# OpenAI GPT Image 2.5 Sunburst — Text To Image

> Use GPT-Image-2.5 Sunburst API on GPTProto at 5% below official rates for precise editing, subject preservation, and detailed images up to 4K.

## Overview

- **Endpoint**: `POST https://gptproto.com/api/v3/openai/gpt-image-2.5-sunburst/text-to-image`
- **Result URL**: `GET https://gptproto.com/api/v3/predictions/{result_id}/result` — the submit response also returns the authoritative URL in `data.urls.get`
- **Model ID**: `gpt-image-2.5-sunburst`
- **Vendor**: OpenAI
- **Scene**: `text-to-image`
- **Category**: text-to-image
- **Modalities**: input text → output image
- **Playground**: https://gptproto.com/model/openai/gpt-image-2.5-sunburst
- **Other scenes of this model**: `image-edit` — same auth, different endpoint path and input schema

## Authentication

Every request needs a GPTProto API key in the `Authorization` header. Create one at https://gptproto.com/dashboard/api-key, then export it:

```bash
export GPTPROTO_API_KEY="your-api-key"
```

Header: `Authorization: Bearer $GPTPROTO_API_KEY` (plus `Content-Type: application/json` on POST).

## Pricing

Platform price by tier (USD, already includes the GPTProto discount):

- **Image Output** — $0.0285 per 1K tokens
- **Image Input** — $0.0076 per 1K tokens
- **Cached Input** — $0.0019 per 1K tokens
- **Text Input** — $0.0047 per 1K tokens

Price range: $0 – $0 per generation.

Prices may change. The model page always shows the live price: https://gptproto.com/model/openai/gpt-image-2.5-sunburst

## API Information

The API is asynchronous: POST the endpoint to create a prediction, then poll its result URL until `status` is `completed` or `failed`.

### Input Schema

The endpoint accepts the following JSON body parameters:

- **`prompt`** (`string`, _required_):

- **`n`** (`range`, _optional_):
  The number of images to generate. Must be between 1 and 10.
  - Range: 1–10

- **`quality`** (`enum`, _optional_):
  low medium high auto (default)
  - Default: `auto`
  - Options: high, medium, low, auto, xhigh, max

- **`size`** (`enum`, _optional_):
  Maximum edge length must be less than or equal to 3840px Both edges must be multiples of 16px Long edge to short edge ratio must not exceed 3:1 Total pixels must be at least 655,360 and no more than 8,294,400
  - Default: `auto`
  - Options: 1024x1024, 1536x1024, 1024x1536, 2048x2048, 2048x1152, 3840x2160, 2160x3840, auto

- **`enable_sync_mode`** (`boolean`, _optional_):
  - Default: `false`
  - Options: true, false

- **`response_format`** (`enum`, _optional_):
  - Default: `url`
  - Options: b64_json, url

**Required Parameters Example**:

```json
{
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle"
}
```

**Full Example**:

```json
{
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
  "n": null,
  "quality": "auto",
  "size": "auto",
  "enable_sync_mode": false,
  "response_format": "url"
}
```

### Output Schema

Both submit and poll return the same envelope:

- **`data.id`** (`string`): Prediction id. Use it as `result_id` when polling for the result.
- **`data.status`** (`string`): One of `created`, `running`, `completed`, `failed`. See Status Values below.
- **`data.outputs`** (`array of string`): Generated files. Empty until `status` is `completed`; then it holds the output URLs (or base64 strings when the model exposes a base64 option).
- **`data.urls.get`** (`string`): Authoritative result URL for this prediction. Prefer it over building the poll URL yourself.
- **`data.error`** (`string | null`): Failure reason when `status` is `failed`, otherwise `null`.
- **`data.executionTime`** (`integer`): Total processing time in milliseconds.
- **`data.timings.inference`** (`integer`): Model inference time in milliseconds.
- **`data.hasNsfwContents`** (`array of boolean`): Per-output moderation flags.
- **`message`** (`string`): `success` on a normal response, otherwise the error message.
- **`code`** (`integer`): Business status code. `200` means the request was accepted.

