and the model renders it. On Unicorn API, Imagen 4 Fast accepts four inputs:

• prompt: the description of the image.
• aspect_ratio: the shape of the frame, such as “1:1”.
• seed: a number that fixes the starting noise. Same prompt plus same seed gives you a near-identical image, which is what makes results reproducible.
• negative_prompt: things you want kept out of the image, such as “text, watermark, blur”.

There is no field for reference images. Imagen 4 Fast cannot look at your product photo or your logo. Everything it knows about the image comes from the prompt.

Nano Banana: an image model with a language model’s understanding

Nano Banana belongs to the Gemini family, so it reads prompts with a language model’s grasp of instructions and can take images as part of the input. That is why “put this mug on a marble kitchen counter” works: you pass the mug photo as a reference image and describe the change. The original Nano Banana accepts up to 3 reference images, plus n (how many images to return), image_size, aspect_ratio and output_format.

Nano Banana does not list a seed or a negative prompt. You steer it with clear instructions and with reference images, and two runs of the same prompt will usually differ.

The practical consequence is simple. If your pipeline is “generate 200 blog headers from text and be able to regenerate any one of them later”, Imagen’s seed is the feature you want. If your pipeline is “take this product and show it in ten settings”, you need reference images, and Imagen 4 Fast cannot do it at all.

Imagen 4 vs Nano Banana pricing in 2026

Unicorn API prices every model in credits. Monthly plans give you credits at $0.01 each (Starter $25, Creator $50, Studio $100, Pro $200 per month). Top-up credits cost $0.015 each and never expire. Here is what one image costs on each Google model.

Model and setting	Credits per image	On a monthly plan	With top-up credits
Imagen 4 Fast	4	$0.04	$0.06
Nano Banana	4	$0.04	$0.06
Nano Banana 2 Lite	4	$0.04	$0.06
Nano Banana Pro, 1K or 2K	18	$0.18	$0.27
Nano Banana Pro, 4K	24	$0.24	$0.36

On price alone, the Imagen 4 vs Nano Banana comparison is a tie. The cost gap only opens when you move to Nano Banana Pro, which is about 4.5x the price at 1K or 2K and 6x at 4K.

A worked example

Say you need 500 images a month for an online shop.

• Imagen 4 Fast or Nano Banana: 500 × 4 = 2,000 credits, or $20. That fits inside the Starter plan’s 2,500 credits with room for retries.
• Nano Banana Pro at 2K: 500 × 18 = 9,000 credits, or $90. That points you at the Studio plan.
• Nano Banana Pro at 4K: 500 × 24 = 12,000 credits, or $120 on plan credits.

Two details catch people out. First, on the Nano Banana models the price is multiplied by n, so asking for 4 variations of one prompt costs 16 credits, not 4. Second, you never have to guess: the POST /v1/models/price endpoint returns the exact price of a request before you run it. See the API documentation for the request shape, the pricing page for plans, and our breakdown of the Nano Banana 2 price if you are weighing the newer models.

Which is better for photos, text, edits and repeatability?

Most “which is better” threads compare one prompt and declare a winner. That tells you little, because the two models are built for different inputs. Judge them by task instead.

Clean single images from a text prompt

Both handle a straightforward scene well: a landscape, a portrait, a product on a plain background. Imagen 4 Fast has one advantage here, the negative prompt. If a model keeps adding stray lettering or a frame around your subject, telling Imagen to avoid it is more reliable than hoping Nano Banana reads “no text” in a long instruction.

Text inside the image

Posters, labels, menus and thumbnails need legible words. Of the Google models in this comparison, Nano Banana Pro is the one built for accurate text rendering, and that is the main reason to pay its higher price. Neither Imagen 4 Fast nor the original Nano Banana is positioned around text. If your images carry headlines, test Nano Banana Pro and GPT Image 1.5, which is also described for legible text, before settling on a cheaper model.

Editing and compositing

This is where the Imagen 4 vs Nano Banana choice is made for you. Imagen 4 Fast has no image input, so it cannot edit. Nano Banana takes up to 3 reference images, Nano Banana Pro up to 8, and Nano Banana 2 Lite up to 10. Background swaps, adding a product to a lifestyle scene, combining a character with a setting: all of these need a Gemini image model or a dedicated edit model.

Consistency across a series

“Consistency” means two different things, and each model owns one of them:

• Reproducing the same image: Imagen 4 Fast with a fixed seed. Useful for regenerating an approved asset or making small prompt tweaks while keeping the composition.
• Keeping the same subject across different images: Nano Banana with reference images. Useful for a mascot, a product line or a person shown in many scenes. If you need many references at once, Nano Banana 2 Lite’s limit of 10 is the highest of the group at the same 4-credit price.

For shop imagery specifically, our guide to AI product photography walks through reference-image workflows in more detail.

How to test Imagen 4 vs Nano Banana side by side

The only comparison that matters is the one run on your prompts. With one Unicorn API key you can send the same prompt to both models and look at the results together. If you would rather click than code, the image studio runs both models in the browser.

