Google/nano-banana-2.1-edit

nano-banana-2.1-edit

Edit images with Google Nano Banana 2.1 Edit API using prompts and reference images. Stable, affordable access for product visuals, ads, and creative workflows.

Image to ImageMulti-Image

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Documentation

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Nano Banana 2.1 Edit Pricing

Parameters	Price	Original Price	Discount

Resolution: 4k	$0.1282 per image	$0.1350 per image	95%
Resolution: 2k	$0.0570 per image	$0.0600 per image	95%
Resolution: 1k	$0.0380 per image	$0.0400 per image	95%
From reference image 1 onward	$0.0019 per reference image	$0.0020 per reference image	95%
Web search surcharge (when enabled)	$0.0133 per generation	$0.0140 per generation	95%
Image search surcharge (when enabled)	$0.0133 per generation	$0.0140 per generation	95%

Examples

README

Fast & Affordable Nano Banana 2.1 Edit API (Google AI Image Editing)

Nano Banana 2.1 Edit API brings Google's high-efficiency image editing model to Flaq AI for developers and creative teams. Transform existing visuals with natural-language instructions, combine multiple reference images, and produce refined outputs with improved detail, text rendering, and visual consistency. Flexible quality and aspect ratio controls help teams adapt source material for a wide range of production needs.

Key Features of Nano Banana 2.1 Edit API

Natural-Language Image Editing: Describe the changes you want and guide edits to subjects, backgrounds, lighting, and composition through prompts.
Multi-Image Reference Support: Combine multiple source images to guide a new visual while preserving important subjects and design details.
Improved Visual Consistency: Keep characters, objects, and scene elements more coherent across complex edit requests.
Clearer Text and Layouts: Refine graphics that include typography or infographic elements with improved rendering accuracy.
Flexible Output Quality: Select from multiple resolutions up to 4K according to the detail needed for the finished asset.
Versatile Aspect Ratios: Rework images for square, portrait, landscape, and panoramic placements.
Optional Search-Grounded Editing: Bring web or image search context into an edit when the visual brief calls for current or reference-based information.

How to Use Nano Banana 2.1 Edit API for Image Editing on Flaq AI

Input: One or more reference images and a natural-language prompt describing the desired changes.
Output: Edited images at the selected resolution and aspect ratio.
Aspect Ratios: Multiple standard and panoramic formats for ads, product pages, editorial graphics, and social content.
Capabilities: Prompt-based image editing, multi-image composition, subject and style consistency, text refinement, optional web and image search, and flexible quality controls.

Best Use Cases for Nano Banana 2.1 Edit API Integration

Campaign Asset Adaptation: Rework existing creative for new messages, layouts, and placements without starting from a blank canvas.
Product Image Updates: Adjust backgrounds, lighting, and scene context around reference products for catalog or promotional use.
Multi-Image Composites: Bring subjects and visual elements from several images into a coherent new composition.
Brand and Character Variations: Refine a series of visuals while keeping recurring objects, characters, and style cues recognizable.
Editorial and Social Refreshes: Update graphics, text-bearing visuals, and platform-specific crops through repeatable API workflows.

Note: Use images you are authorized to edit and keep prompts within Google's safety guidelines.

Reference images are charged from the first image; each enabled search option adds a fixed charge per generation, independent of image count. See current pricing.

Nano Banana 2.1 Edit vs Competitors: Comparative Analysis

Nano Banana 2.1 Edit vs. Nano Banana 2 Edit: Nano Banana 2 Edit supports prompt-driven editing and multi-image workflows. Nano Banana 2.1 Edit builds on that foundation with stronger visual fidelity, text rendering, and consistency across more involved edits.
Nano Banana 2.1 Edit vs. Nano Banana Pro: Nano Banana Pro targets precision work and complex professional design. Nano Banana 2.1 Edit offers an efficient option for teams that need high-quality edits and repeated asset variations.
Nano Banana 2.1 Edit vs. Nano Banana 2 Lite: Nano Banana 2 Lite focuses on speed and low-cost generation and editing. Nano Banana 2.1 Edit is suited to reference-heavy briefs where composition, typography, and recurring subject details matter more.
Nano Banana 2.1 Edit vs. GPT Image: GPT Image supports image editing through OpenAI's API. Nano Banana 2.1 Edit provides a Google model workflow on Flaq AI with multiple reference images, broad aspect ratio choices, and optional search grounding.
Nano Banana 2.1 Edit vs. FLUX.2: FLUX.2 offers reference-guided image editing and composition. Nano Banana 2.1 Edit combines multi-image guidance with Google's prompt understanding and optional web or image search for context-aware visual updates.

