> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apimart.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# GPT-Image-2.5 Image Generation
> - Choose between gpt-image-2.5-flare and gpt-image-2.5-sunburst
- Asynchronous processing returns a task_id for status queries
- Supports text-to-image and image editing with up to 16 reference images
- Supports 15 aspect ratios, exact pixel dimensions, and 1K / 2K / 4K resolution tiers
- Supports low / medium / high / xhigh / max quality levels
**Model selection:** `gpt-image-2.5-flare` is faster and works well for everyday high-quality images, batch generation, and rapid prototyping. `gpt-image-2.5-sunburst` prioritizes editing precision for production assets, advertising creatives, and detailed multi-turn editing. Both models use the same pricing.
```bash cURL theme={null}
curl --request POST \
--url https://api.apimart.ai/v1/images/generations \
--header 'Authorization: Bearer ' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-image-2.5-flare",
"prompt": "a cozy reading nook by a rainy window, warm lamp light, cinematic lighting",
"size": "1:1",
"resolution": "1k",
"quality": "medium",
"n": 1
}'
```
```python Python theme={null}
import requests
response = requests.post(
"https://api.apimart.ai/v1/images/generations",
headers={
"Authorization": "Bearer ",
"Content-Type": "application/json",
},
json={
"model": "gpt-image-2.5-flare",
"prompt": "a cozy reading nook by a rainy window, warm lamp light, cinematic lighting",
"size": "1:1",
"resolution": "1k",
"quality": "medium",
"n": 1,
},
)
print(response.json())
```
```javascript JavaScript theme={null}
const response = await fetch(
"https://api.apimart.ai/v1/images/generations",
{
method: "POST",
headers: {
Authorization: "Bearer ",
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "gpt-image-2.5-flare",
prompt: "a cozy reading nook by a rainy window, warm lamp light, cinematic lighting",
size: "1:1",
resolution: "1k",
quality: "medium",
n: 1,
}),
},
);
console.log(await response.json());
```
```json 200 theme={null}
{
"code": 200,
"data": [
{
"status": "submitted",
"task_id": "task_01KXXXXXXXXXXXXXXX"
}
]
}
```
```json 400 theme={null}
{
"error": {
"code": 400,
"message": "Invalid request parameters",
"type": "invalid_request_error"
}
}
```
```json 401 theme={null}
{
"error": {
"code": 401,
"message": "Authentication failed. Check your API key.",
"type": "authentication_error"
}
}
```
```json 402 theme={null}
{
"error": {
"code": 402,
"message": "Insufficient account balance",
"type": "payment_required"
}
}
```
```json 429 theme={null}
{
"error": {
"code": 429,
"message": "Too many requests. Try again later.",
"type": "rate_limit_error"
}
}
```
## Authentication
All endpoints use Bearer Token authentication. Get your key from the [API Key page](https://apimart.ai/keys).
```
Authorization: Bearer YOUR_API_KEY
```
## Choose a model
| Model | Strength | Recommended use |
| ------------------------ | ------------------------------------- | ------------------------------------------------------------------------------------- |
| `gpt-image-2.5-flare` | Default option with faster generation | Social content, product images, visual search, rapid prototypes, and batch generation |
| `gpt-image-2.5-sunburst` | Prioritizes editing precision | Production product images, advertising creatives, and detailed multi-turn editing |
The two models have identical token usage and pricing for the same parameters. Select one based on the speed-versus-quality tradeoff.
Compared with `gpt-image-2`, GPT-Image-2.5 adds the `xhigh` and `max` quality levels. Its `medium` and `high` levels use roughly one quarter of the output tokens of the previous generation's levels with the same names.
## Request parameters
Image model name: `gpt-image-2.5-flare` or `gpt-image-2.5-sunburst`.
Text description of the image to generate or edit. Describe the subject, scene, composition, style, lighting, and anything that must be preserved or changed.
Output aspect ratio or exact pixel dimensions.
