# Sam 3 · Video API

> Segmentiert und verfolgt Objekte in einem Video anhand von Text-, Punkt- oder Box-Prompts und gibt ein maskiertes Video zurück

Erkennt und verfolgt Objekte in einem über `video_url` bereitgestellten Video. Benenne Ziele in `prompt`, indem du mehrere durch Kommas trennst, oder verwende `point_prompts` und `box_prompts`. `detection_threshold` reicht von 0.1 bis 1, und `apply_mask` steuert, ob die Maske auf das Video gezeichnet wird. Die Ausgabe ist mp4 oder webm sowie ein Zip-Archiv mit Bounding-Box-Overlays pro Frame.

- 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.ru/v1/run/sam-3/video`
- Quote: `POST https://api.doitong.ru/v1/quote/sam-3/video`
- Docs page: https://doitong.com/de/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.ru/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 |  |  | Video-URL für die Segmentierung. |
| `prompt` |  | string | no | `` |  | Segmentierungs-Prompt zur Unterstützung mehrerer getrackter Objekte durch kommagetrennte Beschreibungen wie 'person, cloth'. |
| `text_prompt` |  | string \| null | no |  |  | [VERALTET] Altes Eingabefeld aus Gründen der Abwärtskompatibilität; stattdessen 'prompt' verwenden. |
| `point_prompts` |  | array of object {x, y, label, object_id} | no | `[]` |  | Sammlung punktbasierter Prompts. |
| `point_prompts[].x` |  | integer \| null | no |  |  | Prompt-Position auf der X-Achse. |
| `point_prompts[].y` |  | integer \| null | no |  |  | Prompt-Position auf der Y-Achse. |
| `point_prompts[].label` |  | enum (integer) \| null | no |  | `0`, `1` | Vordergrund-/Hintergrundbezeichnung: 1 bedeutet Vordergrund, 0 Hintergrund. |
| `point_prompts[].object_id` |  | integer \| null | no |  |  | Optionaler Bezeichner zur Gruppierung von Prompts, die ein einzelnes Objekt verfeinern. Wenn zusätzlich Text als Prompt angegeben wird, bestimmt dieser Bezeichner, welches erkannte Objekt durch diese Punkte verfeinert wird. |
| `box_prompts` |  | array of object {x_min, y_min, x_max, y_max, object_id} | no | `[]` |  | Box-Prompts als Koordinatentupel (x_min, y_min, x_max, y_max). |
| `box_prompts[].x_min` |  | integer \| null | no |  |  | Minimaler x-Koordinatenwert der Box. |
| `box_prompts[].y_min` |  | integer \| null | no |  |  | Minimaler y-Koordinatenwert der Box. |
| `box_prompts[].x_max` |  | integer \| null | no |  |  | Maximale X-Koordinate der Box. |
| `box_prompts[].y_max` |  | integer \| null | no |  |  | Maximale Y-Koordinate der Box. |
| `box_prompts[].object_id` |  | integer \| null | no |  |  | Optionaler Bezeichner zur Gruppierung von Boxen, die ein einzelnes Objekt verfeinern. |
| `apply_mask` |  | boolean | no | `true` |  | Gibt an, ob Maskierung auf das Video angewendet werden soll. |
| `video_output_type` |  | enum (string) | no | `X264 (.mp4)` | `X264 (.mp4)`, `VP9 (.webm)` | Ausgabetyp des resultierenden Videos. |
| `detection_threshold` |  | number | no | `0.5` | 0.1 – 1 | Konfidenzgrenzwert für die Erkennung. Ein niedrigerer Wert erhöht die Anzahl der Erkennungen zulasten der Präzision; der Bereich ist 0.0–1.0. |

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.ru/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.ru/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.ru/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.ru/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.ru/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.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size} | Durch Segmentierung erzeugtes Video. |
| `boundingbox_frames_zip` | object {url, content_type, file_name, file_size} \| null | ZIP-Archiv mit Bounding-Box-Overlays für jedes Frame. |

## 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.ru/v1/models/sam-3/video