# Wan 2.7 Text to Video > Generate cinematic, high-fidelity video from a text prompt with smooth, coherent motion and strong prompt adherence, at 720p or 1080p. ## Overview - **Endpoint**: `https://queue.modelrunner.run/wan-video/wan/v2.7/text-to-video` - **Model ID**: `wan-video/wan/v2.7/text-to-video` - **Category**: text-to-video - **Kind**: inference - **Tags**: wan, wan-2.7, alibaba, text-to-video, video-generation ## Pricing - **Price**: $0.15 per output second ## Request Lifecycle This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps: 1. **Submit** — `POST https://queue.modelrunner.run/wan-video/wan/v2.7/text-to-video` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the model; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=` (exact key=value matches, AND-ed). The response carries request handles only (no output yet): ```json { "status": "IN_QUEUE", "request_id": "<21-char id>", "status_url": "https://queue.modelrunner.run/wan-video/wan/v2.7/text-to-video/requests//status", "response_url": "https://queue.modelrunner.run/wan-video/wan/v2.7/text-to-video/requests/", "cancel_url": "https://queue.modelrunner.run/wan-video/wan/v2.7/text-to-video/requests//cancel" } ``` 2. **Poll status** — `GET ` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field. 3. **Read result** — `GET `. Returns the finished request, including the generated `output`: ```json { "id": "", "status": "COMPLETED", "output": ..., "input": ... } ``` The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow. ### Input Schema - **`seed`** (`integer`, _optional_): Random seed for reproducible results. Omit for a random seed each run. - **`prompt`** (`string`, _required_): Text description of the video to generate. - **`duration`** (`integer`, _optional_): Length of the generated video in seconds (2-15). - Default: `5` - Range: `2` to `15` - **`resolution`** (`ResolutionEnum`, _optional_): Output video resolution. 720p bills at $0.10/second; 1080p (default) bills at $0.15/second. - Default: `"1080p"` - Options: `"720p"`, `"1080p"` - **`aspect_ratio`** (`AspectRatioEnum`, _optional_): The aspect ratio of the generated video frame. - Default: `"16:9"` - Options: `"16:9"`, `"9:16"`, `"1:1"`, `"4:3"`, `"3:4"` - **`negative_prompt`** (`string`, _optional_): Describe content to avoid in the generated video. - **`enable_prompt_expansion`** (`boolean`, _optional_): When enabled, an LLM rewrites and enriches your prompt before generation. Disable to follow your exact wording. - Default: `true` ### Output Schema _No `Output` schema properties are available._ ## Default Example **Input** ```json { "prompt": "A hawk soaring over a vast canyon at sunset, wings outstretched against a blazing orange sky, slow cinematic pull-back", "duration": 4, "resolution": "720p", "aspect_ratio": "16:9", "enable_prompt_expansion": true } ``` **Output** ```json "https://media.modelrunner.ai/lGgZg62C1vyYGj8UPVH10.mp4" ``` ## Usage Examples ### cURL The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`. ```bash # 1. Submit the request (returns request handles, not the output) SUBMIT=$(curl --silent --request POST \ --url https://queue.modelrunner.run/wan-video/wan/v2.7/text-to-video \ --header "Authorization: Key $MODEL_RUNNER_KEY" \ --header "Content-Type: application/json" \ --data '{ "prompt": "A hawk soaring over a vast canyon at sunset, wings outstretched against a blazing orange sky, slow cinematic pull-back", "duration": 4, "resolution": "720p", "aspect_ratio": "16:9", "enable_prompt_expansion": true }') STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url') RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url') # 2. Poll until the request leaves the queue / in-progress state while true; do STATUS=$(curl --silent --url "$STATUS_URL" \ --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status') echo "Status: $STATUS" case "$STATUS" in COMPLETED) break ;; FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;; esac sleep 1 done # 3. Read the finished request, including the generated output curl --silent --url "$RESPONSE_URL" \ --header "Authorization: Key $MODEL_RUNNER_KEY" ``` ### JavaScript ```javascript import { modelrunner } from "@modelrunner/client"; const result = await modelrunner.subscribe("wan-video/wan/v2.7/text-to-video", { input: { "prompt": "A hawk soaring over a vast canyon at sunset, wings outstretched against a blazing orange sky, slow cinematic pull-back", "duration": 4, "resolution": "720p", "aspect_ratio": "16:9", "enable_prompt_expansion": true } }); console.log(result.data); ``` ### Python ```python import asyncio import modelrunner_ai async def main(): response = await modelrunner_ai.submit_async( "wan-video/wan/v2.7/text-to-video", arguments={ "prompt": "A hawk soaring over a vast canyon at sunset, wings outstretched against a blazing orange sky, slow cinematic pull-back", "duration": 4, "resolution": "720p", "aspect_ratio": "16:9", "enable_prompt_expansion": true } ) result = await response.get() print(result["output"]) asyncio.run(main()) ``` ## Additional Resources - [Playground](https://modelrunner.ai/models/wan-video/wan/v2.7/text-to-video) - [OpenAPI Schema](https://modelrunner.ai/models/wan-video/wan/v2.7/text-to-video/openapi.json) - [LLM Instructions](https://modelrunner.ai/models/wan-video/wan/v2.7/text-to-video/llms.txt)