1. Create a key on the API keys page and export it: export UNICORN_API_KEY=...
2. Pick 5 to 10 prompts that represent your real workload, not showcase prompts.
3. Run the script below, which creates a job for each model, polls until each job has status "succeeded", and prints the output URLs.

import os
import time
import requests

API = "https://api.unicornapi.net"
HEADERS = {
    "Authorization": f"Bearer {os.environ['UNICORN_API_KEY']}",
    "Content-Type": "application/json",
}

PROMPT = "A ceramic coffee mug on a wooden table by a window, morning light, product photo"

JOBS = {
    # Imagen 4 Fast: text only, with a fixed seed and a negative prompt
    "google-imagen4-fast": {
        "prompt": PROMPT,
        "aspect_ratio": "1:1",
        "seed": 42,
        "negative_prompt": "text, watermark, blur",
    },
    # Nano Banana: same prompt, same aspect ratio
    "nano-banana": {
        "prompt": PROMPT,
        "aspect_ratio": "1:1",
    },
}


def run_job(model, inputs, timeout=300):
    resp = requests.post(
        f"{API}/v1/jobs",
        headers=HEADERS,
        json={"model": model, "input": inputs},
    )
    resp.raise_for_status()
    job_id = resp.json()["id"]

    deadline = time.time() + timeout
    status = None
    while time.time() < deadline:
        job = requests.get(f"{API}/v1/jobs/{job_id}", headers=HEADERS).json()
        status = job["status"]
        if status == "succeeded":
            return job["outputs"][0]["url"]
        time.sleep(2)
    raise TimeoutError(f"{model} job {job_id} did not finish, last status: {status}")


for model, inputs in JOBS.items():
    url = run_job(model, inputs)
    print(f"{model}: {url}")

This test costs 8 credits per prompt, 4 for each model. Ten prompts cost 80 credits, well under a dollar on plan credits.

Testing an edit with Nano Banana

To see the feature Imagen 4 Fast lacks, pass a reference image to Nano Banana. Replace the URL with a public link to your own image.

curl -X POST https://api.unicornapi.net/v1/jobs \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nano-banana",
    "input": {
      "prompt": "Place this mug on a marble kitchen counter next to a small plant, soft daylight",
      "ref_images": ["https://example.com/my-mug.png"],
      "aspect_ratio": "4:3"
    }
  }'

Then poll GET /v1/jobs/{id} the same way and read outputs[0].url. If you work inside Cursor or Claude Code, you can also run these comparisons from your editor through the MCP integration for agents.

Mistakes people make when comparing them

• Reading Ultra reviews and buying Fast. Many opinions about Imagen 4 are about the Ultra tier. If you call Imagen 4 Fast, judge Imagen 4 Fast.
• Comparing on one prompt and one run. Nano Banana varies between runs and Imagen changes with the seed. Run each prompt a few times before deciding.
• Sending reference images to Imagen 4 Fast. It has no ref_images field. If your workflow depends on a reference, Imagen 4 Fast is the wrong model, not a weaker one.
• Paying for Nano Banana Pro by default. At 18 to 24 credits per image, Pro is worth it for in-image text, 4K output or up to 8 references. For a plain 1K scene, the 4-credit models are the sensible first try.
• Forgetting that n multiplies the bill. Four variations on Nano Banana Pro at 4K is 96 credits for one prompt.
• Ignoring the negative prompt. When an Imagen output keeps including something unwanted, list it in negative_prompt instead of rewriting the whole prompt.
• Mixing aspect ratios in a comparison. A 16:9 frame and a 1:1 frame are composed differently. Keep the ratio identical when you compare.

Other image models worth testing

The Imagen 4 vs Nano Banana decision is not the only one worth making. A few non-Google models sit at similar price points and cover gaps in both:

• GPT Image 1.5 (OpenAI): reliable prompt adherence and legible text, an optional transparent background, and up to 4 reference images. It costs 3 credits at low quality, 4 at medium and 22 at high, with aspect ratios 1:1, 3:2 and 2:3. The transparent background is useful for logos and cut-out product shots, which neither Google model in this comparison offers.
• Flux 3 Klein Text to Image (Black Forest Labs, released 20 October 2026): 1.6 credits per 1K image, 2.5 at 2K and 6.5 at 4K, rounded up to whole credits, so a single 1K image bills as 2 credits. It supports a seed and custom width and height, so it competes directly with Imagen 4 Fast for repeatable text-only batches at a lower price.
• Flux 3 Klein Edit Image: the same pricing, with image_urls as input for edits. A cheap alternative to Nano Banana when you need edits at volume.

All of them use the same POST /v1/jobs flow as the script above, so adding one to your test is a one-line change to the JOBS dictionary. Browse the full model catalog for the rest.

Which should you pick?

Use this as a starting rule, then confirm it with your own prompts:

• Text-only prompts, need to regenerate exact images later: Imagen 4 Fast, with a fixed seed.
• Need to keep things out of the image reliably: Imagen 4 Fast, with a negative prompt.
• Editing a photo or placing a product in a new scene: Nano Banana.
• Same subject across many images, many references: Nano Banana 2 Lite, up to 10 references at 4 credits.
• Readable text in the image, 2K or 4K output: Nano Banana Pro, and test GPT Image 1.5 alongside it.
• Lowest cost per image at volume: try Flux 3 Klein before either Google model.

Because Imagen 4 Fast and Nano Banana cost the same, you do not have to pick one for good. Many pipelines route text-only jobs to Imagen and anything with a reference image to Nano Banana, all under one key and one bill. The Imagen 4 vs Nano Banana question then stops being a choice and becomes a routing rule: check whether the request includes an image, and send it to the model built for that input.