# Nano Banana Pro API

> Googles leistungsstärkstes Bildmodell mit zuverlässiger Beschriftung, komplexen Layouts und Ausgabe bis 4K.

Ein Gemini-basiertes Bildmodell, das den Prompt vor dem Zeichnen analysiert. Dadurch gelingen Poster, Infografiken, Menüs und Produktfotos mit lesbarem Text deutlich zuverlässiger als mit rein diffusionsbasierten Modellen. Wählen Sie eine Ausgabe in 1K, 2K oder 4K und ein beliebiges gängiges Seitenverhältnis. Optional kann das Modell Informationen im Web nachschlagen, um das Bild mit aktuellen Fakten zu verankern. Bis zu vier Bilder pro Anfrage.

- Model id: `nano-banana-pro`
- Made by: Google
- Category: Text to Image
- Output: image
- Price: from $0.15 / image (cheapest run $0.15 (150 API credits))
- Run: `POST https://api.doitong.com/v1/run/nano-banana-pro`
- Quote: `POST https://api.doitong.com/v1/quote/nano-banana-pro`
- Docs page: https://doitong.com/de/developers/models/nano-banana-pro

## Pricing

Price: **from $0.15 / image** (the lowest tier; the quote prices your exact input). Billed per generated image (`num_images`, default 1).

Price rules (the first rule that matches the input wins; a parameter you leave out counts as its default):

- When `resolution` = 4K and `enable_web_search` = true: $0.315 (315 API credits) / image
- When `resolution` = 4K: $0.30 (300 API credits) / image
- When `enable_web_search` = true: $0.165 (165 API credits) / image

The full example input below costs $0.15 = 150 API credits (1 image × 150 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/nano-banana-pro` 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 |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  | 3 – 50000 characters | Textanweisungen zur Generierung eines Bildes. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 4 | Für die Generierung angeforderte Bildanzahl. |
| `seed` |  | integer \| null | no |  |  | Seed-Wert zur Initialisierung der Zufallszahlengenerierung. |
| `aspect_ratio` |  | enum (string) \| null | no | `1:1` | `auto`, `21:9`, `16:9`, `3:2`, `4:3`, `5:4`, `1:1`, `4:5`, `3:4`, `2:3`, `9:16` | Seitenverhältnis des resultierenden Bildes. |
| `output_format` |  | enum (string) | no | `png` | `jpeg`, `png`, `webp` | Dateiformat des resultierenden Bilds. |
| `safety_tolerance` |  | enum (string) | no | `4` | `1`, `2`, `3`, `4`, `5`, `6` | Stelle die Toleranz der Inhaltsmoderation zwischen der am wenigsten restriktiven Stufe 6 und der strengsten Stufe 1 ein, die den Großteil der Inhalte blockiert. |
| `system_prompt` |  | string | no | `` | ≤ 50000 characters | Optionale, anfrageweite Vorgaben für Persona und Ausgabestil, die Gemini als Systemanweisung oder OpenAI-kompatiblen Anbietern als Systemnachricht übermittelt werden. Bei leerem Wert wird für diese Anweisung nichts gesendet. |
| `resolution` |  | enum (string) | no | `1K` | `1K`, `2K`, `4K` | Auflösung des Ausgabebilds. |
| `limit_generations` |  | boolean | no | `true` |  | Experimentelle Generierungsgrenze: Die Aktivierung von `True` überschreibt Bildanzahlangaben im Prompt und beschränkt jede Prompt-Runde auf 1 Generierung. |
| `enable_web_search` |  | boolean | no | `false` |  | Umschalter für die Websuche bei der Bildgenerierung, sodass das Modell aktuelle Online-Informationen in das Bild einbeziehen kann. |

## Example input

Required parameters only:

```json
{
  "prompt": "A minimalist poster for a jazz night called \"Blue Hour\", a saxophone silhouette against a deep blue sky, bold sans-serif title"
}
```

A fuller example:

```json
{
  "prompt": "A minimalist poster for a jazz night called \"Blue Hour\", a saxophone silhouette against a deep blue sky, bold sans-serif title",
  "aspect_ratio": "3:4",
  "resolution": "2K"
}
```

## 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/nano-banana-pro?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A minimalist poster for a jazz night called \"Blue Hour\", a saxophone silhouette against a deep blue sky, bold sans-serif title",
    "aspect_ratio": "3:4",
    "resolution": "2K"
  }'

# 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/nano-banana-pro?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "prompt": "A minimalist poster for a jazz night called \"Blue Hour\", a saxophone silhouette against a deep blue sky, bold sans-serif title",
    "aspect_ratio": "3:4",
    "resolution": "2K"
  }),
});
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 = {
    "prompt": "A minimalist poster for a jazz night called \"Blue Hour\", a saxophone silhouette against a deep blue sky, bold sans-serif title",
    "aspect_ratio": "3:4",
    "resolution": "2K",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/nano-banana-pro", 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": "nano-banana-pro",
      "input": {
        "prompt": "A minimalist poster for a jazz night called \"Blue Hour\", a saxophone silhouette against a deep blue sky, bold sans-serif title",
        "aspect_ratio": "3:4",
        "resolution": "2K"
      },
      "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": "nano-banana-pro",
  "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": 150,
    "usd": "0.15",
    "charged": 150,
    "refunded": 0
  },
  "input": {
    "prompt": "A minimalist poster for a jazz night called \"Blue Hour\", a saxophone silhouette against a deep blue sky, bold sans-serif title",
    "aspect_ratio": "3:4",
    "resolution": "2K"
  },
  "output": {
    "images": [
      {
        "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
        "content_type": "image/jpeg",
        "file_name": "0-output.jpeg",
        "file_size": 1843200,
        "width": 1024,
        "height": 768
      }
    ],
    "description": "string"
  },
  "outputs": [
    {
      "type": "image",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768,
      "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 |
|---|---|---|
| `images` | array of object {url, content_type, file_name, file_size, width, height} | Vom Modell erzeugte Bilder. |
| `description` | string | Beschreibung zu den generierten Bildern. |

## Related models

- [Nano Banana Pro Edit](https://doitong.com/developers/models/nano-banana-pro/edit.md) (`nano-banana-pro/edit`): from $0.15 / image
- [Nano Banana 2](https://doitong.com/developers/models/nano-banana-2.md) (`nano-banana-2`): from $0.06 / image
- [FLUX.1 \[dev\]](https://doitong.com/developers/models/flux/dev.md) (`flux/dev`): $0.025 / megapixel
- [FLUX.1 \[schnell\]](https://doitong.com/developers/models/flux/schnell.md) (`flux/schnell`): $0.003 / megapixel
- [FLUX.2 \[dev\]](https://doitong.com/developers/models/flux-2.md) (`flux-2`): $0.012 / megapixel
- [FLUX.2 \[pro\]](https://doitong.com/developers/models/flux-2-pro.md) (`flux-2-pro`): $0.015 / megapixel of output image
- [GPT Image 2.5 Text to Image](https://doitong.com/developers/models/openai/gpt-image-2.5/flare/text-to-image.md) (`openai/gpt-image-2.5/flare/text-to-image`): from $0.008 / image
- [Ideogram V4.5](https://doitong.com/developers/models/ideogram/v4.5.md) (`ideogram/v4.5`): from $0.03 / image

## Links

- HTML page: https://doitong.com/developers/models/nano-banana-pro
- 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=nano-banana-pro
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/nano-banana-pro