# Sam 3 · Video API

> 텍스트, 포인트 또는 박스 프롬프트로 동영상의 객체를 세분화하고 추적하여 마스크가 적용된 동영상을 반환

`video_url`로 제공된 동영상 전체에서 객체를 감지하고 추적합니다. `prompt`에서 대상 이름을 쉼표로 구분해 여러 개 지정하거나 `point_prompts` 및 `box_prompts`를 사용합니다. `detection_threshold`의 범위는 0.1~1이며, `apply_mask`로 동영상에 마스크를 그릴지 제어합니다. 출력은 mp4 또는 webm이며, 프레임별 바운딩 박스 오버레이가 포함된 zip도 반환됩니다.

- Model id: `sam-3/video`
- Made by: Meta
- Category: Video to Video
- Output: video
- Price per 16 frames of your input video: $0.005 / 16 frames of input video (cheapest run $0.005 (5 API credits))
- Run: `POST https://api.doitong.com/v1/run/sam-3/video`
- Quote: `POST https://api.doitong.com/v1/quote/sam-3/video`
- Docs page: https://doitong.com/ko/developers/models/sam-3/video

## Pricing

Price: **$0.005 / 16 frames of input video**. Price per 16 frames of your input video: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

The full example input below costs $0.095 = 95 API credits (19 × 16 frames input video × $0.005).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.com/v1/quote/sam-3/video` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

Send either the canonical name or the native field name (not both). Canonical names are the same across models.

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `video_url` | `video` | string (video URL) | yes |  |  | 분할할 동영상 URL입니다. |
| `prompt` |  | string | no | `` |  | 'person, cloth'와 같이 쉼표로 구분된 설명을 사용하여 여러 추적 객체를 지원하는 분할 프롬프트입니다. |
| `text_prompt` |  | string \| null | no |  |  | [DEPRECATED] 이전 버전과의 호환성을 위해 유지되는 레거시 입력입니다. 대신 'prompt'를 사용하세요. |
| `point_prompts` |  | array of object {x, y, label, object_id} | no | `[]` |  | 포인트 기반 프롬프트 모음입니다. |
| `point_prompts[].x` |  | integer \| null | no |  |  | X축에서의 프롬프트 위치입니다. |
| `point_prompts[].y` |  | integer \| null | no |  |  | Y축에서의 프롬프트 위치입니다. |
| `point_prompts[].label` |  | enum (integer) \| null | no |  | `0`, `1` | 전경/배경 레이블입니다. 1은 전경, 0은 배경을 나타냅니다. |
| `point_prompts[].object_id` |  | integer \| null | no |  |  | 단일 객체를 세분화하는 프롬프트를 그룹화하는 선택적 식별자입니다. 텍스트도 프롬프트로 제공하면 이 식별자가 해당 포인트로 세분화할 감지된 객체를 결정합니다. |
| `box_prompts` |  | array of object {x_min, y_min, x_max, y_max, object_id} | no | `[]` |  | (x_min, y_min, x_max, y_max) 좌표 튜플로 나열된 상자 프롬프트입니다. |
| `box_prompts[].x_min` |  | integer \| null | no |  |  | 상자의 최소 x 좌표입니다. |
| `box_prompts[].y_min` |  | integer \| null | no |  |  | 상자의 최소 y 좌표입니다. |
| `box_prompts[].x_max` |  | integer \| null | no |  |  | 상자의 최대 X 좌표입니다. |
| `box_prompts[].y_max` |  | integer \| null | no |  |  | 상자의 최대 Y 좌표입니다. |
| `box_prompts[].object_id` |  | integer \| null | no |  |  | 단일 객체를 세분화하는 상자를 그룹화하는 선택적 식별자입니다. |
| `apply_mask` |  | boolean | no | `true` |  | 동영상에 마스킹을 적용할지 여부입니다. |
| `video_output_type` |  | enum (string) | no | `X264 (.mp4)` | `X264 (.mp4)`, `VP9 (.webm)` | 결과 동영상의 출력 유형입니다. |
| `detection_threshold` |  | number | no | `0.5` | 0.1 – 1 | 탐지 신뢰도 기준값입니다. 값을 낮추면 정밀도가 낮아지는 대신 탐지 수가 증가하며, 범위는 0.0–1.0입니다. |

File parameters (`video`) take a public `https://` URL, a `data:` URI up to 10 MB, or the `file_url` of an upload from `POST /files`.

## Example input

Required parameters only:

```json
{
  "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
```

A fuller example:

```json
{
  "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/sam-3/video?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.com/v1";
const headers = {
  "Authorization": "Bearer " + process.env.DOITONG_API_KEY,
  "Content-Type": "application/json",
};

// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/sam-3/video?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
  }),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);

// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
  res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
  request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
```

### Python

```python
import os
import requests

API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/sam-3/video", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
    raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])

# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
    request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
    raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
```

### MCP

The MCP server at `https://api.doitong.com/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "sam-3/video",
      "input": {
        "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "sam-3/video",
  "status": "succeeded",
  "created_at": "2026-10-20T12:00:00Z",
  "started_at": "2026-10-20T12:00:02Z",
  "completed_at": "2026-10-20T12:00:09Z",
  "queue_position": null,
  "cost": {
    "credits": 95,
    "usd": "0.095",
    "charged": 95,
    "refunded": 0
  },
  "input": {
    "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
  },
  "output": {
    "video": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_name": "0-output.mp4",
      "file_size": 1843200
    },
    "boundingbox_frames_zip": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_name": "0-output.mp4",
      "file_size": 1843200
    }
  },
  "outputs": [
    {
      "type": "video",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_size": 1843200,
      "width": 1280,
      "height": 720,
      "duration": 5,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size} | 분할 결과로 생성된 동영상입니다. |
| `boundingbox_frames_zip` | object {url, content_type, file_name, file_size} \| null | 각 프레임의 경계 상자 오버레이를 포함하는 ZIP 아카이브입니다. |

## Related models

- [Sam 3 · Image RLE](https://doitong.com/developers/models/sam-3/image-rle.md) (`sam-3/image-rle`): $0.005 / run
- [Segment Anything Model 3](https://doitong.com/developers/models/sam-3/image.md) (`sam-3/image`): $0.005 / run
- [Sam 3 · Video RLE](https://doitong.com/developers/models/sam-3/video-rle.md) (`sam-3/video-rle`): $0.005 / 16 frames of input video
- [Sam 3 · 3D Body](https://doitong.com/developers/models/sam-3/3d-body.md) (`sam-3/3d-body`): $0.02 / run
- [Sam 3 · 3D Objects](https://doitong.com/developers/models/sam-3/3d-objects.md) (`sam-3/3d-objects`): $0.02 / run
- [Sam 3 · 3D Align](https://doitong.com/developers/models/sam-3/3d-align.md) (`sam-3/3d-align`): $0.02 / run
- [Sam 3 · Image · Embed](https://doitong.com/developers/models/sam-3/image/embed.md) (`sam-3/image/embed`): $0.005 / run
- [Sam 3 1 · Video](https://doitong.com/developers/models/sam-3-1/video.md) (`sam-3-1/video`): $0.01 / 16 frames of input video

## Links

- HTML page: https://doitong.com/developers/models/sam-3/video
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=sam-3/video
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/sam-3/video