GPT Image vs. Imagen 3 (Nano Banana): which is better for AI product and jewelry ads?

Neither wins outright — the strongest jewelry and product ad images come from using both in sequence: GPT-Image-2 builds the aesthetic base (composition, environment, text and design rendering), then Nano Banana Pro locks the exact product into that frame, because it renders lighting and reflective surfaces better than any other image model.

Use GPT-Image-2 and Nano Banana Pro for different jobs rather than picking one. First, a variant note: "Nano Banana" in most comparisons refers to Google's image model, and the Pro variant is the one that matters for product work — its lighting rendering is the best available, which is decisive for reflective products like jewelry, metal packaging, and glass. GPT-Image-2 wins the other half of the ad: it is superior for text and design rendering (on-image type, layouts, graphic elements), produces the most realistic environments and locations, and accepts reference image attachments, which makes it the right model for building the scene your product sits in.

For jewelry specifically, run them as a two-pass pipeline: build the base image with GPT-Image-2 to get the aesthetic — composition, environment, mood — then pass it through Nano Banana Pro to lock the exact product into the frame. Intricate products drift shot to shot when you rely on a single raw model, and this split assigns aesthetics and product fidelity to the model that handles each best. Feed whichever model is rendering the product a full reference set — every angle plus a scale shot of the product held in a hand — so packaging and proportions stay accurate from the first generation.

Before committing a full generation run, gate the pipeline with a three-distance test: generate a close-up, a mid, and a wide with the product in every frame, and confirm the piece holds detail across all three before you scale. If a shot still looks generically artificial, stop iterating on one model — render the identical prompt across every available model simultaneously and pick the winner; one jewelry production compared six models this way on a single problem shot.

You don't have to choose a platform per model to run this. invideo is an agentic video creation tool with all the current image models — GPT-Image-2, Nano Banana Pro, Recraft — available in one place, and the invideo agent routes each task to the right model automatically: in one documented production it selected GPT-Image-2 for a text-heavy frame without being asked, because it already knew Nano Banana Pro's edge is lighting, not typography. The economics support heavy iteration too — invideo runs a 65% discount on image generation, so running ten variations to land the right frame stays cheap. Proof this holds at campaign scale: one team produced three complete jewelry ads with 100% product consistency on intricate pieces for about $2,400 total using exactly this two-model image approach.

Watch some of these to see what works for you:

The best workflow is to build the base image with GPT image to get the aesthetic, then run Nano Banana to lock the exact necklace into it.

— invideo's creative team