Test the workflow,
not the marketing claim.
Batch systems should respond to provider limits without dropping job provenance or creating retry storms, so this guide separates admission control, worker concurrency, backoff, and accepted-output accounting across slow image requests.
Premium whole-bean coffee pouch standing on dark walnut, uncoated cream paper with a small black label area, morning side light, restrained props, realistic folds, front panel fully visible, no invented text.
Smart speaker studio
Compact smart speaker in graphite fabric on a deep charcoal pedestal, subtle violet rim light, precise mesh texture, controlled reflections, high-end consumer electronics launch photography, centered composition.
Summer drink social ad
Bright summer beverage can on crushed ice, coral and turquoise color blocking, hard flash photography, realistic condensation, product occupies the lower two-thirds, clean space above for campaign copy, no text.
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,
)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.