Which AI image model should you actually use?

Every model in KilGen can make a competent image. The gap between them shows up in the specifics: how well they hold a face, whether they can spell, how they handle a photograph you hand them versus a description you type.

Start with the task, not the model

The first question is not "which model is best" but "what am I giving it". Every model page in KilGen is split into two tasks:

• Text to Image — you describe it, the model draws it. Nothing else goes in.
• Image to Image — you hand it a photo and the model transforms it.

A model that is excellent at one is often mediocre at the other. Filtering by task first cuts the list roughly in half before you have compared anything.

The short version

Photorealism from a description. Flux and Imagen hold up best. Skin, fabric and natural light survive close inspection in a way that stylised models do not manage.

Editing a photo you already have. Nano Banana and the Qwen edit models are built for it. They keep the parts of the image you did not ask about, which is the whole difficulty of image editing — most models quietly redraw the background too.

Text inside the image. Ideogram and GPT Image. If your image has a sign, a label, a poster or a logo in it, use one of these. Other models produce text-shaped smudges.

Keeping a person recognisable. Anything labelled Image to Image, fed a clear, well-lit, front-facing photo. This depends far more on your input than on the model.

Speed and volume. The Fast and Lite variants. They cost fewer credits and land in seconds. Use them to explore a direction, then re-run the one you like on the full model.

Cost is part of the answer

Every model in the picker shows its credit cost before you run it. A four-times-more-expensive model is not four times better — it is usually better at one specific thing. If that thing is not what you need, you are paying for nothing.

A cheap habit: draft on a Fast model until the prompt is right, then spend the credits once on the good model. Prompt problems are much more common than model problems, and they are far cheaper to find this way.

Just try both

Model choice is not a decision you have to get right in advance. Run the same prompt through two models, put the results side by side, and you will know more in ninety seconds than any comparison article can tell you. The picker keeps your prompt when you switch.