Technical Context
I came across a seemingly minor comment: GPT transfers facial features better than Nano Banana Pro, especially freckles, wrinkles, and other fine details. This isn't just a matter of taste; it's a practical question for AI implementation: which model should you use in a production pipeline when you can't afford to just 'get close' to the right face.
If you look at public comparisons from 2025, the picture isn't so clear-cut. On paper, Nano Banana Pro has strong advantages: higher resolution, better photorealism, and stronger consistency between frames, especially when using multiple references. This is significant for image series and repeatable characters.
But specifications and real-world tasks often diverge. I've seen many instances where a model with impressive metrics maintains a great overall image but starts to 'erase' the face's personality: the shape of the eyelids, the pattern of wrinkles, the density of freckles, the character of the nasolabial folds. And that's what users notice first.
The GPT approach has an interesting advantage here: it often handles local features more cautiously, provided the prompt and reference are well-chosen. Not always. But for tasks requiring the transfer of not just a 'woman in her 40s' but a specific face with recognizable skin texture, I wouldn't dismiss GPT as purely a generative 'fantasy' tool.
To be fair, there are no recent official industry benchmarks specifically for freckles and wrinkles. There are third-party tests on realism, FID, adherence, and consistency, and they usually favor Nano Banana Pro. But I find the user's comment plausible because these kinds of details are best revealed in real-world use cases, not in spreadsheets.
Impact on Business and Automation
If you're using AI automation for character catalogs, advertising, beauty, fashion, or personalized content, errors in facial details quickly lead to extra iterations. The team spends time on manual tweaks to achieve resemblance rather than on creative work.
Who benefits from Nano Banana Pro? Those who need high series stability, 4K resolution, and repeatability across scenes. Who wins with GPT? Those for whom the delicate transfer of identity in a single frame or a short chain of edits is critical.
I wouldn't choose 'the best overall model' here. I would build a hybrid system tailored to the task: one model for series consistency, and another for precise face transfer and the final pass. At Nahornyi AI Lab, we specialize in tackling these bottlenecks. If your generation process stumbles on faces, let's look at the entire workflow and build an AI solution development plan without guesswork and endless revisions.