Nano Banana Pro vs GPT-image-2: Image API Comparison for Developers
You're building an image generation feature and GenRelay gives you two fundamentally different models: Nano Banana Pro at $0.030/image and GPT-image-2 at $0.014/image. The 2× price gap stands out — but cost is only one dimension. The models differ in quality characteristics, maximum resolution, and what they're designed to do. Picking the wrong one means either overpaying for outputs your use case doesn't need, or shipping results that don't meet your quality bar.
This article covers pricing at production scale, quality differences, editing capabilities, and a clear per-use-case verdict. Both models are available on GenRelay — Nano Banana Pro and GPT-image-2 — via the same API key with no separate subscriptions.
How do the two models differ at a high level?
Nano Banana Pro (Gemini 3 Pro Image) is optimized for photorealistic output: accurate material textures, reliable color reproduction, and consistent handling of complex multi-element compositions. It offers native resolution tiers up to 4K.
GPT-image-2 is OpenAI's image model, built for versatile creative generation and practical editing workflows — it supports inpainting, outpainting, and image-based editing through a dedicated endpoint that Nano Banana Pro does not have.
| Dimension | Nano Banana Pro | GPT-image-2 |
|---|---|---|
| Underlying model | Gemini 3 Pro Image | OpenAI GPT-image-2 |
model parameter |
nano-banana-pro |
gpt-image-2 |
| Price — 1K (1024×1024) | $0.030/image | $0.014/image |
| Price — 2K (2048×2048) | $0.030/image | $0.014/image |
| Price — 4K (4096×4096) | $0.042/image | Not available |
| Generation endpoint | /v1/images/generations |
/v1/images/generations |
| Editing endpoint | Not supported | /v1/images/edits |
| Inpainting / outpainting | No | Yes |
| API format | OpenAI-compatible | OpenAI-compatible |
The generation API is identical in shape — the model parameter is the only required change in your code.
What does the pricing gap mean at production volume?
GPT-image-2 at $0.014/image is 53% less expensive than Nano Banana Pro at 1K/2K. That gap compounds quickly:
| Monthly volume | Nano Banana Pro (2K) | GPT-image-2 | Monthly savings |
|---|---|---|---|
| 1,000 images | $30.00 | $14.00 | $16.00 |
| 10,000 images | $300.00 | $140.00 | $160.00 |
| 100,000 images | $3,000.00 | $1,400.00 | $1,600.00 |
At 10K images/month, GPT-image-2 saves $160 — meaningful for early-stage products. At 100K+, it's a significant cost center decision.
One important nuance: Nano Banana Pro's 1K and 2K tiers are identically priced at $0.030. There is no cost penalty for defaulting to 2K — always request 2K unless bandwidth is a specific constraint. GPT-image-2 does not offer a higher-resolution tier on GenRelay; if 4K output is a requirement, Nano Banana Pro at $0.042/image is the only option in this comparison.
Which model produces better results?
The answer is use-case dependent. Neither model is strictly better; they excel in different areas.
Nano Banana Pro produces stronger photorealistic results — accurate rendering of materials (glass, leather, metal, fabric weave), reliable spatial relationships in complex scenes, and preserved fine detail at 2K and 4K. For e-commerce product photography, marketing hero images, and print-quality assets, the higher fidelity justifies the cost.
GPT-image-2 handles creative and illustrative styles well, shows stronger instruction adherence for text-in-image scenarios, and is well-suited to diverse prompt styles. For conceptual content, diagrams, illustrations, and high-volume background asset generation, GPT-image-2 delivers solid results at a significantly lower per-image cost.
| Use case | Recommended | Reason |
|---|---|---|
| Product photography (photorealistic) | Nano Banana Pro | Material and texture fidelity |
| Marketing hero images, complex compositions | Nano Banana Pro | Scene accuracy, color reproduction |
| 4K print-ready assets | Nano Banana Pro | Only model with 4K tier |
| Illustrative or conceptual content | GPT-image-2 | Creative range, style flexibility |
| Text rendered inside the image | GPT-image-2 | Stronger text instruction adherence |
| High-volume background/thumbnail generation | GPT-image-2 | Cost-efficient for simpler subjects |
| Prototype / prompt iteration | GPT-image-2 | Low iteration cost ($0.014) |
GPT-image-2 supports image editing — Nano Banana Pro does not
This is the sharpest functional difference between the two models. GPT-image-2 supports image editing via /v1/images/edits: you submit an existing image, an optional mask, and a prompt — the model fills the masked region or modifies the image accordingly (inpainting/outpainting). Nano Banana Pro is generation-only.