**Example Response — submit (task accepted)**:

```json
{
  "data": {
    "id": "pred_example_01",
    "model": "gpt-image-2.5-sunburst",
    "outputs": [],
    "urls": {
      "get": "https://gptproto.com/api/v3/predictions/pred_example_01/result"
    },
    "hasNsfwContents": [],
    "status": "created",
    "createdAt": "2026-01-01T12:00:00Z",
    "executionTime": 0,
    "timings": {
      "inference": 0
    }
  },
  "message": "success",
  "code": 200
}
```

**Example Response — poll (completed)**:

```json
{
  "data": {
    "id": "pred_example_01",
    "model": "gpt-image-2.5-sunburst",
    "outputs": [
      "https://oss-us.gptproto.com/example/output.png"
    ],
    "urls": {
      "get": "https://gptproto.com/api/v3/predictions/pred_example_01/result"
    },
    "status": "completed",
    "error": null,
    "executionTime": 12345,
    "timings": {
      "inference": 12000
    },
    "has_nsfw_contents": [],
    "created_at": "2026-01-01T12:00:00Z"
  },
  "message": "success",
  "code": 200
}
```

### Status Values

- `created` — Task accepted. Use `data.id` as `result_id` for polling.
- `running` — Generation is in progress. Keep polling.
- `completed` — Finished successfully. Generated files are in `data.outputs`.
- `failed` — Generation failed. Read `data.error` for the reason.

## Usage Examples

### 1. Submit a request

```bash
curl --request POST "https://gptproto.com/api/v3/openai/gpt-image-2.5-sunburst/text-to-image" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
    "n": null,
    "quality": "auto",
    "size": "auto",
    "enable_sync_mode": false,
    "response_format": "url"
  }'
```

```python
import os
import requests

url = "https://gptproto.com/api/v3/openai/gpt-image-2.5-sunburst/text-to-image"
headers = {
  "Authorization": f"Bearer {os.environ['GPTPROTO_API_KEY']}",
  "Content-Type": "application/json"
}
payload = {
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
  "n": None,
  "quality": "auto",
  "size": "auto",
  "enable_sync_mode": False,
  "response_format": "url"
}

response = requests.request("POST", url, headers=headers, json=payload)
print(response.json())
```

```typescript
const response = await fetch("https://gptproto.com/api/v3/openai/gpt-image-2.5-sunburst/text-to-image", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.GPTPROTO_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    prompt: "A tiny origami fox sailing a teacup across a moonlit puddle",
    n: null,
    quality: "auto",
    size: "auto",
    enable_sync_mode: false,
    response_format: "url",
  }),
});

const data = await response.json();
console.log(data);
```

### 2. Poll until the task finishes

Replace `YOUR_RESULT_ID` with `data.id` from the submit response (or call `data.urls.get` directly), and keep polling every 1–3 seconds while `status` is `created` or `running`.

```bash
result_id="YOUR_RESULT_ID"

curl --request GET "https://gptproto.com/api/v3/predictions/$result_id/result" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY"
```

```python
import os
import requests

result_id = "YOUR_RESULT_ID"
url = f"https://gptproto.com/api/v3/predictions/{result_id}/result"
headers = {
  "Authorization": f"Bearer {os.environ['GPTPROTO_API_KEY']}"
}
response = requests.request("GET", url, headers=headers)
print(response.json())
```

```typescript
const result_id = "YOUR_RESULT_ID";
const response = await fetch(`https://gptproto.com/api/v3/predictions/${result_id}/result`, {
  method: "GET",
  headers: {
    "Authorization": `Bearer ${process.env.GPTPROTO_API_KEY}`,
  },
});

const data = await response.json();
console.log(data);
```

## Example Prompts

- A cool young European woman in a sharp white suit performing an energetic drum solo in the middle of an elegant outdoor wedding reception, captured with one drumstick raised above her head and the other striking a cymbal, surprised guests in formal clothing surrounding her, champ…
- Extreme macro photograph of an open mechanical wristwatch movement, intricate gears and jewels, brushed steel and gold plating, dramatic studio lighting, ultra-detailed
- A young male museum security guard standing beside a dramatic Renaissance painting after closing time, secretly copying the exact heroic pose of the figure inside the artwork, one arm raised dramatically while still holding his security radio, empty gallery around him, polished w…

## HTTP Status Codes

- `400` — malformed body, a parameter or value this model does not accept, or input blocked by the provider's content moderation
- `401` — missing or invalid API key
- `403` — insufficient credits
- `413` — request body too large
- `429` — rate limited; retry with backoff
- `500` — An internal server error occurred
- `502` — An internal server error occurred
- `504` — Gateway timeout — upstream service did not respond in time; retry later

## Additional Resources

- [Model playground](https://gptproto.com/model/openai/gpt-image-2.5-sunburst)
- [All models](https://gptproto.com/model)
- [API keys](https://gptproto.com/dashboard/api-key)
- [Platform overview for LLMs](https://gptproto.com/llm-full.txt)
- Any other model: `https://gptproto.com/model/{vendor}/{model}/{scene}/llms.txt`