Gemini vs ChatGPT Images vs Nano Banana: Which One Actually Keeps Your Face? (2026)

There are three names that come up constantly when people search for how to turn a selfie into a styled photo: Google Gemini, ChatGPT Images, and "Nano Banana" — Google's own internal nickname for the Gemini image model that stuck hard enough it now shows up in search more than the official name does. People searching for any one of these are usually trying to answer the same underlying question: which one actually preserves my face instead of generating a stranger who looks vaguely like me.

The honest answer is that it depends on what you are asking the model to change, and each of the three breaks in a different place.

Gemini / Nano Banana

Gemini's image model — Nano Banana is the same thing — is built around conversational editing: you upload a photo, describe a change, and it edits rather than regenerates from scratch. That architecture is why it tends to hold facial structure better than a pure text-to-image model when the request is a wardrobe or setting change. It is also why it struggles with bigger asks — change the pose entirely, change the lighting dramatically — because editing-in-place does not have the same freedom a full regeneration has. The specific mechanics of getting a face to hold through a Gemini edit are covered in how to use Google Gemini to generate photos with your face, and the broader walkthrough is in the complete Gemini tutorial.

ChatGPT Images

OpenAI's image model takes the opposite approach: it regenerates the scene more fully from the text prompt, using the reference as a strong style and likeness anchor rather than an editable base layer. That gives it more freedom on dramatic changes — full scene replacement, pose changes, lighting changes — at some cost to the finest facial details on a first pass. The September 2026 Images 2.5 update specifically improved reference-photo preservation, which narrowed this gap; the details are in what actually changed for photos of yourself.

The Actual Difference That Matters

Strip away the branding and the real variable is editing versus regeneration. An editing-first model (Gemini/Nano Banana) is the better choice when the reference photo is already close to what you want and you are changing one or two things — outfit, background, lighting. A regeneration-first model (ChatGPT Images) is the better choice when you are asking for something further from the original — a completely different setting, pose, or scene — because it is not constrained to edit around the existing pixels.

Neither is "more accurate" in the abstract. They are accurate at different kinds of requests.

A Test You Can Run in Five Minutes

Pick one reference photo. Ask for the same small change — "same person, same pose, change the shirt to a navy crewneck" — in both tools. Then ask for the same large change — "same person, now standing on a rooftop terrace at golden hour" — in both. The small-change result will usually favor Gemini; the large-change result will usually favor ChatGPT. That five-minute test tells you more about which tool fits your actual use case than any general ranking does, because "which is better" depends entirely on how far you are asking the model to travel from the source photo.

Is Gemini Free to Use This Way?

Google's consumer apps give limited free generations per day before requiring a paid plan; the exact mechanics and whether it can actually generate from a text prompt alone (versus only editing an upload) are covered in can Google Gemini generate images.

Where PROMPTMVSTR Fits In

The prompts in the archive are written to work across both architectures — editing-style and regeneration-style — because the wardrobe, lighting, and composition language that makes a photo look real is the same regardless of which model is holding your face steady. If you want to skip picking a tool and just get the output, the Virtual Photoshoot handles the model selection for you.