If your product includes user-uploaded image editing, background replacement, or "refine this area" interactions, GPT-image-2 is the correct choice regardless of price. This capability does not exist in Nano Banana Pro at all.
How do I call each model?
The generation API is identical for both — only model changes:
import requests
API_KEY = "grk_live_YOUR_KEY_HERE"
BASE = "https://genrelay.ai"
def generate_image(model: str, prompt: str, size: str = "1024x1024") -> str:
resp = requests.post(
f"{BASE}/v1/images/generations",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
},
json={"model": model, "prompt": prompt, "size": size, "n": 1},
)
resp.raise_for_status()
return resp.json()["data"][0]["url"]
prompt = "A ceramic coffee mug on a wooden table, warm morning light, shallow depth of field"
# GPT-image-2 — $0.014 per image
url_gpt = generate_image("gpt-image-2", prompt)
# Nano Banana Pro — $0.030 per image at 2K
url_pro = generate_image("nano-banana-pro", prompt, size="2048x2048")
print("GPT-image-2:", url_gpt)
print("Nano Banana Pro:", url_pro)
For GPT-image-2 image editing (inpainting with a mask):
def edit_image(image_path: str, mask_path: str, prompt: str) -> str:
with open(image_path, "rb") as img, open(mask_path, "rb") as mask:
resp = requests.post(
f"{BASE}/v1/images/edits",
headers={"Authorization": f"Bearer {API_KEY}"},
files={
"image": ("image.png", img, "image/png"),
"mask": ("mask.png", mask, "image/png"),
},
data={"model": "gpt-image-2", "prompt": prompt, "n": "1"},
)
resp.raise_for_status()
return resp.json()["data"][0]["url"]
# Replace product image background
url_edited = edit_image(
"product.png",
"mask.png", # White pixels = area for model to fill; black = preserve
"Replace background with a clean light grey studio gradient",
)
print("Edited:", url_edited)
The image and mask files must be PNG format with matching dimensions.
Can I use both models in the same pipeline?
Yes — this is a practical production pattern. Use GPT-image-2 for initial draft generation at $0.014/image, then render the approved version with Nano Banana Pro at higher resolution when the quality requirement demands it:
def draft_then_final(prompt: str) -> str | None:
# Fast, affordable draft
draft_url = generate_image("gpt-image-2", prompt)
if user_confirms(draft_url): # your UI confirmation function
# Final render at 2K with Nano Banana Pro
final_url = generate_image("nano-banana-pro", prompt, size="2048x2048")
return final_url
return None
# Draft: $0.014. Final: $0.030, paid only on confirmation.
# 5 prompts tested → 1 approved: $0.070 + $0.030 = $0.100
# vs generating Pro for every attempt: 5 × $0.030 = $0.150 — 33% saving on iteration
Editing flows (user-initiated inpainting or outpainting) always route through gpt-image-2 regardless of which model generated the original image.
FAQ
Do both models use the same authentication and endpoint?
Yes. Both call POST https://genrelay.ai/v1/images/generations with the same Bearer token from your GenRelay account. Change only model: "nano-banana-pro" or "gpt-image-2". Full setup details are in the Nano Banana Pro API guide and GPT-image-2 API guide.
Does GPT-image-2 support 2K or 4K output?
The current GenRelay offering for GPT-image-2 is at standard resolution (1024×1024). For 2K (2048×2048) and 4K (4096×4096) output, Nano Banana Pro is the available option.
Can I request multiple images per call (n parameter)?
Both models support generating multiple images per request using the n parameter. Each image is billed at the individual per-image rate regardless of batch size.
Which model handles complex multi-object scenes better?
Nano Banana Pro handles multi-element compositions — several distinct objects with specific spatial relationships — more consistently. For simple single-subject prompts, GPT-image-2 produces solid results at lower cost.
If I want to add image editing to my app, do I have to use GPT-image-2?
As of August 2026, yes. The /v1/images/edits endpoint on GenRelay is available for gpt-image-2 only. If editing (inpainting, outpainting, or region replacement) is part of your product flow, GPT-image-2 is required for those operations.
Summary verdict
| Requirement | Choose |
|---|---|
| Photorealistic product or marketing images | Nano Banana Pro |
| 4K resolution output | Nano Banana Pro |
| High-volume generation at low cost | GPT-image-2 |
| Image editing / inpainting / outpainting | GPT-image-2 |
| Rapid prototyping and prompt iteration | GPT-image-2 |
| Mixed pipeline (draft + final render) | Both |
Both models are available on GenRelay with no model-specific subscriptions and free credits on new accounts. The right model is determined by your output requirements — run your actual prompts through both before committing at scale.