GPT Image 2 vs Nano Banana Pro: Pricing and API (2026)

GPT Image 2 vs Nano Banana Pro is the image-model choice most teams are weighing in late 2026: OpenAI’s newest image model against Google’s high-fidelity Gemini image model. Both render at 1K, 2K and 4K, both accept reference images, and both follow detailed prompts well. Where they really differ is price, framing options and how many references you can feed them, and those differences decide more real projects than small gaps in perceived quality.

This comparison covers what each model is, what each image costs, where each one tends to be stronger, and how to run your own side-by-side test with one API key and a short Python script.

GPT Image 2 vs Nano Banana Pro: the short answer

If you only read one section, make it this one.

• Pick GPT Image 2 when you generate at volume, need unusual formats (ultra-wide banners, 4:5 social portraits, tall 1:3 strips) or want to steer the output with many reference images. It costs 6 credits at 1K, 10 at 2K and 16 at 4K, and takes up to 16 references.
• Pick Nano Banana Pro when exact on-image text and high fidelity are the whole brief, such as posters, packaging mockups and infographics, and a single image is worth 18 to 24 credits.
• Not sure? Test both. A GPT Image 2 vs Nano Banana Pro run of ten prompts each at 1K costs 60 + 180 = 240 credits, which is $2.40 on a monthly plan.

Published reviews do not settle the question. Several of the widely shared tests since April pit GPT Image 2 against Nano Banana 2, which is a different Google model, and the ones that do use Pro reach mixed verdicts depending on the prompt set. At least one evaluation found Nano Banana Pro ahead on Chinese-language scenes. Treat any single review as one data point, then check it against your own prompts.

What are GPT Image 2 and Nano Banana Pro?

Both are text-to-image models: you send a natural-language prompt and get back an image that matches it. Both also do image-to-image work through reference images. A reference image is a picture you attach to the request so the model can copy a face, a product, a layout or a style instead of inventing one.

GPT Image 2 (OpenAI)

OpenAI released GPT Image 2 on 21 April 2026. On Unicorn API its model id is gpt-image-2, which is also the name you will see written as “gpt-image-2”, “OpenAI GPT Image 2” or “openai/gpt-image-2” in other tools. Its strengths on paper are breadth and control:

• Three resolutions: 1K, 2K and 4K.
• The widest aspect-ratio range of any model in this comparison: auto plus 15 fixed ratios, from 3:1 to 1:3.
• Up to 16 reference images per request.
• Input fields: prompt, n (number of images), resolution, aspect_ratio, ref_images and background.

Nano Banana Pro (Google)

Google released Nano Banana Pro on 20 November 2025, about five months before GPT Image 2. Its id is nano-banana-pro. It is the high-end member of the Gemini image family and is aimed at fidelity:

• High-fidelity images with accurate text rendering.
• Resolutions of 1K, 2K and 4K.
• Five aspect ratios: 16:9, 1:1, 9:16, 21:9 and 4:3.
• Up to 8 reference images.
• Input fields: prompt, n, resolution, aspect_ratio, ref_images and output_format.

The “Nano Banana” name covers several models, which causes much of the confusion in search results. The original Nano Banana is a quick, natural-looking generator and editor with up to 3 references. Nano Banana 2 Lite is the cheapest Gemini image model, producing fast 1K images with up to 10 references. Nano Banana Pro sits above both. When you read a GPT Image 2 vs Nano Banana Pro review, check which Nano Banana the author actually ran.

How much does each image cost?

Price is the clearest difference between the two. Unicorn API bills in credits. Monthly plans give credits at $0.01 each (Starter $25, Creator $50, Studio $100, Pro $200 per month), and top-up credits cost $0.015 each and never expire.

Resolution	GPT Image 2	Nano Banana Pro	Nano Banana Pro costs
1K	6 credits ($0.06 plan / $0.09 top-up)	18 credits ($0.18 / $0.27)	3x more
2K	10 credits ($0.10 / $0.15)	18 credits ($0.18 / $0.27)	1.8x more
4K	16 credits ($0.16 / $0.24)	24 credits ($0.24 / $0.36)	1.5x more

Two things stand out in the GPT Image 2 vs Nano Banana Pro pricing. First, the gap shrinks as resolution rises: at 1K you pay three times as much for Pro, at 4K only half again as much. Second, Nano Banana Pro charges 18 credits for both 1K and 2K, so there is rarely a reason to ask it for 1K. Request 2K and downscale if you need smaller files.

Prices are per output. If you set n to 4, you pay four times the per-image price, rounded up to whole credits.

What a monthly plan buys

Plan (credits)	GPT Image 2 at 1K	GPT Image 2 at 4K	Nano Banana Pro at 1K/2K	Nano Banana Pro at 4K
Starter, $25 (2,500)	416 images	156 images	138 images	104 images
Creator, $50 (5,000)	833 images	312 images	277 images	208 images
Studio, $100 (10,000)	1,666 images	625 images	555 images	416 images
Pro, $200 (20,000)	3,333 images	1,250 images	1,111 images	833 images

You can check the exact credit cost of any request before running it with POST /v1/models/price; the request format is in the API documentation. Full plan details are on the pricing page.

