GPT Image 2 vs Nano Banana 2

GPT Image 2 is OpenAI's next-generation image model with near-perfect text rendering, pixel-level editing precision, and strong photorealism. It accepts up to 16 reference images for image-to-image transformations and generates results in approximately 3 seconds. Ideal for product photography, branded visuals, multilingual creatives, and detailed multi-object compositions.

Near-Perfect Text Rendering

Renders legible, correctly spelled text inside images — signs, labels, multilingual creatives, UI mockups — with higher accuracy than any previous generation.

Pixel-Level Editing

Precise image editing with preserved lighting, shadows, and textures — replace objects, refine backgrounds, and apply changes without visual artifacts.

Up to 16 Reference Images

Supply up to 16 reference images to guide transformations — maintain brand identity, character consistency, or style across generations.

Fast ~3 Second Generation

Generates images in approximately 3 seconds — fast enough for interactive workflows, live previews, and high-volume production pipelines.

Nano Banana 2 is Google's Gemini 3.1 Flash Image model. It creates images from a text prompt and edits existing ones from plain-language instructions — say 'add a sunset glow', 'replace the chair with a throne' or 'remove the background' and it applies the change. Attach reference images and it switches to editing automatically. Nano Banana 2 Lite is a lighter, cheaper variant.

Intuitive Natural Language Editing

Describe the change in plain language and Nano Banana 2 applies it — object swaps, background changes, facial refinements, style shifts.

Text-to-Image Too

Not only edits: describe a scene from scratch and Nano Banana 2 draws it; attach references and it edits instead.

Precision Object & Background Editing

Pixel-level object replacement, background modification, and facial refinement without distortions across iterations.

Physics-Aware Outputs

Edits maintain coherent lighting, natural shadows, and correct spatial relationships — outputs look real, not pasted.