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Nano Banana is not a separate API. It is Google’s name for a family of Gemini image-generation and image-editing models accessed through the Gemini API. For a complete image-to-video workflow, use Nano Banana to design or refine a still image, then pass that image to a video model such as Veo or Gemini Omni Flash.
This guide covers the current model IDs, setup, image generation, conversational editing, resolution controls, video handoff, asynchronous jobs, prompting, costs, and common failures. Model names and availability can change, so verify the live documentation before deploying.
What is the Nano Banana API?
“Nano Banana API” is informal shorthand for using Google’s Gemini API with its Nano Banana image models. Nano Banana itself is not a standalone image app, video editor, or separate developer product.
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| Nickname | Model ID | Best for |
|---|---|---|
| Nano Banana 2 Lite | gemini-3.1-flash-lite-image |
Fast, high-volume drafts and variations |
| Nano Banana 2 | gemini-3.1-flash-image |
General image generation, editing, references, and consistency |
| Nano Banana Pro | gemini-3-pro-image |
Complex brand assets, layouts, localization, and premium output |
| Nano Banana | gemini-2.5-flash-image |
Legacy integrations that need compatibility |
Google recommends newer Nano Banana variants, so tutorials that use only gemini-2.5-flash-image may no longer describe the best current path.
Which Nano Banana model should you choose?
- Use Nano Banana 2 Lite for thumbnails, quick concepts, and large batches where latency and cost matter more than complex references. It supports 1K output and is not the best choice for demanding multi-reference edits.
- Use Nano Banana 2 as the default. It offers the strongest balance of quality, speed, editing, image references, and consistency, with 1K, 2K, and 4K output options.
- Use Nano Banana Pro for detailed creative work, intricate layouts, brand assets, and high-resolution production images. It supports output up to 4K.
- Use the original Nano Banana only when maintaining an existing legacy integration is more important than migrating.
These models are distinct from access through the Gemini app, Google AI Studio, and Google Cloud. The Gemini app is a consumer interface; AI Studio is useful for experimentation and key creation; the Gemini API is the programmable interface; and Google Cloud provides separate enterprise billing, governance, and deployment options.
Requirements and API-key setup
You need a Google AI Studio or Google Cloud account, a Gemini API key, a current Google GenAI SDK, and Python 3.x or Node.js. Production applications also need quota and billing planning.
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export GEMINI_API_KEY="your-api-key"In Windows PowerShell:
$env:GEMINI_API_KEY="your-api-key"The SDK reads GEMINI_API_KEY by default in Google’s examples. Never commit the key to a repository, place it in browser code, or include it in client-side logs. For a server application, use a secrets manager and rotate exposed keys.
Install the current Google GenAI SDK
pip install google-genai pillowFor JavaScript:
npm install @google/genaiSDK interfaces change. The examples below follow Google’s current GenAI SDK and Interactions API documentation; check the getting-started guide and current release before publishing an application.
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Generate an image with Nano Banana 2
The current image-generation examples use interactions.create(). Python:
import base64
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.1-flash-image",
input=(
"Create a cinematic product image of a matte-black travel bottle "
"on a wet basalt rock beside a mountain stream at dawn. "
"Soft golden rim light, realistic water droplets, premium outdoor "
"advertising style, wide 16:9 composition."
),
)
with open("product-hero.png", "wb") as output:
output.write(base64.b64decode(interaction.output_image.data))JavaScript:
import { GoogleGenAI } from "@google/genai";
import fs from "node:fs";
const ai = new GoogleGenAI({});
const interaction = await ai.interactions.create({
model: "gemini-3.1-flash-image",
input:
"Create a cinematic product image of a matte-black travel bottle " +
"on a wet basalt rock beside a mountain stream at dawn. " +
"Soft golden rim light, realistic water droplets, premium outdoor " +
"advertising style, wide 16:9 composition.",
});
if (interaction.output_image) {
fs.writeFileSync(
"product-hero.png",
Buffer.from(interaction.output_image.data, "base64")
);
}Do not assume every response contains only an image. Model output can include text and multiple content blocks. For production code, inspect the response and handle the absence of output_image explicitly.
