# Isaac 0.1 API

> Syns-sprogmodel, der besvarer spørgsmål om et billede som tekst, bokse, punkter eller polygoner

Modtager en `image_url` og en `prompt` og returnerer et svar i det format, der er angivet af `response_style`. Tekst egner sig til billedtekster og beskrivelser, mens boks-, punkt- og polygonformater tilføjer lokaliseringsoutput. Resultatet indeholder det genererede output, et partial-output-flag, en eventuel fejlmeddelelse og forbrugsoplysninger.

- Model id: `perceptron/isaac-01`
- Made by: Perceptron
- Category: Vision
- Output: text
- Estimated price per run: $0.001 / run (cheapest run $0.001 (1 API credit))
- Run: `POST https://api.doitong.com/v1/run/perceptron/isaac-01`
- Quote: `POST https://api.doitong.com/v1/quote/perceptron/isaac-01`
- Docs page: https://doitong.com/da/developers/models/perceptron/isaac-01

## Pricing

Price: **$0.001 / run**. Estimated from the typical cost of one run. The quote is exactly what the run charges.

The full example input below costs $0.001 = 1 API credit (1 request × 1 API credits).

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/perceptron/isaac-01` 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 |
|---|---|---|---|---|---|---|
| `image_url` | `image` | string (image URL) | yes |  |  | URL, der identificerer billedet til behandling. |
| `prompt` |  | string | yes |  |  | Prompt anvendt på billedet. |
| `response_style` |  | enum (string) | no | `text` | `text`, `box`, `point`, `polygon` | Vælg billedsvarformatet: alle tilstande inkluderer tekst, hvor box tilføjer afgrænsningsbokse, point tilføjer punkter, og polygon tilføjer polygoner; text alene returnerer kun tekst. Brug polygon til detaljeret segmentering eller box til lokalisering. Brug text til beskrivelser og billedtekster og point til optælling af mange objekter. |

File parameters (`image`) 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
{
  "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

## 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/perceptron/isaac-01?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }'

# 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/perceptron/isaac-01?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }),
});
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 = {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/perceptron/isaac-01", 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": "perceptron/isaac-01",
      "input": {
        "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
      },
      "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": "perceptron/isaac-01",
  "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": 1,
    "usd": "0.001",
    "charged": 1,
    "refunded": 0
  },
  "input": {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "output": {
    "output": "To determine which car is trying to park into the garage, we need to carefully observe the positions and movements of the vehicles in the image.\n\n1. **Identify the Vehicles**: \n   - There are three vehicles visible: a green truck, an orange car, and a brown car.\n   - The green truck is positioned in front of the garage entrance.\n   - The orange car is parked to the left of the green truck.\n   - The brown car is parked to the right of the green truck.\n\n2. **Analyze the Positions**:\n   - The green truck is directly in front of the garage entrance, suggesting it is in the process of moving towards the entrance.\n   - The orange car is parked parallel to the garage but is not in the path of the green truck.\n   - The brown car is also parked parallel to the garage but is not in the path of the green truck.\n\n3. **Determine the Action**:\n   - Given the position of the green truck directly in front of the garage entrance, it is most likely that the green truck is trying to park into the garage.\n   - The orange and brown cars are already parked and do not appear to be in motion or attempting to park.\n\nTherefore, the car that is trying to park into the garage is the green truck.",
    "partial": false,
    "error": "string",
    "usage": {
      "completion_tokens": 1,
      "prompt_tokens": 1,
      "total_tokens": 1
    }
  },
  "outputs": [
    {
      "type": "file",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "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 |
|---|---|---|
| `output` | string | Resultat produceret af modellen. |
| `partial` | boolean | Angiver, om outputtet er ufuldstændigt. |
| `error` | string \| null | Meddelelse, der beskriver en fejl, hvis der opstod en. |
| `usage` | object {completion_tokens, prompt_tokens, total_tokens} \| null | Forbrugsdetaljer. |

## Related models

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- [Bria Ad Delayer](https://doitong.com/developers/models/bria/ad-delayer.md) (`bria/ad-delayer`): $0.30 / request
- [Florence 2 Large Caption](https://doitong.com/developers/models/florence-2-large/caption.md) (`florence-2-large/caption`): $0.003 / run
- [Florence 2 Large Detailed Caption](https://doitong.com/developers/models/florence-2-large/detailed-caption.md) (`florence-2-large/detailed-caption`): $0.004 / run
- [Florence 2 Large OCR](https://doitong.com/developers/models/florence-2-large/ocr.md) (`florence-2-large/ocr`): $0.003 / run
- [Florence 2 Large Region To Category](https://doitong.com/developers/models/florence-2-large/region-to-category.md) (`florence-2-large/region-to-category`): $0.003 / run
- [Florence-2 Large · More Detailed Caption](https://doitong.com/developers/models/florence-2-large/more-detailed-caption.md) (`florence-2-large/more-detailed-caption`): $0.004 / run
- [Florence-2 Large · Region to Description](https://doitong.com/developers/models/florence-2-large/region-to-description.md) (`florence-2-large/region-to-description`): $0.003 / run

## Links

- HTML page: https://doitong.com/developers/models/perceptron/isaac-01
- 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=perceptron/isaac-01
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/perceptron/isaac-01