Which AI Image Model Should You Use?

Three 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.

Short version: draft on the cheap one, finish on the expensive one.

The three models

• FLUX Schnell — 1 credit an image, up to 4 per run, text prompts only. For exploring an idea.
• Nano Banana Pro — 16 credits an image, one per run, up to 3 reference images. For the hard shots.
• GPT Image 2 — 24 credits an image, one per run, up to 3 reference images. For when the brief is specific.

FLUX Schnell — the draft model

Black Forest Labs' distilled model returns a usable image in very few steps. At a credit a render and four images per run, trying five directions costs less than a single image on either of the other two. That economics is the point: explore here, commit elsewhere.

Its weakness is words. Short text sometimes lands, but if the wording has to be correct — a poster headline, a product label — this isn't the model.

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.

One image per run, six credits.

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.

Eight credits, one image per run.

A workflow that wastes fewer credits

1. Draft on FLUX Schnell. Four images, four credits. 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. Re-run the winning prompt on the heavy model. 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.

Twenty images of exploration plus two GPT Image 2 finished renders comes to 68 credits — a controlled way to spend the high-quality budget on selected ideas.

Things that are true of all three

• 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.

New accounts start with free credits, which is enough for a real handful of FLUX Schnell images and a look at the other two. Compare the models or just start with a prompt.