* `auto`: let the model choose from the prompt or reference images
* Aspect ratio: `1:1`, `3:2`, `2:3`, `4:3`, `3:4`, `5:4`, `4:5`, `16:9`, `9:16`, `2:1`, `1:2`, `21:9`, `9:21`, `3:1`, `1:3`
* Exact dimensions, such as `1600x1200`
For image-to-image requests, omit `size` to let the service calculate dimensions from the input aspect ratio and `resolution`.
Resolution tier used with an aspect-ratio `size`: `1k`, `2k`, or `4k`. This field is ignored for exact pixel dimensions.
Image quality: `low`, `medium`, `high`, `xhigh`, `max`, or `auto`.
`xhigh` and `max` are exclusive to GPT-Image-2.5. Sending them to `gpt-image-2` returns HTTP 400; the request is not silently downgraded.
Number of images to generate. Range: `1` to `4`. Pass a number, not a string.
Output file format: `png`, `jpeg`, or `webp`.
Compression level from `0` to `100`. Only applies to `jpeg` and `webp`.
Background mode: `transparent`, `opaque`, or `auto`.
`background: "transparent"` requires `output_format: "png"` or `output_format: "webp"`. JPEG has no alpha channel.
Content moderation level: `auto` or `low`. APIMart explicitly sends `low` when omitted; an explicit `auto` value is passed through.
Reference image URLs for image-to-image generation or editing. Up to `16` images are accepted, and including this field activates editing mode.
Only publicly accessible HTTP(S) URLs are accepted. Upload local images with `POST /v1/uploads/images`, then use the returned `url`.
## Size rules
Exact pixel dimensions must satisfy all of these constraints:
* Width and height are both multiples of `16`
* Neither side exceeds `3840` pixels
* Long-side to short-side ratio is at most `3:1`
* Total pixel count is between `655,360` and `8,294,400`
Resolutions above 2560×1440 are experimental and may be less stable than common sizes.
### Aspect ratio and resolution mapping
| `size` | `1k` | `2k` | `4k` |
| ------ | --------- | --------- | --------- |
| `1:1` | 1024×1024 | 2048×2048 | 2880×2880 |
| `3:2` | 1536×1024 | 2048×1360 | 3520×2336 |
| `2:3` | 1024×1536 | 1360×2048 | 2336×3520 |
| `4:3` | 1024×768 | 2048×1536 | 3312×2480 |
| `3:4` | 768×1024 | 1536×2048 | 2480×3312 |
| `5:4` | 1280×1024 | 2560×2048 | 3216×2576 |
| `4:5` | 1024×1280 | 2048×2560 | 2576×3216 |
| `16:9` | 1536×864 | 2048×1152 | 3840×2160 |
| `9:16` | 864×1536 | 1152×2048 | 2160×3840 |
| `2:1` | 2048×1024 | 2688×1344 | 3840×1920 |
| `1:2` | 1024×2048 | 1344×2688 | 1920×3840 |
| `21:9` | 2016×864 | 2688×1152 | 3840×1648 |
| `9:21` | 864×2016 | 1152×2688 | 1648×3840 |
| `3:1` | 1536×512 | 3072×1024 | 3840×1280 |
| `1:3` | 512×1536 | 1024×3072 | 1280×3840 |
You may also pass any exact dimensions that satisfy the size rules; they do not need to appear in this table.
## Usage examples
### Text to image
```json theme={null}
{
"model": "gpt-image-2.5-flare",
"prompt": "a sky garden in a futuristic city, morning mist, architectural photography",
"size": "16:9",
"resolution": "2k",
"quality": "high",
"n": 1
}
```
### Precision editing with Sunburst
```json theme={null}
{
"model": "gpt-image-2.5-sunburst",
"prompt": "preserve the product and package text, replace the background with a soft off-white studio, and add a natural shadow",
"image_urls": ["https://example.com/product.png"],
"resolution": "2k",
"quality": "xhigh"
}
```
### Transparent background
```json theme={null}
{
"model": "gpt-image-2.5-flare",
"prompt": "e-commerce product photo of white sneakers, complete subject, transparent background",
"size": "1:1",
"resolution": "2k",
"quality": "high",
"background": "transparent",
"output_format": "png"
}
```
## Submission and task query
Successful submission immediately returns an asynchronous task ID. `data` is an array; read `data[0].task_id`.
