Nano Banana vs GPT-Image-1 vs Seedream 4: The 2026 Image Model Benchmark for Product Ads

If you've shipped a single ad creative in the last six months, you've felt it: the image-generation landscape is moving faster than any of us can keep up with. Three models now dominate the conversation for ad creative — Google's Nano Banana, OpenAI's GPT-Image-1, and ByteDance's Seedream 4 — and they each have very different strengths.

We ran 200 real e-commerce product shots through all three, with the same prompt, the same source photo, and the same aspect ratio. Here's what we found.

TL;DR — which model should you use for ads?

How we tested

We picked 20 real Shopify product URLs across 5 verticals — beauty, supplements, apparel, home goods, and pet — then generated 10 variations per product on each of the three models. Same prompt, same input image, 1080×1350 (4:5 ratio for Meta feed). Total: 200 generations × 3 models = 600 images.

Each output was scored blind by 3 marketers on:

1. Identity preservation — does it still look like the product?
2. Brand fidelity — does it match the source brand's color palette and tone?
3. Ad-readiness — would you ship it without retouching?

ModelCard rundown

What surprised us

1. Nano Banana is freakishly good at "keep the product, change everything else"

If you give Nano Banana a clean product photo and a scene description, it preserves the geometry of the product almost perfectly. The bottle silhouette, the label position, the cap shape — it doesn't drift the way Midjourney or Flux do.

This is the killer use case for performance ads: you keep your real product, the model only invents the world around it.

2. GPT-Image-1 is the only model that still nails text inside the image

"30% off this weekend only" rendered cleanly across 9 out of 10 generations.

For comparison, Nano Banana hit it 5/10, and Seedream 4 hit it 4/10. If your ad concept depends on a hook headline baked into the image, GPT-Image-1 is still the safest bet.

3. Seedream 4 is shockingly cheap and shockingly fast

Average generation time on Seedream 4 was 2.1 seconds at our resolution, vs. 5.8s for Nano Banana and 9.4s for GPT-Image-1. At 600 generations, that's the difference between "we'll do this overnight" and "we'll do this on a coffee break."

Cost per 1,000 ad creatives (May 2026 pricing)

If you're a brand running a Meta + TikTok testing factory, those numbers compound fast.

What this means for your ad workflow

The old playbook — pick one model, write one prompt, ship — doesn't work anymore. Each model has a clear comparative advantage. The brands winning in 2026 are routing prompts to the right model based on the creative concept:

• Hero product shot for the feed? → Nano Banana with the real product image as reference.
• Promo with on-image text? → GPT-Image-1.
• Bulk lifestyle variations? → Seedream 4.

That's exactly what AdFrame does under the hood. We learn your Brand DNA from one product URL, then route every ad concept to the model that's best for it — so you don't have to think about which API to call.

Methodology notes

• All three models were called with the same source photo and 1:1 prompt.
• Aspect ratio: 4:5 (1080×1350).
• Negative prompts were left blank to avoid biasing any model.
• Scoring was double-blind (raters didn't know which model produced which image).
• Pricing reflects published API rates as of May 2026.

We'll re-run this benchmark every quarter as the models evolve. Subscribe to the AdFrame newsletter to get the next one in your inbox.