GPT Image vs Nano Banana: Generation and Editing Tests

WHY THIS GUIDE EXISTS

Test the workflow,
not the marketing claim.

Helps teams separate new-image generation from reference editing and test the workflows each model is actually expected to perform in production.

Characters

Forest ranger character sheet

Character sheet for Mara, a forest ranger with cropped auburn hair, olive field jacket, brass compass and worn canvas backpack; front, profile and three-quarter views; neutral studio background; consistent facial features.

Identity consistency fixture3:2

Editing

Product background edit

Keep the bottle shape, cap, label geometry and camera angle identical; replace the white studio background with pale travertine and add one soft olive branch shadow from the upper left.

Campaign scene variation1:1

Ecommerce

Ceramic skincare hero

Editorial product photograph of a matte ceramic skincare bottle on pale limestone, soft north-window light, delicate contact shadow, warm neutral palette, 85mm lens compression, no text, no logo.

Clean premium product hero1:1

PYTHON QUICKSTART

Keep the request reproducible.

Store credentials in the environment. Record the exact model identifier, dimensions, test date, and retry count with the resulting asset.

import os
from openai import OpenAI

client = OpenAI(
  api_key=os.environ["APIMART_API_KEY"],
  base_url="https://api.apimart.ai/v1",
)

result = client.images.generate(
  model="gpt-image-1-official",
  prompt=PROMPT,
  size="1024x1024",
  n=1,
)

REVIEW CHECKLIST

Before you publish a result

Record model ID, parameters, date, latency, and retry count.
Review instruction following and protected attributes.
Flag invented text, marks, anatomy, or unwanted objects.
Keep the raw output linked to the original fixture.
Do not claim a universal winner from one prompt.