Gemini Nano Banana 2 vs GPT Image 2 for Product Photos

If you are creating product photos with AI in 2026, two of the most relevant general-purpose options are Google's Gemini 3.1 Flash Image, better known as Nano Banana 2, and OpenAI's GPT Image 2. Both can generate new images and edit existing ones, but they are not identical tools.

For ecommerce sellers, the useful question is not which model wins overall. It is which workflow gives you the right kind of control for the job in front of you. A seller creating six consistent bracelet angles has different needs from someone making one lifestyle advert or correcting a background.

This comparison is based on the current official Google and OpenAI documentation as of August 16, 2026. It does not pretend that a documentation comparison is the same as a controlled image-quality benchmark.

1. Reference images: Gemini gives you unusually explicit multi-reference control

Google makes multi-reference workflows a central part of the Gemini 3 image family. Its current documentation says Gemini 3.1 Flash Image can preserve high fidelity for up to 10 object references in one workflow, with support for up to four character references as well.

That is useful for products whose important details are spread across several views. For a bracelet, you might provide a hero view, a clasp close-up and a side profile. For a pendant, you might add a rear view that shows the bail and chain connection. The point is not to upload 10 images every time. It is that the model can be given extra evidence when one photo does not fully describe the product.

OpenAI describes GPT Image 2 as supporting high-fidelity image inputs, but its current model page does not advertise the same kind of simple object-reference count. That makes Gemini's documentation easier to plan around when your workflow depends on several separate product references.

2. Editing: both are designed around preserving an existing image

For product sellers, image editing is usually safer than creating the item from text alone. If you already have an accurate product photograph, the model should be changing the scene around it rather than inventing the product again.

Gemini supports conversational, multi-turn image editing. You can begin with a product reference, ask for a new background, then continue refining the same result. Google's documentation says these workflows preserve visual context between turns.

GPT Image 2 is also explicitly positioned as an image-generation and editing model with high-fidelity image inputs. OpenAI's earlier GPT Image 1.5 release highlighted preservation of composition, lighting, logos and fine details for ecommerce catalogues, and GPT Image 2 is now the company's current state-of-the-art replacement for that model.

In practice, that means either ecosystem makes more sense when you start with the real product. The risky workflow is still asking a model to invent a commercially accurate bracelet, ring or watch from a text description and assuming the output matches what you sell.

3. Output control: Gemini is very explicit about resolution and aspect ratios

Gemini 3.1 Flash Image supports 1K, 2K and 4K output, plus a smaller 0.5K option, and Google documents a broad set of aspect ratios. That is useful when one product needs a square marketplace image, a 4:5 social advert, a vertical story creative and a wide banner.

OpenAI describes GPT Image 2 as supporting flexible image sizes. Its API is also built around explicit image-generation and image-editing endpoints, which makes it well suited to software workflows where image creation is part of a larger automated system.

For a normal seller using a chat interface, both are easy to work with. For developers building a repeatable catalogue pipeline, the exact API controls, costs and throughput limits matter more than the consumer interface.

4. Gemini has one unusual advantage: image and web grounding

Gemini 3.1 Flash Image can use Google Search grounding, including image-search grounding, when generating an image. For ecommerce work, this is not normally necessary for reproducing your own product, but it can be useful for context-heavy creative work.

Imagine creating a seasonal campaign that needs a current visual reference, a location-specific setting or a design informed by live information. Grounding gives Gemini a route to retrieve that context before generating. You should still avoid letting web references override the real product reference itself.

GPT Image 2's model page focuses more directly on image generation, editing and high-fidelity inputs. If your task is simply to turn a real product photo into a new commercial scene, web grounding may not add much anyway.

5. Product accuracy still has to be checked manually

Neither company's documentation gives sellers a guarantee that a generated commercial image will reproduce every product detail perfectly. That is especially important for jewellery, where an image can look convincing while quietly changing the clasp, stone count, chain path, engraving or thickness.

Whichever model you use, compare the final image with the real product at full size. Check details customers could reasonably use to judge what they are buying. If a result is visually attractive but materially inaccurate, it is not a successful ecommerce image.

So which workflow should a seller choose?

Gemini 3.1 Flash Image is particularly attractive when you want to feed the model several product references, need very explicit 2K or 4K output control, or want to use Google's grounding features as part of the creative process.

GPT Image 2 is a strong fit when your workflow centres on high-fidelity editing of an existing source image or when you are building around OpenAI's current image API. OpenAI specifically positions it as its state-of-the-art model for fast, high-quality generation and editing.

For most sellers, the better habit is to test the same real product and the same brief in both tools rather than relying on general claims about which model is best. Use one sharp primary reference, add complementary angles only when they reveal missing geometry, and score the outputs on product fidelity before judging the background or styling.

This is also where structured tools such as Lustra Studio can help. A specialist jewellery workflow can standardise the references, poses and colour-change instructions instead of making you rebuild the setup from scratch for every image. General image models provide flexibility. Repeatable product workflows provide consistency.

One important change happening now

Google's older Imagen models are being shut down on August 17, 2026, and Google now recommends Nano Banana models for image-generation work. If you still have a workflow built around Imagen, this comparison is therefore not just theoretical. The practical migration path is toward Gemini's native image models.

Sources: Google AI for Developers, Gemini image generation documentation and Gemini 3.1 Flash Image model page, updated July 2026; OpenAI Developers, GPT Image 2 model page, accessed August 2026; OpenAI, ChatGPT Images 2.0 announcement, April 21, 2026.