# nano-banana-edit > Nano Banana is a mysterious, high-performance image model. It excels at precise, language-driven edits and consistent character preservation, allowing users to modify images with natural text commands. ## Overview - **Endpoint**: `POST https://api.muapi.ai/api/v1/nano-banana-edit` - **Model ID**: `nano-banana-edit` - **Category**: image to image - **Variant**: Edit Image - **Family**: nano - **Cost**: 0.03 credits per call (some models compute cost dynamically based on params) ## API Usage MuApi uses a **submit-then-poll** pattern: submit a job, get a `request_id`, then poll the predictions endpoint until `status` is `completed`. Optionally pass `?webhook=YOUR_URL` on the submit call to receive a POST callback when the job finishes (skip polling). **Authentication**: send your MuApi key in the `x-api-key` header. Get one at https://muapi.ai/access-keys. ### 1. Submit a job ```http POST https://api.muapi.ai/api/v1/nano-banana-edit Content-Type: application/json x-api-key: YOUR_API_KEY ``` **Minimum (required only):** ```json { "prompt": "", "images_list": "https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/551398692388/57fe557b-afc5-45cd-9d6a-23b54ffa667a.jpg" } ``` **Full example (all params):** ```json { "prompt": "", "images_list": "https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/551398692388/57fe557b-afc5-45cd-9d6a-23b54ffa667a.jpg", "aspect_ratio": "Auto" } ``` **Response:** ```json { "request_id": "abc123", "status": "processing" } ``` ### 2. Poll for the result ```http GET https://api.muapi.ai/api/v1/predictions/{request_id}/result x-api-key: YOUR_API_KEY ``` Possible `status` values: `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`. Poll every 2-5 seconds until terminal. When `completed`, the result URLs are in the `outputs` array. **Example response when `completed`:** ```json { "id": "abc123", "status": "completed", "outputs": [ "https://cdn.muapi.ai/.../output.png" ], "urls": { "get": "https://api.muapi.ai/api/v1/predictions/abc123/result" }, "created_at": "2026-05-08T12:34:56Z", "has_nsfw_contents": [] } ``` ### cURL ```bash # 1. Submit REQUEST_ID=$(curl -s -X POST https://api.muapi.ai/api/v1/nano-banana-edit \ -H "x-api-key: $MUAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{"prompt":"","images_list":"https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/551398692388/57fe557b-afc5-45cd-9d6a-23b54ffa667a.jpg"}' | jq -r .request_id) # 2. Poll until completed while :; do RESP=$(curl -s https://api.muapi.ai/api/v1/predictions/$REQUEST_ID/result -H "x-api-key: $MUAPI_API_KEY") STATUS=$(echo "$RESP" | jq -r .status) [ "$STATUS" = "completed" ] && echo "$RESP" | jq .outputs && break [ "$STATUS" = "failed" ] && echo "$RESP" && exit 1 sleep 3 done ``` ### Python ```python import os, time, requests API = "https://api.muapi.ai/api/v1" headers = {"x-api-key": os.environ["MUAPI_API_KEY"]} r = requests.post(f"{API}/nano-banana-edit", headers=headers, json={"prompt":"","images_list":"https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/551398692388/57fe557b-afc5-45cd-9d6a-23b54ffa667a.jpg"}) request_id = r.json()["request_id"] while True: res = requests.get(f"{API}/predictions/{request_id}/result", headers=headers).json() if res["status"] == "completed": print(res["outputs"]); break if res["status"] == "failed": raise RuntimeError(res.get("error")); time.sleep(3) ``` ## Input Schema The API accepts the following input parameters: - **`prompt`** (`string`, _required_): Text prompt describing the image, what you want the final edited image to look like. - **`images_list`** (`array`, _required_): List of URLs of input images for editing. - **`aspect_ratio`** (`string`, _optional_): Aspect ratio of the output image. - Default: `"Auto"` - Options: `"Auto"`, `"1:1"`, `"3:4"`, `"4:3"`, `"9:16"`, `"16:9"`, `"3:2"`, `"2:3"`, `"5:4"`, `"4:5"`, `"21:9"` ## Output Schema The polling endpoint returns the following fields: - **`id`** (`string`): The request ID. - **`status`** (`string`): One of `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`. - **`outputs`** (`array`): URLs to generated images/videos/audio. Empty until `status` is `completed`. - **`urls.get`** (`string`): Self-link to re-fetch this prediction. - **`error`** (`string` | `null`): Error message if `status` is `failed`. - **`created_at`** (`string`): ISO-8601 timestamp of when the request was created. - **`has_nsfw_contents`** (`array of boolean`): Per-output NSFW detection flags. ## Webhooks (optional) Append `?webhook=https://your-server/path` to the submit URL. When the job reaches a terminal state, MuApi will POST the same shape as the polling response to your URL — no polling needed. ## Agent Integration MuApi ships an MCP server and CLI so agents (Claude Code, Cursor, custom) can call this endpoint without writing HTTP code: ```bash # Install the CLI npm install -g muapi-cli # Authenticate once muapi auth login # Expose all MuApi models as MCP tools to your agent muapi mcp serve ``` The MCP server exposes tools that wrap submit + poll for every model, including `nano-banana-edit`. See `muapi --help` for category-specific shortcuts (`muapi image generate`, `muapi video from-image`, etc.). ## Related Models - [Pro Edit Image](https://muapi.ai/playground/nano-banana-pro-edit) - [Edit Image](https://muapi.ai/playground/nano-banana-edit) - [Text to Image](https://muapi.ai/playground/nano-banana) - [Image Effects](https://muapi.ai/playground/nano-banana-effects) - [Photo Packs](https://muapi.ai/playground/photo-pack) - [v2 Text to Image](https://muapi.ai/playground/nano-banana-2) - [Lite Edit](https://muapi.ai/playground/nano-banana-2-lite-edit) - [Lite](https://muapi.ai/playground/nano-banana-2-lite) - [Text to Image Pro](https://muapi.ai/playground/nano-banana-pro) - [v2 Image Edit](https://muapi.ai/playground/nano-banana-2-edit) ## Resources - [Playground Page](https://muapi.ai/playground/nano-banana-edit) - [API Reference](https://muapi.ai/playground/nano-banana-edit?tab=2) - [Global llms.txt](https://muapi.ai/llms.txt)