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Nano Banana 2.1 vs GPT Image 2.5

Compare image shapes, output-size and quality controls for the picture you want to make.

Describe what to make with Image
≈ – credits
Try a prompt:

Examples are being prepared.

When to pick which

Specs side by side

SpecNano Banana 2.1GPT Image 2.5
ModesText to Image, Image EditorText to Image, Image Editor
Resolution1K, 2K, 4K—
Aspect ratiosAuto, 21:9, 16:9, 9:16, 3:2, 2:3, 4:3, 3:4, 5:4, 4:5, 1:1, 4:1, 1:4, 8:1, 1:8Auto, 1:1, 4:3, 3:4, 16:9, 9:16
Reference imagesUp to 9Up to 9
Output images per request1–41–4
Quality—Low, Medium, High, Auto

About

Nano Banana 2.1 and GPT Image 2.5 both generate new images, edit supplied pictures and accept up to nine references. Both can request up to four output images. Their controls differ: Nano Banana 2.1 offers explicit 1K, 2K and 4K settings and a wide choice of aspect ratios; GPT Image 2.5 offers low, medium and high quality settings across its supported shapes. For a cover, thumbnail or illustration, choose a frame and output setting that suit its destination, then compare how the images follow the same brief. Check lettering and recognizable details in the actual result rather than assuming a winner from the model name.

Questions

How should I choose between the models?

Start with the kind of image you need and a few details you can inspect. Compare how each actual output handles the subject, framing, object relationships and requested words. Decide which result fits the project, with its prompt and settings in view.

How can I compare the two models fairly?

Use the same brief for Nano Banana 2.1 and GPT Image 2.5. Keep each actual output with its model name and settings, then compare the subject, framing and requested details against the brief.

What makes a useful spatial-detail test?

Describe a small number of objects and their positions clearly, such as a bottle at the back left and a sphere in front of it. Check the object count, placement, relative size and overlap in each image. Include only relationships that matter to the picture.

Can I compare an edit using the same source image?

An edit comparison needs the same source image and a change request supported by both models. Keep the source, the requested change and each model’s actual input settings with the two outputs. Inspect both the requested change and the details you asked to preserve.

How do I keep settings comparable?

Use a frame shape both models support and record the options actually submitted for each request. If the available controls differ, note that difference instead of treating similarly named settings as equivalent. Keep any conclusion specific to the images and settings being compared.