Call the [task status endpoint](/en/api-reference/tasks/status) with the returned ID. Poll every 2–5 seconds until the status becomes `completed` or `failed`. Use `POST /v1/tasks/batch` to query multiple tasks.
```json theme={null}
{
"code": 200,
"data": {
"id": "task_01KXXXXXXXXXXXXXXX",
"status": "completed",
"progress": 100,
"cost": 0.01325,
"credits_cost": 0.1325,
"result": {
"images": [
{
"url": ["https://upload.apimart.ai/f/image/example.png"],
"expires_at": 1789000000
}
]
},
"usage": {
"input_tokens": 16,
"output_tokens": 439,
"total_tokens": 455
}
}
}
```
Image URLs are located at `data.result.images[].url[]`. Download and store them promptly instead of relying on temporary URLs.
| Status | Meaning |
| ------------ | --------------------------------------------------------------------- |
| `submitted` | Task submitted |
| `processing` | Generation in progress |
| `completed` | Generation succeeded; `result.images` is available |
| `failed` | Generation failed; check `error.message`; reserved funds are refunded |
## Billing
GPT-Image-2.5 is billed by actual token usage. Flare and Sunburst use identical rates. Check the [pricing page](https://apimart.ai/pricing) or `/api/pricing` for current account pricing.
| Item | Price per 1M tokens |
| ------------------ | ------------------- |
| Image output | \$30.00 |
| Image input | \$8.00 |
| Cached image input | \$2.00 |
| Text input | \$5.00 |
| Cached text input | \$1.25 |
### 1024×1024 output token reference
| `quality` | Output tokens | Official output cost |
| --------- | ------------- | -------------------- |
| `low` | 196 | \$0.00588 |
| `medium` | 439 | \$0.01317 |
| `high` | 1756 | \$0.05268 |
| `xhigh` | 3122 | \$0.09366 |
| `max` | 7024 | \$0.21072 |
With `quality: "auto"`, the model chooses the actual level at runtime. The service reserves funds using the `max` level for the selected size and settles against actual usage when the task finishes. Specify `quality` when available balance matters.
For `n > 1`, the reservation scales linearly with the requested image count. Final billing uses the number of images actually generated, and failed tasks are refunded automatically.
## Output token reference
Values are per generated image. Actual billing also includes prompt and reference-image input tokens.
| Size | Pixels | low | medium | high | xhigh | max |
| --------- | --------- | --- | ------ | ---- | ----- | ----- |
| `1:1` | 1024×1024 | 196 | 439 | 1756 | 3122 | 7024 |
| `3:2` | 1536×1024 | 158 | 343 | 1372 | 2459 | 5488 |
| `2:3` | 1024×1536 | 158 | 343 | 1372 | 2459 | 5488 |
| `4:3` | 1024×768 | 134 | 301 | 1204 | 2140 | 4815 |
| `3:4` | 768×1024 | 134 | 301 | 1204 | 2140 | 4815 |
| `5:4` | 1280×1024 | 173 | 378 | 1510 | 2702 | 6119 |
| `4:5` | 1024×1280 | 173 | 378 | 1510 | 2702 | 6119 |
| `16:9` | 1536×864 | 120 | 280 | 1078 | 1917 | 4312 |