Edit an existing image with a prompt
Nano Banana accepts text and image input together. Its editing workflow is conversational: describe what should change and what must remain unchanged.
import base64
from google import genai
client = genai.Client()
with open("product-hero.png", "rb") as image_file:
image_data = base64.b64encode(image_file.read()).decode("utf-8")
interaction = client.interactions.create(
model="gemini-3.1-flash-image",
input=[
{
"type": "image",
"data": image_data,
"mime_type": "image/png",
},
{
"type": "text",
"text": (
"Keep the bottle, camera angle, mountain stream, lighting, "
"and composition unchanged. Replace only the matte-black "
"finish with brushed copper. Preserve realistic reflections "
"and water droplets."
),
},
],
)
for step in interaction.steps:
if step.type == "model_output":
for block in step.content:
if block.type == "image":
with open("product-copper.png", "wb") as output:
output.write(base64.b64decode(block.data))You can ask it to add or remove an object, replace a background, change clothing or lighting, alter a product finish, apply a visual style, combine references, or turn a video into a poster, thumbnail, storyboard frame, or other still. Nano Banana 2 is the better choice when consistency across multiple references matters.
Generative editing is not deterministic layer-based masking. “Change only the background” is an instruction, not a guarantee that reflections, edges, shadows, faces, or nearby pixels will remain identical. Preserve the source, compare every result, and use conventional compositing when exact preservation is essential.
Control aspect ratio and resolution
For Gemini 3 image models, request the format explicitly:
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interaction = client.interactions.create(
model="gemini-3.1-flash-image",
input="A cinematic 16:9 establishing shot of a neon-lit Tokyo alley in rain",
response_format={
"type": "image",
"mime_type": "image/jpeg",
"aspect_ratio": "16:9",
"image_size": "2K",
},
)Use uppercase values such as 1K, 2K, and 4K; lowercase forms may be rejected. Nano Banana 2 Lite is limited to 1K.
- 1K: prompt testing and rough drafts.
- 2K: a practical default for web and social assets.
- 4K: final work that genuinely benefits from additional detail.
Generate at the intended aspect ratio whenever possible. Cropping afterward can remove carefully composed negative space or alter the subject’s placement.
Build the Nano Banana-to-video workflow
The most reliable creative pipeline is:
- Generate a key image with Nano Banana 2.
- Review product geometry, identity, logos, hands, faces, and text.
- Make constrained still-image edits until the design is approved.
- Use the approved still as an image-to-video starting frame or visual reference.
- Generate several motion variations.
- Finish the chosen clip in a conventional editor.
Keep the roles separate:
- Nano Banana: image creation, image editing, composition, style transfer, and video-to-image work.
- Gemini Omni Flash: conversational video generation and editing, including
text_to_video,image_to_video,reference_to_video, andedittasks. - Veo: dedicated cinematic video-generation workflows, including image-to-video and related video operations.
- Traditional editing software: precise cuts, timeline assembly, color, audio, captions, and delivery exports.
Route A: Nano Banana image to Veo
Use the still as a starting frame or reference for the subject, costume, product design, and environment. Google’s Veo guide demonstrates this Nano Banana-to-Veo handoff.
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Because Veo methods and model IDs can vary between Gemini API, preview, and Google Cloud endpoints, copy the exact current method from that guide rather than hard-coding an old example. Conceptually, the request looks like this:
operation = client.models.generate_videos(
model="veo-3.1-generate-preview",
prompt=(
"Slow cinematic camera push toward the bottle. "
"Water droplets slide down the surface. "
"The mountain stream moves naturally in the background. "
"Preserve the bottle's shape, label placement, and copper finish."
),
image=starting_image,
)Route B: Gemini Omni Flash
Omni Flash is the more natural choice when you want a conversational video transformation or edit. Specify the appropriate task mode in the video configuration, or use a prompt that clearly states the intended operation. Consult Google’s Omni documentation for the current model and request shape.
Poll asynchronous video operations
Video generation normally returns an operation rather than an immediately downloadable binary. Your application should:
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- Submit the request.
- Record the operation name and source asset.
- Poll at a sensible interval, such as 5–10 seconds.
- Use exponential backoff for transient network failures.
- Stop after a defined maximum wait, rather than blocking indefinitely.
- Check for an error field before downloading.
- Save the video and metadata such as model, prompt, timestamp, and exposed seed.
- Delete temporary uploads when they are no longer needed.
At timeout, show a status such as “generation still processing; check status later” and retain the operation identifier. Retry safely only where the endpoint supports idempotent behavior; otherwise, an automatic retry may create and charge for a duplicate job.