Is GPT Image 2 free anywhere?

Searches for “gpt image 2 free” usually come from people who have seen it inside a chat app. Consumer apps set their own limits, and those change often. Through any API, every image is billed. The cheapest way to try GPT Image 2 on Unicorn API is a single 1K image at 6 credits, either in the browser playground or with the code further down.

Resolution, aspect ratios and reference images

After price, the specs that matter most in a GPT Image 2 vs Nano Banana Pro decision are framing and references.

Feature	GPT Image 2	Nano Banana Pro
Maker	OpenAI	Google
Released	21 April 2026	20 November 2025
Resolutions	1K, 2K, 4K	1K, 2K, 4K
Aspect ratios	auto, 16:9, 1:1, 9:16, 21:9, 9:21, 4:3, 3:4, 3:2, 2:3, 2:1, 1:2, 3:1, 1:3, 5:4, 4:5	16:9, 1:1, 9:16, 21:9, 4:3
Max reference images	16	8
Extra fields	background	output_format
Price per image	6 / 10 / 16 credits	18 / 18 / 24 credits
Headline strength	Widest aspect-ratio range, most references	High fidelity, accurate text

Why aspect ratio matters more than it looks

If your output has to land in a fixed slot, generating at the right ratio beats cropping. Cropping a 1:1 image to 4:5 throws away composition the model worked out, and it can cut text near the edges. GPT Image 2 renders 4:5 and 3:4 portraits, 3:2 and 2:3 photo frames, and extreme 3:1 or 1:3 banners natively. Nano Banana Pro covers the common five (widescreen, square, vertical video, cinematic 21:9 and classic 4:3) and nothing else. For a portrait social feed, a website hero strip or a tall sidebar ad, that alone can decide it.

Reference images: 16 vs 8

References are how you keep a product, a mascot or a person consistent across many images. Eight is plenty for one product shot with a style guide. Sixteen helps when you want several angles of a product plus brand colour swatches plus a layout example in one request. If you are building character or product consistency, our guide to an AI character generator from a photo covers how to choose and order references.

Text, realism and editing: where each one is stronger

Quality is where GPT Image 2 vs Nano Banana Pro comparisons get noisy, because results swing with the prompt. Here is what each model is designed for, and what that means in practice.

Text inside images

Nano Banana Pro’s design explicitly targets accurate text rendering, so it is the safer first pick for posters, menus, packaging and infographics where a single wrong letter ruins the image. GPT Image 2 also handles text-heavy prompts, and some public testers rate its layouts and spatial logic highly. If text accuracy is your main criterion, test both and count spelling errors per image instead of judging by eye.

Prompt tips that help both models:

• Put the exact words in quotation marks: headline text "OPEN UNTIL 2 AM".
• Keep each text block short. A headline and a subline work better than a paragraph.
• Say where the text goes (“centred at the top”, “on the bottle label”).
• Use 2K or 4K for small text. More pixels give letters room to form.

Non-English text

If you generate in Chinese, Japanese, Arabic or other scripts, do not rely on English-language tests. One published evaluation found Nano Banana Pro generally stronger on Chinese scenarios. Run your own short set in your target language before committing.

Photorealism and style

Both models produce convincing photographic images. Reviewers tend to describe the Gemini image family as leaning toward cinematic, natural-looking realism and GPT Image 2 as strong on prompt logic, meaning objects end up where you asked and counts are right. Those are tendencies, not rules. A prompt with five specific objects in specific positions is a good stress test for prompt adherence; a moody portrait at dusk is a good test for realism.

Editing and consistency

Both take reference images, so both can restyle a photo, place a product in a new scene or keep a character consistent. GPT Image 2’s higher reference limit gives you more control in complex edits. Nano Banana Pro’s output_format field lets you pick the file format of the result, which saves a conversion step in some pipelines. GPT Image 2 has a background field; check the docs for the values it accepts.

How to run a GPT Image 2 vs Nano Banana Pro test with one API key

The fastest way to settle GPT Image 2 vs Nano Banana Pro for your use case is to send the same prompts to both and compare. On Unicorn API both models share one endpoint, one key and one credit balance, so the only change is the model field.

1. Create a key on the API keys page and export it as UNICORN_API_KEY.
2. Submit a job with POST /v1/jobs. You get back a job id.
3. Poll GET /v1/jobs/{id} until status is "succeeded".
4. Read the image URL from outputs[0].url.

One request with curl

curl -X POST https://api.unicornapi.net/v1/jobs \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "input": {
      "prompt": "A red fox resting in a snowy forest at dawn, soft light, highly detailed",
      "resolution": "2K",
      "aspect_ratio": "16:9"
    }
  }'

Swap "gpt-image-2" for "nano-banana-pro" to run the same prompt on Google’s model.

