# minimax-hailuo-2.3-pro-i2v > Hailuo 2.3 Pro I2V breathes life into still images with stunning motion synthesis and cinematic camera control. Using deep motion understanding, it predicts realistic subject movement, depth, and environmental motion from a single input frame — delivering smooth, film-grade clips. ## Overview - **Endpoint**: `POST https://api.muapi.ai/api/v1/minimax-hailuo-2.3-pro-i2v` - **Model ID**: `minimax-hailuo-2.3-pro-i2v` - **Category**: image to video - **Variant**: Pro I2V - **Family**: minimax-2.3 - **Cost**: 0.63 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/minimax-hailuo-2.3-pro-i2v Content-Type: application/json x-api-key: YOUR_API_KEY ``` **Minimum (required only):** ```json { "prompt": "", "image_url": "https://example.com/your-image_url" } ``` **Full example (all params):** ```json { "prompt": "", "image_url": "https://example.com/your-image_url", "resolution": "1080p" } ``` **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/minimax-hailuo-2.3-pro-i2v \ -H "x-api-key: $MUAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{"prompt":"","image_url":"https://example.com/your-image_url"}' | jq -r .request_id) # 2. Poll until completed while :; do RESP=$(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}/minimax-hailuo-2.3-pro-i2v", headers=headers, json={"prompt":"","image_url":"https://example.com/your-image_url"}) 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 video. - **`image_url`** (`string`, _required_): URL of the input image. - **`resolution`** (`string`, _optional_): The resolution of the generated video. - Default: `"1080p"` - Options: `"1080p"` ## 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 `minimax-hailuo-2.3-pro-i2v`. See `muapi --help` for category-specific shortcuts (`muapi image generate`, `muapi video from-image`, etc.). ## Related Models - [Voice Clone](https://muapi.ai/playground/minimax-voice-clone) - [Pro I2V](https://muapi.ai/playground/minimax-hailuo-2.3-pro-i2v) - [Pro T2V](https://muapi.ai/playground/minimax-hailuo-2.3-pro-t2v) - [Standard I2V](https://muapi.ai/playground/minimax-hailuo-2.3-standard-i2v) - [Fast I2V](https://muapi.ai/playground/minimax-hailuo-2.3-fast) - [Standard T2V](https://muapi.ai/playground/minimax-hailuo-2.3-standard-t2v) ## Resources - [Playground Page](https://muapi.ai/playground/minimax-hailuo-2.3-pro-i2v) - [API Reference](https://muapi.ai/playground/minimax-hailuo-2.3-pro-i2v?tab=2) - [Global llms.txt](https://muapi.ai/llms.txt)