Which AI Image Model Should You Use?

The available models share one prompt box here, and the switch between them is a dropdown rather than a new account. That makes the interesting question not which model is best — none of them is, across the board — but which one to spend credits on for the picture in front of you.

Start with the image's requirements, choose a model, and check the quote before generating.

Choosing a model

• Nano Banana Pro — for detailed compositions and work with reference images.
• GPT Image 2 — for specific briefs and structured layouts.

The model directory lists the current choices and their examples.

Nano Banana Pro — the hard-shots model

Google DeepMind's heavier model is the one that survives the things that break other models: legible text (including non-Latin scripts), dense compositions where thirty things have to stay coherent, and fine detail like engraving, small print and distant faces. It also holds a subject consistent across scenes, which is what makes it the pick for character work with reference images.

GPT Image 2 — the literal-minded one

OpenAI's model reads a long, specific brief and renders what the brief says. Three objects means three. "Leave room for a headline" leaves room. That makes it the choice for layouts, covers, diagrams, labelled charts and the clean e-commerce product shot most models over-decorate — anywhere the structure matters more than the photorealism.

A workflow that wastes fewer credits

1. Start with a focused draft. Choose an available model and check its quote. You're testing composition and direction, not finishing anything.
2. Keep the prompt, not the picture. The output that came closest tells you which wording worked.
3. Compare the winning prompt on another model if needed. Nano Banana Pro if the image has words or dense detail; GPT Image 2 if it has a layout to respect.
4. Iterate with a reference. Feed the result back into image-to-image and push it one step further.

The current composer quote depends on the model, resolution, reference images and output count. It is visible before submitting; check it again when changing settings or moving to image-to-image.

Things the workflows share

• The same aspect ratios: 1:1, 16:9, 9:16, 4:3, 3:4 — picked before generating, so you're not cropping afterwards.
• The same balance of credits. No separate subscription, no API key to paste, no GPU.
• A generation that fails isn't charged.
• Everything you make is saved to your library with the prompt and the model it came from.

Check your available account credits and the composer quote before generating. Compare the models or just start with a prompt.