Google's high-efficiency image model. Generates from a prompt at up to 4K, can search the web for reference, and costs roughly a quarter of Nano-Banana Pro.
The best image results come from specific composition, style, and lighting language. Be explicit about what should be in frame and what should feel dominant.
Best results start with a precise subject, composition, and style direction.
Google Nano-Banana 2.1
Google's high-efficiency image model. Generates from a prompt at up to 4K, can search the web for reference, and costs roughly a quarter of Nano-Banana Pro.
Tell the model what should dominate the frame first.
Use lighting language early; it changes everything downstream.
When editing, describe what stays, not just what changes.
References help when continuity matters more than novelty.
A strong image prompt defines the subject, composition, lighting, and finish instead of leaving them implied.
Use precise visual language to control subject, composition, lighting, and style from the start.
Preserve the useful parts of the image while steering the rest with masks, references, or prompt edits.
Bring in reference images or LoRAs when consistency is more important than exploration.
Google Nano-Banana 2.1 is strongest when the visual brief is specific about framing, style, and what should read first.
Use it for campaign images, product shots, subject consistency, or polished concept work.
When editing, say exactly what changes and what must remain untouched.
Lock the subject, composition, and lighting direction before you chase style nuance.
Use references or edits when the same subject, style, or layout has to survive across versions.
Once the frame works, refine only the weak areas instead of rewriting the whole composition.
Finish strong compositions by scaling them without rebuilding the frame from scratch.
Use editing tools after the initial generation when the composition is right but the details still need polish.