A side-by-side test in Python

This script sends each prompt to both models at the same resolution and aspect ratio, waits for the results and prints the image URLs. It uses 9:16 because both models support it, so neither gets an unfair crop.

import os
import time
import requests

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

MODELS = ["gpt-image-2", "nano-banana-pro"]

PROMPTS = [
    'A minimalist poster for a coffee shop, headline text "OPEN UNTIL 2 AM", '
    "warm neon light, rainy street at night",
    "Five ceramic mugs in a row on a wooden shelf, left to right: red, blue, "
    "white, green, yellow, soft morning light, product photo",
    "Portrait of an elderly fisherman mending a net at dusk, cinematic lighting",
]

SETTINGS = {"resolution": "2K", "aspect_ratio": "9:16"}


def submit(model, prompt):
    body = {"model": model, "input": {"prompt": prompt, **SETTINGS}}
    r = requests.post(f"{API}/v1/jobs", headers=HEADERS, json=body, timeout=30)
    r.raise_for_status()
    return r.json()["id"]


def wait(job_id, every=3, limit=300):
    waited = 0
    while waited < limit:
        r = requests.get(f"{API}/v1/jobs/{job_id}", headers=HEADERS, timeout=30)
        r.raise_for_status()
        job = r.json()
        if job["status"] == "succeeded":
            return job["outputs"][0]["url"]
        if job["status"] in ("failed", "canceled", "cancelled", "error"):
            raise RuntimeError(f"Job {job_id} ended with status {job['status']}")
        time.sleep(every)
        waited += every
    raise TimeoutError(f"Job {job_id} did not finish in {limit} seconds")


# Submit everything first so the jobs run in parallel, then collect results.
pending = [(p, m, submit(m, p)) for p in PROMPTS for m in MODELS]

for prompt, model, job_id in pending:
    url = wait(job_id)
    print(f"{model:16} | {prompt[:50]}... \n  {url}\n")

At 2K this run costs 3 x 10 = 30 credits for GPT Image 2 and 3 x 18 = 54 credits for Nano Banana Pro, so 84 credits in total. Expand PROMPTS to ten or more real prompts from your product before drawing conclusions.

If you work inside Cursor, Claude Code or another coding agent, you can call the same models through MCP; see using Unicorn API from agents.

How to score the results

Write down your criteria before you look at images, or you will end up judging on whichever picture caught your eye first. A simple sheet works: for each image, score prompt adherence (did it include everything asked for, in the right place?), text accuracy (count wrong or missing characters), realism or style fit, and whether you would ship it without a re-roll. Then divide credits spent by the number of shippable images. A model that costs three times as much but needs half the retries can come out close.

Which should you choose for your project?

Use case	Better first pick	Why
Blog and social images at volume	GPT Image 2	6 credits at 1K, native 4:5 and 3:4
Posters, menus, packaging text	Nano Banana Pro	Designed for accurate text rendering
Web banners and wide headers	GPT Image 2	3:1, 2:1 and 21:9 supported
Product shots with many reference angles	GPT Image 2	Up to 16 references
Hero images where one perfect render matters	Test both	The 4K price gap is only 1.5x
Chinese or other non-Latin text	Test both, lean Nano Banana Pro	Some evaluations favour Pro here

A mixed pipeline often beats picking one

You do not have to commit to one model. A common pattern is to explore cheaply and finish expensively: generate drafts and variations with GPT Image 2 at 1K, pick the winning concept, then render the final at 2K or 4K on whichever model handles that specific image better. With one API key, switching is a string change.

Cheaper alternatives in the same families

If neither price fits, look one tier down. The full list is in the model catalog.

• Nano Banana 2 Lite: 4 credits per image, fast 1K output, up to 10 references.
• Nano Banana: 4 credits per image, natural-looking images and edits, up to 3 references.
• GPT Image 1.5: 3, 4 or 22 credits at low, medium or high quality, with legible text and an optional transparent background, which suits logos and stickers.
• GPT Image 2.6 (Economy) Text to Image and GPT Image 2.6 (Economy) Image to Image: OpenAI’s newest listings, added on 6 November 2026 at 5 credits per image. Check their pages for supported options before swapping them into a pipeline.

Common mistakes when comparing image models

• Comparing different settings. A 4K image from one model against a 1K image from the other is not a fair test. Match resolution and aspect ratio every time.
• Judging from one prompt. Any model can win a single prompt. Use at least ten that reflect your actual workload, including the awkward ones.
• Mixing up Nano Banana models. Many viral comparisons use Nano Banana 2, not Pro. Results do not transfer between them.
• Paying for 1K on Nano Banana Pro. 1K and 2K cost the same 18 credits. Ask for 2K.
• Forgetting n. Setting n to 4 quadruples the cost. Check with POST /v1/models/price before batch runs.
• Requesting a ratio the model does not support. Nano Banana Pro accepts five ratios. Sending 4:5 to it will not get you a native 4:5 image, so plan crops or route those jobs to GPT Image 2.
• Ignoring retries in cost maths. Cost per shippable image matters more than cost per image. Track how many renders you throw away.
• Trusting cherry-picked samples. Social posts show the best of many attempts. Your first render is the honest benchmark.

The practical takeaway: GPT Image 2 is the cheaper, more flexible default, and Nano Banana Pro earns its higher price when on-image text and fidelity carry the job. Spend a couple of dollars running both on your own prompts and the GPT Image 2 vs Nano Banana Pro question answers itself for your workload.