OpenAI's current image model is gpt-image-1, available through the API and via ChatGPT's native image generation (powered by GPT-4o). There is no publicly released "GPT Image 2" as of mid-2025, so this tutorial covers the latest available version and the workflows marketers actually use to produce ad creatives with it. If you're searching for the newest GPT image capabilities, this is where the real output lives.

Why Should Marketers Care About gpt-image-1?

The model handles text rendering inside images better than any competing image generator. That single capability changes the game for ad production because you can generate hero banners, social cards, and product overlays with accurate headlines baked directly into the image. No Photoshop text layer, no post-production. FLUX Kontext and Midjourney still struggle with multi-line text at smaller sizes, while gpt-image-1 renders it correctly the majority of the time.

The model also follows complex layout instructions with surprising reliability. You can specify "product bottle on the left third, headline top-right, tagline bottom-center" and get a composition that approximates what a junior designer would deliver.

Step-by-Step Workflow for DTC Ad Creatives

1. Set your output parameters

If you're using the API, set size to 1536x1024 for landscape ads (Meta feed, display) or 1024x1536 for Stories and Reels. Set quality to high for production assets. The medium setting renders faster but produces visible artifacts in gradients and skin tones.

2. Structure your prompt in layers

The most reliable prompt format for marketing assets follows this order:

  • Scene description (what the camera sees)
  • Product placement (where the product sits, what angle)
  • Text content (exact copy, placement, approximate font style)
  • Style and lighting (photographic style, color temperature)

Example prompt:

Professional product photograph of a matte-black skincare bottle centered on a marble surface, soft directional light from the upper left, shallow depth of field. Bold white sans-serif text reading "YOUR SKIN DESERVES BETTER" positioned across the top third. Smaller text at the bottom reading "Shop now at glowskin.co". Clean, editorial, high-end beauty advertising aesthetic.

3. Use reference anchoring for brand consistency

When working through ChatGPT, upload an existing brand asset or moodboard image and instruct the model to match its color palette and composition style. This produces more consistent output across a campaign than relying on text descriptions of colors alone. Phrases like "match the warm amber tones and minimal negative space of the attached reference" work well.

4. Iterate with targeted edits

Instead of re-prompting from scratch, use follow-up messages to adjust specific elements. "Move the headline lower and make the background 20% darker" will preserve the overall composition while refining layout. This conversational editing loop is where ChatGPT's image generation pulls ahead of standalone tools like Midjourney, where each variation is a fresh roll of the dice.

5. Batch variations for A/B testing

Generate 3-4 color or copy variations of your strongest output. Change the headline, swap background color, or shift product angle. Each variation takes seconds, giving you a testing matrix that would take a designer hours to produce manually.

What Fails and How to Fix It

Small text below ~20pt equivalent still garbles occasionally. Keep body copy to short phrases, and add fine print in post-production.

Logos are unreliable. The model will approximate your logo if you upload it as a reference, but letter spacing and vector precision break down. Always composite logos in Figma or Canva afterward.

Photorealistic hands holding products remain inconsistent. If you need a hand-in-frame shot, expect to generate 5-8 attempts before getting correct finger anatomy. Cropping at the wrist often solves the problem faster.