Nano Banana 2.1

• - 🏢 Google's Nano Banana editing variant, catalogued for image inpainting work.
• - 🆕 Released October 2026; newest entry in the Nano Banana edit family.
• - 🔧 Built for localized edits that leave surrounding pixels untouched.
• - 🎯 Focus on identity preservation and fine detail retention during edits.
• - 💬 Nano Banana models support text-defined "semantic masking" for targeted edits.
• - 👁️ Pairs with the text-to-image sibling of the same generation.
• - 📚 Successor to the Nano Banana 2 editing variant from February 2026.
• - 🧠 Part of Google's broader Gemini-era multimodal model lineup.

Google

Google is an American multinational technology corporation and one of the world's most valuable brands. A subsidiary of parent company Alphabet Inc., Google operates across search, cloud computing, consumer electronics, and artificial intelligence. Its…

Nano Banana 2.1 is the editing-oriented variant of Google's Nano Banana image model line, exposed in this catalog as an inpainting model: you supply an existing image plus an instruction, and the model rewrites only the region the prompt describes while keeping the rest of the frame intact. Its stated emphasis is precise inpainting with identity and detail preservation — useful for prop swaps, object removal, wardrobe or background changes, and portrait touch-ups where a face must stay recognizable.

It is the current head of its family, following Nano Banana 2 (February 2026), with the lighter Nano Banana 2 Lite and the earlier precision-oriented Nano Banana Pro alongside it. The matching generation-2.1 text-to-image model is Nano Banana 2.1, so generation and editing can be run on the same release line for consistent look and subject handling.

Google describes the Nano Banana family as image generation and editing models that apply real-world knowledge and reasoning to produce precise results, including semantic masking, where a mask is defined through text rather than drawn by hand, so a specific part of an image can be changed while the rest is left untouched.

Google has not published detailed specifications or benchmark figures for this specific editing variant in sources available here, so no performance numbers are quoted. Treat the description above as feature-level positioning within the family rather than a measured comparison.