# GPT Image 2 (gpt-image-2)

Vendor: OpenAI
Model ID: `gpt-image-2`
Base URL: `https://api.mulerouter.ai`
Type: Inference API (async task-based)

## Description
OpenAI's next-generation image model supporting text-to-image generation and image editing up to 4K resolution with multiple quality tiers

## Variant: Image Edit

Endpoint: `POST /vendors/openai/v1/gpt-image-2/edit`

### Input Schema
The API accepts the following input parameters:

- **`n`** (`integer`, _optional_):
  Number of edited images to generate.
  - Default: `1`
  - Range: `1` to `4`

- **`mask`** (`string`, _optional_):
  Optional mask image (URL or Base64-encoded) to specify the edit region.

- **`size`** (`string`, _optional_):
  Output image resolution.
  - Options: `"1024x1024"`, `"1536x1024"`, `"1024x1536"`, `"2048x2048"`, `"2048x1152"`, `"3840x2160"`, `"2160x3840"`, `"auto"`
  - Default: `"auto"`

- **`format`** (`string`, _optional_):
  Output image format.
  - Options: `"png"`, `"jpeg"`, `"webp"`
  - Default: `"png"`

- **`images`** (`list<string>`, _required_):
  Input images to edit. Each item is a URL or Base64-encoded string.
  - Items: min: 1

- **`prompt`** (`string`, _required_):
  Text prompt describing the desired edit.

**Required Parameters Example**:

```json
{
  "prompt": "<Text prompt describing the desired edit>",
  "images": []
}
```

**Full Example**:

```json
{
  "n": 1,
  "mask": "<Optional mask image (URL or Base64-encod>",
  "size": "auto",
  "format": "png",
  "images": [],
  "prompt": "<Text prompt describing the desired edit>"
}
```

## Variant: Text to Image

Endpoint: `POST /vendors/openai/v1/gpt-image-2/generation`

### Input Schema
The API accepts the following input parameters:

- **`n`** (`integer`, _optional_):
  Number of images to generate.
  - Default: `1`
  - Range: `1` to `4`

- **`size`** (`string`, _optional_):
  Output image resolution.
  - Options: `"1024x1024"`, `"1536x1024"`, `"1024x1536"`, `"2048x2048"`, `"2048x1152"`, `"3840x2160"`, `"2160x3840"`, `"auto"`
  - Default: `"auto"`

- **`format`** (`string`, _optional_):
  Output image format.
  - Options: `"png"`, `"jpeg"`, `"webp"`
  - Default: `"png"`

- **`prompt`** (`string`, _required_):
  Text prompt to guide image generation.

- **`quality`** (`string`, _optional_):
  Image quality level.
  - Options: `"high"`, `"medium"`, `"low"`, `"auto"`
  - Default: `"high"`

**Required Parameters Example**:

```json
{
  "prompt": "<Text prompt to guide image generation>"
}
```

**Full Example**:

```json
{
  "n": 1,
  "size": "auto",
  "format": "png",
  "prompt": "<Text prompt to guide image generation>",
  "quality": "high"
}
```

## Variant: /vendors/openai/v1/gpt-image-2/edit/{task_id}

Endpoint: `POST /vendors/openai/v1/gpt-image-2/edit/{task_id}`

## Variant: /vendors/openai/v1/gpt-image-2/generation/{task_id}

Endpoint: `POST /vendors/openai/v1/gpt-image-2/generation/{task_id}`

## Usage (Async Task API)
This model uses an async task-based workflow with two API calls:
1. **Submit a task** — `POST /v1/inference/gpt-image-2` to create a generation task
2. **Poll for result** — `GET /v1/inference/gpt-image-2/{task_id}` to check status and retrieve the result

### Step 1: Submit a Task

#### cURL
```bash
curl -X POST https://api.mulerouter.ai/vendors/openai/v1/gpt-image-2/edit \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR_MULEROUTER_API_KEY>" \
  -d '{
    "prompt": "Your prompt here"
  }'
```

#### Python
```python
import requests

API_KEY = "<YOUR_MULEROUTER_API_KEY>"
ENDPOINT = "https://api.mulerouter.ai/vendors/openai/v1/gpt-image-2/edit"

response = requests.post(
    ENDPOINT,
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    },
    json={
        "prompt": "Your prompt here"
    }
)

result = response.json()
task_id = result["task_info"]["id"]
print(f"Task created: {task_id}")
```

#### Node.js / TypeScript
```typescript
const API_KEY = "<YOUR_MULEROUTER_API_KEY>";
const ENDPOINT = "https://api.mulerouter.ai/vendors/openai/v1/gpt-image-2/edit";

const response = await fetch(ENDPOINT, {
    method: "POST",
    headers: {
        "Content-Type": "application/json",
        "Authorization": `Bearer ${API_KEY}`
    },
    body: JSON.stringify({
        prompt: "Your prompt here"
    })
});

const result = await response.json();
const taskId = result.task_info.id;
console.log("Task created:", taskId);
```

#### Submit Response (202)
```json
{
  "task_info": {
    "id": "8e1e315e-b50d-4334-a231-be7d19a372f4",
    "status": "processing",
    "created_at": "2026-01-01T00:00:00.000Z"
  }
}
```

### Step 2: Poll for Result

Use the task ID from Step 1 to poll the status endpoint until the task is completed.

Endpoint: `GET /v1/inference/gpt-image-2/{task_id}`

#### cURL
```bash
curl -X GET https://api.mulerouter.ai/vendors/openai/v1/gpt-image-2/edit/<TASK_ID> \
  -H "Authorization: Bearer <YOUR_MULEROUTER_API_KEY>"
```

#### Python
```python
import time

status_url = f"https://api.mulerouter.ai/vendors/openai/v1/gpt-image-2/edit/{task_id}"

while True:
    status = requests.get(status_url, headers={
        "Authorization": f"Bearer {API_KEY}"
    }).json()
    task_status = status["task_info"]["status"]
    if task_status in ("completed", "succeeded"):
        print("Result:", status)
        break
    elif task_status == "failed":
        print("Task failed:", status)
        break
    time.sleep(5)
```

#### Node.js / TypeScript
```typescript
const statusUrl = `https://api.mulerouter.ai/vendors/openai/v1/gpt-image-2/edit/${taskId}`;

while (true) {
    const statusRes = await fetch(statusUrl, {
        headers: { "Authorization": `Bearer ${API_KEY}` }
    });
    const status = await statusRes.json();
    const taskStatus = status.task_info.status;
    if (taskStatus === "completed" || taskStatus === "succeeded") {
        console.log("Result:", status);
        break;
    } else if (taskStatus === "failed") {
        console.log("Task failed:", status);
        break;
    }
    await new Promise(r => setTimeout(r, 5000));
}
```

## Additional Resources

### Documentation

- [Model Playground](https://www.mulerouter.ai/models/gpt-image-2)
- [API Documentation](https://mulerouter.ai/docs/api-reference/endpoint/openai/gpt-image-2/generation)

### MuleRouter Platform

- [Platform Documentation](https://www.mulerouter.ai/docs)
- [API Keys Management](https://www.mulerouter.ai/app/api-keys)