# Nano Banana Edit — Atlas Cloud API

> Google's state-of-the-art image generation and editing model.

This is the machine-readable API reference for **Nano Banana Edit** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `google/nano-banana/edit`
- **Built by**: Google
- **Modality**: Image
- **Model page**: https://www.atlascloud.ai/models/google/nano-banana/edit
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.038 per image
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

export ATLASCLOUD_API_KEY="your-api-key"
```

Then ask in plain English; the agent calls `atlas_generate_image` with `model: "google/nano-banana/edit"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas generate image google/nano-banana/edit -p "Your prompt here"
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `google/nano-banana/edit`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"google/nano-banana/edit"`

- **`prompt`** (`string`, _required_):
  The positive prompt for image generation.

- **`images`** (`array[string]`, _required_):
  List of URLs of input images for editing.
  - Min items: 1
  - Max items: 10

- **`aspect_ratio`** (`string`, _optional_):
  The aspect ratio of the generated media.
  - Options: "1:1", "3:2", "2:3", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"

- **`output_format`** (`string`, _optional_):
  The format of the output image.
  - Default: `"default"`
  - Options: "default", "png", "jpeg"

- **`media_resolution`** (`string`, _optional_):
  Controls how input media is processed. LOW reduces tokens per image/video, possibly losing detail but allowing longer videos in context. Supported values: HIGH, MEDIUM, LOW.
  - Default: `"default"`
  - Options: "default", "low", "medium", "high"

- **`enable_base64_output`** (`boolean`, _optional_):
  If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.
  - Default: `false`

- **`enable_sync_mode`** (`boolean`, _optional_):
  If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "google/nano-banana/edit",
  "prompt": "",
  "images": [
    ""
  ]
}
```


**Full Example**:

```json
{
  "model": "google/nano-banana/edit",
  "prompt": "",
  "images": [
    ""
  ],
  "aspect_ratio": "1:1",
  "output_format": "default",
  "media_resolution": "default",
  "enable_base64_output": false,
  "enable_sync_mode": false
}
```


### Output Schema

The API returns the following output format:


- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated content (empty when status is not completed).

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.



**Example Response**:

```json
{
  "id": "",
  "urls": {},
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "created_at": "",
  "has_nsfw_contents": []
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "google/nano-banana/edit",
  "prompt": "",
  "images": [
    ""
  ],
  "aspect_ratio": "1:1",
  "output_format": "default",
  "media_resolution": "default",
  "enable_base64_output": false,
  "enable_sync_mode": false
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/google/nano-banana/edit)

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

#### Google Nano-Banana Edit

**Nano-Banana Edit** is Google’s advanced **AI-powered image editing and generation model**, designed to make visual transformation as intuitive as describing it in words. Built on Google’s cutting-edge computer vision and generative research, it combines precision, flexibility, and semantic awareness for professional-grade editing.

Try the New Version of Nano Banana!

- [Nano Banana Pro](https://www.atlascloud.ai/models/google/nano-banana-pro/text-to-image)
- [Nano Banana Pro edit](https://www.atlascloud.ai/models/google/nano-banana-pro/edit)
- [Nano Banana Pro Ultra](https://www.atlascloud.ai/models/google/nano-banana-pro/text-to-image-ultra)
- [Nano Banana Pro Edit Ultra](https://www.atlascloud.ai/models/google/nano-banana-pro/edit-ultra)
- [Nano Banana Pro Multi](https://www.atlascloud.ai/models/google/nano-banana-pro/text-to-image-multi)

#### 🌟 Why it stands out

- **Natural Language Editing** Modify images using simple text instructions — no masking, layering, or manual tools required.
- **Context-Aware Understanding** Accurately interprets scene structure, spatial relationships, and object semantics for realistic results.
- **Style and Tone Preservation** Keeps lighting, shadows, and texture consistent with the original image while applying changes seamlessly.
- **High Precision Control** Excels at fine-grained edits such as color adjustments, object replacement, or composition shifts with minimal distortion.
- **Creative Versatility** Suitable for concept art, photography, advertising design, and everyday content creation.

#### ⚙️ How to use

- **Input:** existing image + text prompt

- **Output:** edited image (**JPEG/PNG/WEBP**)

- **Size:** 1:1, 4:3, 16:9, 21:9, and so on.

- Supports style transfer, relighting, background replacement, and object modification

- Works with natural prompts like:

_(Description truncated. Full text on the model page.)_

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Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/google/nano-banana/edit · Docs: https://www.atlascloud.ai/docs