Image model showdown

Compare

Same prompt, five top image generators — see who wins your style

Find which image model "gets" your style

5-column Gab AI Deck recipe for direct image model comparison

Image Model Showdown runs the same prompt through GPT Image 2 Nano Banana, Qwen Image, Wan Image, and Imagen 4 simultaneously. The deck surfaces five interpretations of your brief — different lighting, composition, typography handling, and texture biases — so you can pick a model per project (or per brand) with evidence instead of vibes.

How to use this recipe

  1. Click "Use this recipe" to clone the 5-column deck.
  2. Paste the exact same prompt into all five columns — every word matters; do not adjust per model.
  3. Run all five in parallel and compare side by side. Score each on subject accuracy, photorealism (or stylisation), typography, and prompt adherence.
  4. Pin the winner; for the runner-up, re-prompt with notes from the winning column to test whether it can match.
  5. Save the deck as a recipe; reuse it whenever you onboard a new client or test a new prompt formula.

Best for

Image Model Showdown FAQ

Why these five image models?

They span the major styles: Imagen 4 (photoreal + clean shapes), Nano Banana (stylised + textured), Qwen Image (artistic + composition-strong), Wan Image (typography-strong), Imagen 4 (vector-friendly). Swap any column to a different model in the catalog via the header.

Should I tweak the prompt per model?

Not for the comparison run — the point is to see how each model interprets the same words. After you pick a winner, fork the deck to iterate on prompt phrasing for that specific model.

How do I compare typography?

Spec a logotype or text-heavy prompt to see how each model handles letterforms. Qwen Image 2 and Seedream 4.5 typically lead; Imagen and Gab Image Generator can be hit-or-miss.

Can I include Nano Banana?

Yes — open the column rail and add a sixth column bound to Nano Banana 2 (or any image model). The recipe is a starting point.

How many seeds should I generate per column?

Generate 2–4 per column to control for randomness. The variation between seeds within a model can be larger than between models — keep that in mind when scoring.

Will the upscale stage hurt the comparison?

Compare at native resolution first; only upscale the chosen winner so you are not biasing on Topaz settings.

Workflow columns