Multi-Model Image API Comparison 2026: Nano Banana Pro, Nano Banana 2, GPT-image-2
You're building a product that generates images programmatically — product photos, avatars, in-app visuals — and you need to choose an image model API. Three models are available on GenRelay today: Nano Banana Pro, Nano Banana 2, and GPT-image-2. Each has a different cost curve, different resolution ceiling, and different strengths. This article breaks down all three so you can pick the right one without trial and error.
Quick comparison at a glance
| Model | 1K price | 4K price | Inpainting / editing | Best for |
|---|---|---|---|---|
| Nano Banana Pro | $0.030/image | $0.042/image | No | Photorealistic visuals, high-res renders |
| Nano Banana 2 | $0.020/image | $0.036/image | No | High-volume generation, cost-sensitive workloads |
| GPT-image-2 | $0.014/image | $0.014/image | Yes | Editing, iteration, mixed text-image tasks |
Pricing above reflects GenRelay as of August 2026. All three models are accessed through the same GenRelay endpoint — https://genrelay.ai/v1/images/generations — so you can A/B test or switch models by changing a single field in your request body.
What is a multi-model image API, and why use one?
A multi-model image API — like GenRelay — is a unified interface that lets you call different image generation models without managing separate accounts, authentication flows, or SDKs for each provider. You authenticate once, use one endpoint, and switch models by name. This matters when you want to:
- Run cost-vs-quality experiments across models without rebuilding your integration each time.
- Use the cheapest model for drafts and a higher-quality model for final renders.
- Fall back to a different model if one provider is degraded.
GenRelay exposes Nano Banana Pro, Nano Banana 2, and GPT-image-2 behind a single OpenAI-compatible interface, so switching between them is a one-line change.
Nano Banana Pro — high-resolution photorealism
Nano Banana Pro is Google's flagship image generation model available on GenRelay. It produces photorealistic output and supports resolutions up to 4K.
Pricing:
- 1K resolution: $0.030/image
- 2K resolution: $0.030/image
- 4K resolution: $0.042/image
The 1K and 2K price points are identical at $0.030, which makes 2K a strong default for most use cases — you get meaningfully more pixels at no extra cost. The jump to 4K adds $0.012 per image, justified when your output will be printed or displayed at large sizes.
When to use Nano Banana Pro:
- Product photography backgrounds with fine detail
- Architectural renders or interior visualization
- Any output that will be cropped, zoomed, or displayed at print resolution
Nano Banana Pro does not support inpainting or image editing — it generates new images from text prompts only. For editing workflows, see GPT-image-2 below.
See the full Nano Banana Pro API guide for parameter reference and batch generation examples.
Nano Banana 2 — affordable high-volume generation
Nano Banana 2 is the second-generation model in Google's Nano Banana family, optimized for throughput and cost efficiency.
Pricing:
- 1K resolution: $0.020/image
- 4K resolution: $0.036/image
At $0.020 per 1K image, Nano Banana 2 is the most affordable model on GenRelay's image lineup — 33% less than Nano Banana Pro at the same resolution. For workloads generating thousands of images per day, that difference compounds quickly.
Example cost comparison — 10,000 images at 1K:
- Nano Banana 2: $200
- Nano Banana Pro: $300
- GPT-image-2: $140
For a startup generating social media visuals or e-commerce thumbnails at scale, Nano Banana 2 hits the best price-to-output ratio when your use case doesn't require the absolute highest image fidelity.
When to use Nano Banana 2:
- High-volume thumbnail or preview generation
- A/B testing creative variations at scale
- Pipeline stages that are later filtered before human review
GPT-image-2 — editing, iteration, and text-in-image
GPT-image-2 is OpenAI's image generation model available via GenRelay. Its flat price per image — $0.014 regardless of resolution — is the lowest per-image cost of the three models. More importantly, it is the only model here that supports inpainting and image editing.