Use the Nano Banana 2.1 Edit Model Directly in Powerful AI Tools

Explore several AI creation tools for quick image and video workflows in your browser, then scale successful ideas with Flaq AI's production-ready model APIs. Flaq AI provides a unified API layer for all models, making it easy to use and scale your workflows.

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API Examples

JavaScriptPythoncURL

Submit Example

// Step 1: Submit generation request
const response = await fetch('https://api.flaq.ai/api/v1/image/task', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({
    model_name: 'nano-banana-2.1-edit',
    prompt: 'Add flying cars and neon lights to this cityscape',
    image_url_list: ['https://example.com/input-image.jpg'],
    width: 16,
    height: 9,
    resolution: '2k',
    enable_web_search: false,
    enable_image_search: true
  })
});

const { data } = await response.json();
const taskId = data.task_id;

Polling Example

// Step 2: Poll for results
const taskId = data.task_id;

const pollResult = async (taskId) => {
  const res = await fetch(`https://api.flaq.ai/api/v1/image/${taskId}`, {
    headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
  });
  return res.json();
};

while (true) {
  const pollResultData = await pollResult(taskId);
  const status = pollResultData.data.task_status;

  if (status === 'succeed') {
    console.log(pollResultData.data.task_result.images[0].url);
    break;
  }

  if (status === 'failed') {
    console.error(pollResultData.data.task_status_msg);
    break;
  }

  await new Promise(resolve => setTimeout(resolve, 10000));
}

Submit Example

# Step 1: Submit generation request
import requests

response = requests.post(
    'https://api.flaq.ai/api/v1/image/task',
    headers={
        'Content-Type': 'application/json',
        'Authorization': 'Bearer YOUR_API_KEY'
    },
    json={
        'model_name': 'nano-banana-2.1-edit',
        'prompt': 'Add flying cars and neon lights to this cityscape',
        'image_url_list': ['https://example.com/input-image.jpg'],
        'width': 16,
        'height': 9,
        'resolution': '2k',
        'enable_web_search': False,
        'enable_image_search': True
    }
)

result = response.json()
task_id = result['data']['task_id']

Polling Example

# Step 2: Poll for results
task_id = response.json()['data']['task_id']
poll_url = f"https://api.flaq.ai/api/v1/image/{task_id}"

while True:
    poll_result = requests.get(poll_url, headers={'Authorization': 'Bearer YOUR_API_KEY'}).json()
    status = poll_result['data']['task_status']
    if status == 'succeed':
        print(poll_result['data']['task_result']['images'][0]['url'])
        break
    if status == 'failed':
        print(poll_result['data']['task_status_msg'])
        break
    time.sleep(10)

Submit Example

# Step 1: Submit generation request
curl -X POST https://api.flaq.ai/api/v1/image/task \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model_name": "nano-banana-2.1-edit",
    "prompt": "Add flying cars and neon lights to this cityscape",
    "image_url_list": ["https://example.com/input-image.jpg"],
    "width": 16,
    "height": 9,
    "resolution": "2k",
    "enable_web_search": false,
    "enable_image_search": true
  }'

Polling Example

# Step 2: Poll for results
# Replace {task_id} with the task_id returned from the submit response
curl -X GET "https://api.flaq.ai/api/v1/image/{task_id}" \
  -H "Authorization: Bearer YOUR_API_KEY"

Related Nano Banana 2.1 Models

Nano Banana 2.1 Edit Pricing

Examples

Related Models

README

Fast & Affordable Nano Banana 2.1 Edit API (Google AI Image Editing)

Key Features of Nano Banana 2.1 Edit API

How to Use Nano Banana 2.1 Edit API for Image Editing on Flaq AI

Best Use Cases for Nano Banana 2.1 Edit API Integration

Nano Banana 2.1 Edit vs Competitors: Comparative Analysis

Use the Nano Banana 2.1 Edit Model Directly in Powerful AI Tools

Text to Image

Image to Image

Text to Video

Image to Video

AI Media Creator

Reference to Video

AI Canvas

API Examples

Submit Example

Polling Example

Submit Example

Polling Example

Submit Example

Polling Example

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