Nano Banana Pro is an image generation and editing model built by Google DeepMind on Gemini 3 Pro, suited for tasks with high requirements for text readability, brand consistency, and compositional detail. It can transform text descriptions, product photos, and character references into posters, infographics, or scene composites, and adjust lighting, focus, and visual elements through natural language.
Submit requests to the public API at api.acedata.cloud using the documented parameters, then use the results in your application.
Input parameters and result formats vary by service. Use the public API for this model and follow its guide for generation, task retrieval and editing operations.
Clarify capacity, inputs and outputs, and invocation methods before choosing a model.
The number of reference images and characters falls within native capability limits; aspect ratio, generation count, and result format are configured separately according to the selected endpoint.
Discover what nano-banana-pro can bring to your work.
The model emphasizes the readability and visual presentation of text in images, making it suitable for integrating headlines, product selling points, and descriptions into posters or packaging mockups. It can also convert multilingual content, adjusting the text together with the original design. Providing accurate copy, language, and typographic hierarchy offers more control over the result than simply asking it to “make an ad.”
It can combine references for people, products, and environments to generate scenes with consistent lighting and visual relationships. Its strength goes beyond simply stitching assets together; it also includes maintaining continuity in facial appearance and brand elements within complex compositions. Prompts should explain the purpose of each image and list the appearance, clothing, or product details that must be retained.
Changing daytime into a night scene, shifting focus to the product, altering the camera angle, or adjusting the color mood can all be specified as clear editing goals. When making local edits, describing what “needs to change” separately from what should “remain unchanged” helps revise an existing visual direction rather than redesigning the entire image each time.
Start with specific tasks to find where the model can make an impact.
Input brand assets, campaign copy, and design direction to create key visuals with headlines and selling points, then adapt the text for different languages. Suitable for promotional tasks that need to balance image style with information delivery. Before delivery, focus on checking spelling, brand names, and text hierarchy, then choose a landscape or portrait format based on the publishing placement.
Provide product photos or references for people and clothing, describe the target environment, camera, and lighting, and generate product scene changes, styling displays, or lifestyle assets. For product-related tasks, clearly specify that shape, color, and logos must be retained; outfit results are suitable as visual concepts, while actual fit, materials, and wearing effects still need to be verified against physical items.
Convert organized key knowledge points, processes, or handwritten notes into infographics, diagrams, and storyboard drafts. Specify the audience, information order, and visual focus in the input so the model can organize the image around the content. The deliverables are suitable for instructional explanations or proposal communication; numerical values and technical relationships should be reviewed item by item.
Choose based on task complexity, input materials, and expected results.
When a task includes substantial text, multiple reference assets, character consistency, or detailed photography control, Nano Banana Pro is better aligned with professional asset creation. The original Nano Banana corresponds to Gemini 2.5 Flash Image and is more suited to fast, easy editing; simple creative exploration does not need to pursue complex compositions from the start.
Nano Banana 2 corresponds to Gemini 3.1 Flash Image and is positioned as a balanced model that combines speed with general-purpose creation; Pro focuses on complex visual tasks, brand consistency, and fine control. The former can be considered for frequent routine content, while key visuals with text-heavy layouts, complex asset relationships, or a need for careful adjustments are better suited to Pro.
From a small-scale task to full integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand the scope of output quality and capabilities.
Answers to common questions about using nano-banana-pro.
They are different models: Pro corresponds to Gemini 3 Pro Image, while the original corresponds to Gemini 2.5 Flash Image. Pro focuses on complex compositions, text within images, and fine-grained control; when calling it, enter nano-banana-pro. The service name nano-banana does not mean the original model is being used.
When using /nano-banana/images, specify model as nano-banana-pro, action as generate, and select 2K or 4K through resolution. Set the image ratio separately with aspect_ratio; resolution tiers and landscape or portrait composition are two different choices.
For the dedicated endpoint, use action=edit, provide image URLs through image_urls, and describe the changes in prompt. You can also use /openai/images/edits, explicitly specifying the model and image. It is recommended to also state what to preserve, for example, preserve the product shape and change only the background and lighting.
Yes, you can continue editing based on an existing result, for example, first finalize the composition, then adjust the text or lighting. In practice, you can use the previous image as the reference for the next round and clearly specify what to modify this time. Saving satisfactory intermediate results helps prevent later changes from affecting details that have already been confirmed.
The dedicated endpoint provides image_url in data and returns task_id and trace_id for associating tasks. The compatible image endpoint can return either an image URL or a Base64 result; when asynchronous processing is needed, you can use async and callback_url to integrate completed results into asset management or content workflows.
Model information · Updated: 2026-10-01. For calling parameters and billing rules, see the API and pricing sections.
Choose a development approach or application suited to this model and follow its setup guide.
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SDK installation, authentication and examples↗TypeScript SDK Integration Guide
SDK installation, authentication and examples
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$0.0333· per generation
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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