Every model comparison you read is out of date within a quarter, so the useful version is not a scoreboard — it is a description of what each engine does when your prompt is hard. Those tendencies change much more slowly than the rankings do. Here is how the three we publish with behave on the same wording.
| gpt-image-2 | FLUX 2 Pro | Nano Banana 2 | |
|---|---|---|---|
| Strongest at | Following long, multi-clause instructions; legible text in-image | Photographic realism — skin, fabric, atmosphere, believable light | Speed, and editing an image you already have |
| Typical weakness | Can look slightly clean and illustrative on "real photo" briefs | Drifts from the letter of a complex prompt toward what looks good | Less depth on very elaborate scene descriptions |
| Reach for it when | The image has to say something specific, or carry words | You want it to be mistaken for a photograph | You are exploring, or changing one thing in an existing frame |
The practical decision
- 1.Does the image contain readable text, a specific object count, or a precise arrangement? Start with gpt-image-2 — instruction adherence is the constraint.
- 2.Does it need to pass as photography? Start with FLUX 2 Pro, and spend your prompt budget on light rather than on adjectives.
- 3.Do you not yet know what you want? Explore on Nano Banana 2, find the composition, then re-run the settled prompt on whichever of the other two matches the finish you need.
That last workflow is the one most people arrive at eventually: cheap fast iteration to find the picture, one careful expensive pass to make it. Treating the models as a pipeline rather than as rivals is what makes the differences useful instead of academic.
The same prompt is not the same picture
A prompt tuned on one engine is a draft on another. Style words in particular do not transfer cleanly — a phrase that reads as "editorial" on one model reads as "stock photo" on the next. When you move a prompt across engines, expect to re-tune the style and lighting clauses and leave the subject and composition clauses alone.
On this site the model used is recorded per generation and shown on the page, so the model browser is a like-for-like reference rather than a claim: same kind of subject, different engine, visible result.
- Browse by model — See the same kind of prompt across all three engines we publish with
- When a reference image beats a better prompt
- Holding one style across a set