Pricing: $0.014/image (flat, resolution-independent)
Editing capabilities:
- Inpainting: mask a region of an existing image and regenerate it with a new prompt
- Outpainting: extend an image beyond its borders
- Variation: generate variations of a source image
These editing capabilities make GPT-image-2 the right choice for any workflow where users provide input images and expect the model to modify or extend them — not generate from scratch.
See the GPT-image-2 API guide for the full editing endpoint reference.
When to use GPT-image-2:
- Photo retouching or object removal pipelines
- UI mockup iteration (user uploads, then requests changes)
- Backgrounds removal or replacement on user-provided assets
- Any use case where users need to interact with an existing image
How to call all three via GenRelay
All three models use the same endpoint and the same authentication. Switching models is a single field change:
import requests
API_KEY = "grk_live_YOUR_KEY_HERE"
def generate_image(prompt: str, model: str, size: str = "1024x1024") -> str:
response = requests.post(
"https://genrelay.ai/v1/images/generations",
headers={"Authorization": f"Bearer {API_KEY}"},
json={
"model": model, # "nano-banana-pro" | "nano-banana-2" | "gpt-image-2"
"prompt": prompt,
"n": 1,
"size": size, # "1024x1024" | "2048x2048" | "4096x4096"
"response_format": "url"
}
)
return response.json()["data"][0]["url"]
# Try all three on the same prompt:
prompt = "modern studio product photo of a glass perfume bottle on white background"
url_pro = generate_image(prompt, "nano-banana-pro", "2048x2048")
url_v2 = generate_image(prompt, "nano-banana-2", "1024x1024")
url_gpt = generate_image(prompt, "gpt-image-2")
print(url_pro, url_v2, url_gpt)
For the GPT-image-2 inpainting endpoint:
import base64, requests
def inpaint_image(image_path: str, mask_path: str, prompt: str) -> str:
with open(image_path, "rb") as img_f, open(mask_path, "rb") as mask_f:
response = requests.post(
"https://genrelay.ai/v1/images/edits",
headers={"Authorization": f"Bearer {API_KEY}"},
files={
"image": img_f,
"mask": mask_f,
},
data={
"model": "gpt-image-2",
"prompt": prompt,
"n": "1",
"size": "1024x1024"
}
)
return response.json()["data"][0]["url"]
Which model should you use?
| Scenario | Recommended model |
|---|---|
| Maximum image quality, hero visuals | Nano Banana Pro (2K or 4K) |
| High-volume generation, cost is priority | Nano Banana 2 (1K) |
| User uploads an image and requests edits | GPT-image-2 |
| Building a multi-step iterate-until-approved flow | GPT-image-2 |
| Draft thumbnails before a final high-res render | Nano Banana 2 → Nano Banana Pro |
| Tight budget, quality acceptable at lower fidelity | GPT-image-2 ($0.014 flat) |
A common production pattern: use GPT-image-2 for the interactive draft loop (cheap, supports edits), then pass the approved composition to Nano Banana Pro at 4K for the final render. The full flow is two API calls, both via GenRelay.
For a detailed cost breakdown by workload volume, see the AI image generation API pricing comparison.
FAQ
Can I use all three models under a single GenRelay account?
Yes. Your API key works across all models. Usage is billed per generation and tracked in your GenRelay dashboard regardless of which model generated each image.
Does GenRelay support batch image generation?
Yes. Pass "n": 4 (or up to the model's maximum) in the request body to generate multiple images in a single API call. Results are returned as an array under data.
What resolution strings are valid for each model?
Nano Banana Pro and Nano Banana 2 accept "1024x1024", "2048x2048", and "4096x4096". GPT-image-2 accepts "1024x1024" and "1792x1024" (landscape). Always pass size as a string; the API rejects integer inputs.
Is there a rate limit per model?
GenRelay enforces per-account rate limits, not per-model limits. You can see your current quota in the dashboard. Pro plan accounts have higher concurrent generation limits than free-tier accounts.
What happens if an image generation fails?
Failed generations are not charged. The API returns a non-200 status code with an error object. Common causes: prompt content policy rejection, invalid size value, or temporary upstream capacity limits. Implement exponential backoff for retries on 503 errors.