| `9:16` | 864×1536 | 120 | 280 | 1078 | 1917 | 4312 |
| `2:1` | 2048×1024 | 132 | 295 | 1180 | 2098 | 4720 |
| `1:2` | 1024×2048 | 132 | 295 | 1180 | 2098 | 4720 |
| `21:9` | 2016×864 | 105 | 225 | 943 | 1617 | 3682 |
| `9:21` | 864×2016 | 105 | 225 | 943 | 1617 | 3682 |
| `3:1` | 1536×512 | 56 | 134 | 535 | 937 | 2140 |
| `1:3` | 512×1536 | 56 | 134 | 535 | 937 | 2140 |
| `1:1@2k` | 2048×2048 | 397 | 892 | 3568 | 6343 | 14272 |
| `3:2@2k` | 2048×1360 | 211 | 460 | 1838 | 3216 | 7351 |
| `2:3@2k` | 1360×2048 | 211 | 460 | 1838 | 3216 | 7351 |
| `4:3@2k` | 2048×1536 | 247 | 556 | 2223 | 3952 | 8892 |
| `3:4@2k` | 1536×2048 | 247 | 556 | 2223 | 3952 | 8892 |
| `5:4@2k` | 2560×2048 | 377 | 826 | 3303 | 5911 | 13385 |
| `4:5@2k` | 2048×2560 | 377 | 826 | 3303 | 5911 | 13385 |
| `16:9@2k` | 2048×1152 | 157 | 367 | 1413 | 2511 | 5650 |
| `9:16@2k` | 1152×2048 | 157 | 367 | 1413 | 2511 | 5650 |
| `2:1@2k` | 2688×1344 | 180 | 405 | 1617 | 2874 | 6466 |
| `1:2@2k` | 1344×2688 | 180 | 405 | 1617 | 2874 | 6466 |
| `21:9@2k` | 2688×1152 | 143 | 306 | 1285 | 2202 | 5016 |
| `9:21@2k` | 1152×2688 | 143 | 306 | 1285 | 2202 | 5016 |
| `3:1@2k` | 3072×1024 | 103 | 247 | 988 | 1729 | 3952 |
| `1:3@2k` | 1024×3072 | 103 | 247 | 988 | 1729 | 3952 |
| `1:1@4k` | 2880×2880 | 659 | 1483 | 5930 | 10542 | 23719 |
| `3:2@4k` | 3520×2336 | 450 | 982 | 3926 | 6870 | 15703 |
| `2:3@4k` | 2336×3520 | 450 | 982 | 3926 | 6870 | 15703 |
| `4:3@4k` | 3312×2480 | 491 | 1104 | 4413 | 7845 | 17650 |
| `3:4@4k` | 2480×3312 | 491 | 1104 | 4413 | 7845 | 17650 |
| `5:4@4k` | 3216×2576 | 535 | 1173 | 4690 | 8393 | 19006 |
| `4:5@4k` | 2576×3216 | 535 | 1173 | 4690 | 8393 | 19006 |
| `16:9@4k` | 3840×2160 | 371 | 865 | 3336 | 5930 | 13342 |
| `9:16@4k` | 2160×3840 | 371 | 865 | 3336 | 5930 | 13342 |
| `2:1@4k` | 3840×1920 | 300 | 675 | 2700 | 4799 | 10798 |
| `1:2@4k` | 1920×3840 | 300 | 675 | 2700 | 4799 | 10798 |
| `21:9@4k` | 3840×1648 | 234 | 500 | 2099 | 3598 | 8196 |
| `9:21@4k` | 1648×3840 | 234 | 500 | 2099 | 3598 | 8196 |
| `3:1@4k` | 3840×1280 | 139 | 332 | 1328 | 2324 | 5311 |
| `1:3@4k` | 1280×3840 | 139 | 332 | 1328 | 2324 | 5311 |
## Limits and common errors
| Item | Limit or handling |
| ------------------------ | ------------------------------------------------------------ |
| Images per request (`n`) | 1–4 |
| Reference images | Up to 16 |
| Output format | PNG / JPEG / WebP |
| Transparent background | PNG / WebP only |
| Partial streaming images | Not supported |
| Unsupported `quality` | `xhigh` / `max` require GPT-Image-2.5 |
| Invalid exact dimensions | Use multiples of 16 within the pixel and aspect-ratio limits |
## Response
Response status code; 200 when submission succeeds.
Submission response data.
Initially `submitted`.
Unique task ID used to query generation status and results.