# Qwen Wan 2.6 — Text To Video

> Try Wan-2.6 API on GPT Proto. Generate 1080p cinematic videos with native audio, lip-sync, and multi-shot storytelling up to 15s. Starting at $0.10/s.

## Overview

- **Endpoint**: `POST https://gptproto.com/api/v3/alibaba/wan-2.6/text-to-video`
- **Result URL**: `GET https://gptproto.com/api/v3/predictions/{result_id}/result` — the submit response also returns the authoritative URL in `data.urls.get`
- **Model ID**: `wan-2.6`
- **Vendor**: Qwen
- **Scene**: `text-to-video`
- **Category**: text-to-video
- **Modalities**: input text → output video
- **Playground**: https://gptproto.com/model/qwen/wan-2.6
- **API documentation**: https://docs.gptproto.com
- **Other scenes of this model**: `image-to-video`, `reference-to-video` — same auth, different endpoint path and input schema

## Authentication

Every request needs a GPTProto API key in the `Authorization` header. Create one at https://gptproto.com/dashboard/api-key, then export it:

```bash
export GPTPROTO_API_KEY="your-api-key"
```

Header: `Authorization: Bearer $GPTPROTO_API_KEY` (plus `Content-Type: application/json` on POST).

## Pricing

Platform price by tier (USD, already includes the GPTProto discount):

- Cheapest tier — **1280*720 · 5**: $0.45 per run
- Most expensive tier — **1920*1080 · 15**: $2.02 per run
- 30 priced tiers in total; the parameters below select the tier.
  - `size`: 1080*1920, 1088*832, 1248*1632, 1280*720, 1440*1440, 1632*1248, 1920*1080, 720*1280, 832*1088, 960*960
  - `duration`: 5–15
- Example — size=1280*720, duration=5: $0.45
- Example — size=1280*720, duration=10: $0.9
- Example — size=1920*1080, duration=15: $2.02

Price range: $0.45 – $2.02 per generation.

Prices may change. The model page always shows the live price: https://gptproto.com/model/qwen/wan-2.6

## API Information

The API is asynchronous: POST the endpoint to create a prediction, then poll its result URL until `status` is `completed` or `failed`.

### Input Schema

The endpoint accepts the following JSON body parameters:

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

- **`negative_prompt`** (`string`, _optional_):
  The negative prompt for the generation.

- **`size`** (`enum`, _optional_):
  The video resolution.
  - Default: `1920*1080`
  - Options: 1280*720, 720*1280, 960*960, 1088*832, 832*1088, 1920*1080, 1080*1920, 1440*1440, 1632*1248, 1248*1632

- **`duration`** (`enum`, _optional_):
  The duration of the generated media in seconds.
  - Default: `5`
  - Options: 5, 10, 15

- **`prompt_extend`** (`boolean`, _optional_):
  Whether to extend the prompt.
  - Default: `true`
  - Options: true, false

- **`audio`** (`string`, _optional_):
  Audio URL to guide generation (optional).

- **`shot_type`** (`string`, _optional_):
  The type of shots to generate.

- **`seed`** (`range`, _optional_):
  The random seed to use for the generation.
  - Default: `1`
  - Range: 0–2147483647

**Required Parameters Example**:

```json
{
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle"
}
```

**Full Example**:

```json
{
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
  "negative_prompt": "",
  "size": "1920*1080",
  "duration": 5,
  "prompt_extend": true,
  "audio": "",
  "shot_type": "",
  "seed": 1
}
```

### Output Schema

Both submit and poll return the same envelope:

- **`data.id`** (`string`): Prediction id. Use it as `result_id` when polling for the result.
- **`data.status`** (`string`): One of `created`, `running`, `completed`, `failed`. See Status Values below.
- **`data.outputs`** (`array of string`): Generated files. Empty until `status` is `completed`; then it holds the output URLs (or base64 strings when the model exposes a base64 option).
- **`data.urls.get`** (`string`): Authoritative result URL for this prediction. Prefer it over building the poll URL yourself.
- **`data.error`** (`string | null`): Failure reason when `status` is `failed`, otherwise `null`.
- **`data.executionTime`** (`integer`): Total processing time in milliseconds.
- **`data.timings.inference`** (`integer`): Model inference time in milliseconds.
- **`data.hasNsfwContents`** (`array of boolean`): Per-output moderation flags.
- **`message`** (`string`): `success` on a normal response, otherwise the error message.
- **`code`** (`integer`): Business status code. `200` means the request was accepted.

