aiqbench analysis
This guide demonstrates a practical application of Google's Gemini image editing capabilities via OpenRouter, allowing developers to modify existing images using text prompts within Python or TypeScript workflows. The technical focus remains on the specific API mechanics for image-to-image generation rather than broader consumer product launches or general software updates.
Nano Banana API: Edit Images with Gemini in Code

Send a source image and an edit prompt in one request, get the edited image back, and change the editing model by editing a single field. Runnable Python and TypeScript included.
This guide shows how to edit an image with a text prompt in code. You send the source image and an edit prompt to google/gemini-3.1-flash-image through the OpenRouter API, and the edited image comes back in the response. “Nano Banana” is the nickname for Google’s Gemini image models. This slug is Nano Banana 2, the default fast model in that family. Because you reach it through one API, you can use a different editing model later by changing one field. Image editing changes an existing image. Image generation creates a new image from text. This guide covers editing, so every request here includes a source image. For creating images from text, see the image generation docs or the image generation tutorial. Editing takes one request. Put the source image in input_references and the instruction in prompt, then read the edited image from data[0].b64_json and decode it to disk. google/gemini-3.1-flash-image is Nano Banana 2, the default fast Gemini image model. Check that…
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