Nano Banana Prompt Guide: Examples and Best Practices

Nano Banana and Nano Banana Pro are powerful models built to serve different creative needs. Whether you're looking for fast image edits or complex 4K compositions requiring detailed reasoning, both models are capable of producing professional-grade results.

However, because the two models are architecturally distinct, they each respond best to different prompting strategies. Nano Banana is optimized for speed and pattern recognition—making it ideal for quick image generation and editing—while Nano Banana Pro focuses on structure and logical reasoning, enabling it to construct intricate scenes, data-heavy infographics, and render text with high precision.

In this guide, we've assembled a set of practical workflows to help you get the most out of both models. Although we highlight the unique capabilities of Nano Banana here, many of these techniques build on the foundational prompting principles covered in our main image prompting guide. Those basics are useful to know, but this Nano Banana-specific guide is designed to stand alone—so you can dive right in.

Nano Banana vs. Nano Banana Pro

Though they share a name, it's important to understand that Nano Banana and Nano Banana Pro are two distinct models, each optimized for different types of tasks. Nano Banana (powered by Gemini 2.5 Flash) is built for high-speed editing and style transfers, while Nano Banana Pro (powered by Gemini 3 Pro) is designed for complex reasoning and high-fidelity output.

The table below outlines the key differences between the two models from a prompting perspective:

Feature	Nano Banana (Flash)	Nano Banana Pro
Primary role	Image editing, inpainting, style transfer	Complex composition, text rendering, infographics
Prompting style	Iterative, conversational instructions	Structured, detailed instructions
How it works	Rapid pattern-matching and mapping	Reasoning engine that plans scene logic
Text rendering	Basic, prone to errors	High-fidelity, supports multiple languages
Data grounding	Static training data	Real-time Google Search integration

How to Prompt Nano Banana (Gemini 2.5 Flash)

Nano Banana (Gemini 2.5 Flash) is built for speed and high-velocity tasks, making it especially effective for conversational editing and style transfers. Rather than constructing a scene from scratch using logical rules, it analyzes existing pixels and predicts how they should change to match your description. This makes it ideal for modifying images while preserving their overall look and feel.

Basic Image Generation and Styles

For standard image generation, Nano Banana performs best with prompts that clearly define the subject, action, and context. While it can generate images from simple keywords, it truly excels with descriptive, narrative-driven instructions that give the model a clear visual direction. Here's what to include in your prompts for optimal results:

• Subject and action: Specify who or what the focus is and what they're doing

Contextual details: Provide "where" and "when" information (lighting conditions, background objects) to prevent the model from guessing

Stylization: Name your desired aesthetic explicitly—like "photorealistic," "watercolor illustration," or "3D render"—to control the visual mood

Here's an example prompt:

A high-fashion medium shot of a model in a charcoal grey tailored suit sitting on a slate stone bench in a formal garden. The monochromatic palette of grey and black is broken only by the lush, dark green of the manicured cypress trees in the background. The composition uses depth by placing a blurred stone statue in the foreground, the model in the middleground, and a distant villa in the background. Lighting: Rembrandt lighting with the key light placed high and to one side, creating a small triangle of light on the cheek for a moody, classic aesthetic.

Prompting for Image Editing

One of Nano Banana's most powerful features is its ability to perform semantic masking. Instead of manually painting over specific areas, you can use natural language to tell the model exactly what to change. The model identifies the target object based on your description and applies the edit while keeping the rest of the image intact.

Prompting for Character Consistency

Maintaining consistent characters across multiple images is one of AI generation's biggest challenges. While using a fixed set of descriptors (often called an "anchor string") might seem like a solution, this usually results in slightly different characters appearing each time you change the scene.

The most reliable way to achieve consistency with Nano Banana is to build a 360-degree character sheet—a two-step process:

1. Generate the reference sheet: First, create 2-3 images of your character within a single frame or as separate generations. Instruct the model to show the character from multiple angles (looking left, right, and from behind). This gives the model a complete visual understanding of the character's features and clothing.
2. Use references for new scenes: Once you have your character sheet, use those images as references to place that specific character in different situations. By referring back to your original sheet, you ensure proportions and details remain stable.