**Example Response — submit (task accepted)**:

```json
{
    "data": {
        "id": "pred_example_01",
        "model": "wan-2.6",
        "outputs": [],
        "urls": {
            "get": "https://gptproto.com/api/v3/predictions/pred_example_01/result"
        },
        "hasNsfwContents": [],
        "status": "created",
        "createdAt": "2026-01-01T12:00:00Z",
        "executionTime": 0,
        "timings": {
            "inference": 0
        }
    },
    "message": "success",
    "code": 200
}
```

**Example Response — poll (completed)**:

```json
{
    "data": {
        "id": "pred_example_01",
        "model": "wan-2.6",
        "outputs": [
            "https://oss-us.gptproto.com/example/output.mp4"
        ],
        "urls": {
            "get": "https://gptproto.com/api/v3/predictions/pred_example_01/result"
        },
        "status": "completed",
        "error": null,
        "executionTime": 12345,
        "timings": {
            "inference": 12000
        },
        "has_nsfw_contents": [],
        "created_at": "2026-01-01T12:00:00Z"
    },
    "message": "success",
    "code": 200
}
```

### Status Values

- `created` — Task accepted. Use `data.id` as `result_id` for polling.
- `running` — Generation is in progress. Keep polling.
- `completed` — Finished successfully. Generated files are in `data.outputs`.
- `failed` — Generation failed. Read `data.error` for the reason.

## Usage Examples

### 1. Submit a request

```bash
curl --request POST "https://gptproto.com/api/v3/alibaba/wan-2.6/text-to-video" \
    --header "Authorization: Bearer $GPTPROTO_API_KEY" \
    --header "Content-Type: application/json" \
    --data '{
        "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
        "negative_prompt": "",
        "size": "1920*1080",
        "duration": 5,
        "prompt_extend": true,
        "audio": "",
        "shot_type": "",
        "seed": 1
    }'
```

```python
import os
import requests

url = "https://gptproto.com/api/v3/alibaba/wan-2.6/text-to-video"
headers = {
    "Authorization": f"Bearer {os.environ['GPTPROTO_API_KEY']}",
    "Content-Type": "application/json"
}
payload = {
    "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
    "negative_prompt": "",
    "size": "1920*1080",
    "duration": 5,
    "prompt_extend": True,
    "audio": "",
    "shot_type": "",
    "seed": 1
}

response = requests.request("POST", url, headers=headers, json=payload)
print(response.json())
```

```typescript
const response = await fetch("https://gptproto.com/api/v3/alibaba/wan-2.6/text-to-video", {
    method: "POST",
    headers: {
        "Authorization": `Bearer ${process.env.GPTPROTO_API_KEY}`,
        "Content-Type": "application/json",
    },
    body: JSON.stringify({
        prompt: "A tiny origami fox sailing a teacup across a moonlit puddle",
        negative_prompt: "",
        size: "1920*1080",
        duration: 5,
        prompt_extend: true,
        audio: "",
        shot_type: "",
        seed: 1,
    }),
});

const data = await response.json();
console.log(data);
```

### 2. Poll until the task finishes

Replace `YOUR_RESULT_ID` with `data.id` from the submit response (or call `data.urls.get` directly), and keep polling every 1–3 seconds while `status` is `created` or `running`.

```bash
result_id="YOUR_RESULT_ID"

curl --request GET "https://gptproto.com/api/v3/predictions/$result_id/result" \
    --header "Authorization: Bearer $GPTPROTO_API_KEY"
```

```python
import os
import requests

result_id = "YOUR_RESULT_ID"
url = f"https://gptproto.com/api/v3/predictions/{result_id}/result"
headers = {
    "Authorization": f"Bearer {os.environ['GPTPROTO_API_KEY']}"
}
response = requests.request("GET", url, headers=headers)
print(response.json())
```

```typescript
const result_id = "YOUR_RESULT_ID";
const response = await fetch(`https://gptproto.com/api/v3/predictions/${result_id}/result`, {
    method: "GET",
    headers: {
        "Authorization": `Bearer ${process.env.GPTPROTO_API_KEY}`,
    },
});

const data = await response.json();
console.log(data);
```

## Example Prompts

- A highly cinematic 10-second shot.[0–3 seconds] A first-person POV camera rapidly pushes forward, gliding through a cold, dark stone tunnel, racing toward a bright exit ahead. The environment feels damp and shadowy. In the background audio, the sound of a woman’s heavy breathing…
- glowing blue fox runs across a bioluminescent forest at night. Mushrooms pulse with soft light as particles float in the air. The camera follows close behind the fox, weaving between trees. Magical atmosphere, vibrant colors, fantasy cinematic style, sense of wonder and discovery
- Extremely fast paced cinematic FPV flying through a post apocalyptic world reclaimed by nature, showcasing collapsed megastructures, overgrown skyscrapers, rusted bridges, abandoned vehicles, flooded streets, broken highways, and ruined industrial zones, with dynamic low altitude…

## HTTP Status Codes

- `400` — malformed body, a parameter or value this model does not accept, or input blocked by the provider's content moderation
- `401` — missing or invalid API key
- `403` — insufficient credits
- `413` — request body too large
- `429` — rate limited; retry with backoff
- `500` — An internal server error occurred
- `502` — An internal server error occurred
- `504` — Gateway timeout — upstream service did not respond in time; retry later

## Additional Resources

- [Model playground](https://gptproto.com/model/qwen/wan-2.6)
- [API documentation](https://docs.gptproto.com)
- [All models](https://gptproto.com/model)
- [API keys](https://gptproto.com/dashboard/api-key)
- [Platform overview for LLMs](https://gptproto.com/llms-full.txt)
- Any other model: `https://gptproto.com/model/{vendor}/{model}/{scene}/llms.txt`