Prompt templates for stronger results
Image generation
Create a [shot type] of [subject] in [environment].
Subject: [identity, materials, shape, clothing, logo placement]
Composition: [camera angle, perspective, subject position, negative space]
Lighting: [time of day, key light, contrast, reflections, shadows]
Style: [photorealistic, editorial, cinematic, illustration]
Output: [aspect ratio, resolution, intended use]Constrained image editing
Using the supplied image, change only [target element].
Make the change: [replacement, material, color, texture]
Integrate it with: [lighting, perspective, reflections]
Keep unchanged: [identity, composition, camera angle, background,
logo, text, and all other objects]Image-to-video
Animate the supplied image into [duration and style].
Camera: [locked-off, dolly, crane, handheld, orbit]
Motion: [subject movement, environmental movement, speed, direction]
Continuity: Preserve [identity, geometry, clothing, palette].
Do not add [objects, text, logos, cuts].
Audio: [ambient sound, dialogue, music direction, or silent]Change one variable at a time: composition first, then identity and geometry, lighting, background, motion, and finally resolution. A prompt such as “change only the red sofa to dark green leather” is usually more controllable than rewriting the entire scene.
Common problems and fixes
Invalid or unavailable model ID
Show both the nickname and official model ID in configuration, and check Google’s current model documentation. Older code may refer to gemini-2.5-flash-image, legacy Generate Content calls, or preview Veo IDs. Do not assume an Imagen integration remains available: Google’s documentation lists a shutdown date of August 17, 2026, so verify its present status.
The response contains text instead of an image
Check that you selected an image model, used the supported endpoint, and requested an image response where required. The response may contain interleaved text and image blocks, so inspect interaction.steps instead of assuming output_image always exists.
The edit changes too much
Use “change only” language, name every important invariant, provide a higher-quality source, and split a complex edit into smaller operations. For pixel-level accuracy, use a conventional editor with an explicit mask.
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Do not rely on generated text for final legal or marketing copy. Generate the visual without the final wording, then add exact text in an image editor. Inspect trademarks, small labels, faces, hands, and product geometry at 100 percent. Google’s image documentation also states that generated images include a SynthID watermark.
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Identity drifts
Reuse a consistent reference image, describe invariant features, and provide multiple reference views where supported. Avoid regenerating a person or product from text alone across every iteration.
Input-file errors
Verify the MIME type, file size, video format, and file accessibility. Large images and video should use Google’s documented Files API or file-input method rather than oversized inline base64. A public URL is not automatically fetchable by the API, and an expired or unauthorized file URI will fail.
Timeouts, quota errors, and unexpected charges
Log the model and operation name, use bounded polling, handle transient errors separately from permanent errors, and monitor quota. Keep drafts short and low resolution, cache approved stills, avoid regenerating unchanged inputs, and batch work only within your rate limits.
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Costs and production considerations
Pricing depends on model, resolution, endpoint, region, and account tier. Google’s pricing page currently shows Veo 3.1 examples ranging from $0.05 per generated second for 720p Lite to $0.60 per second for 4K Standard; these figures were checked against the documentation supplied for this article and may change. Consult the live Gemini API pricing page before budgeting.
Use the fastest suitable model for prompt iteration, then render the approved version with a higher-quality model or resolution. Track retries because a failed network request is not necessarily proof that the server did not start a billable job. Store source images, prompts, model IDs, operation IDs, outputs, and timestamps so results can be reproduced or audited.
For enterprise deployment, Google Cloud offers separate generative-media and Vertex AI products with centralized billing and governance. Do not assume that a consumer Gemini plan, Flow access, Gemini API quota, and Google Cloud entitlement are interchangeable.
Rights, safety, and finishing
Obtain the necessary rights for uploaded photographs, video, likenesses, music, logos, trademarks, copyrighted characters, and client-owned product material. Images found through search are not automatically licensed for reuse. Follow Google’s image-generation policies and rights guidance.
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For creators who do not need code, the Gemini app or Google Flow may be more convenient. Developers can start with AI Studio and the Gemini API; organizations needing governance may prefer Google Cloud. Dedicated tools such as Runway or Adobe Firefly may fit specialized creative workflows, while DaVinci Resolve is better for deterministic editing, color, audio, and timeline control.
The practical division is simple: design and refine the still with Nano Banana, animate or edit it with Veo or Gemini Omni Flash, then finish the selected footage in a conventional editor.
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