GPT Image 2.5

The two models

Both accept text and image input, return images, read the same parameters, and meter on the same token rate card. The number of tokens an image consumes differs between them, so equal rates do not mean equal cost per image.

Quick example

curl https://reapi.ai/api/v1/images/generations \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2.5-flare",
    "prompt": "An editorial poster for a ceramic kettle, soft window light",
    "size": "1536x1024",
    "quality": "high"
  }'

import requests

resp = requests.post(
    "https://reapi.ai/api/v1/images/generations",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "model": "gpt-image-2.5-flare",
        "prompt": "An editorial poster for a ceramic kettle, soft window light",
        "size": "1536x1024",
        "quality": "high",
    },
)
task_id = resp.json()["id"]

const res = await fetch('https://reapi.ai/api/v1/images/generations', {
  method: 'POST',
  headers: {
    Authorization: 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'gpt-image-2.5-flare',
    prompt: 'An editorial poster for a ceramic kettle, soft window light',
    size: '1536x1024',
    quality: 'high',
  }),
});
const { id } = await res.json();

body := strings.NewReader(`{
  "model": "gpt-image-2.5-flare",
  "prompt": "An editorial poster for a ceramic kettle, soft window light",
  "size": "1536x1024",
  "quality": "high"
}`)

req, _ := http.NewRequest("POST", "https://reapi.ai/api/v1/images/generations", body)
req.Header.Set("Authorization", "Bearer YOUR_API_KEY")
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)

Endpoint

POST https://reapi.ai/api/v1/images/generations

Headers:

The call returns a task id immediately. Poll GET https://reapi.ai/api/v1/tasks/:id until status is completed or failed.

Parameters

Size rules

Beyond the three standard sizes, a custom WIDTHxHEIGHT is accepted when all of the following hold:

• Both edges are divisible by 16.
• The aspect ratio is between 1:3 and 3:1.
• Neither edge exceeds 3840 pixels.
• Total pixel count is between 655,360 and 8,294,400.

Resolutions above 2560x1440 are marked experimental by the model creator.

Quality tiers

xhigh and max are new in this generation; earlier GPT Image models stop at high. Higher tiers consume more tokens, take longer, and cost more. Use low for drafts and compare tiers before settling on one for production output.

Transparent backgrounds

background: "transparent" returns a real alpha channel and requires output_format of png or webp. JPEG carries no alpha channel and cannot hold a transparent background regardless of the setting.

Modes

There is no mode parameter. Which fields you send is the mode:

Media inputs

Every media input is a public HTTP(S) URL. Base64 payloads and data: URIs are rejected on every reAPI endpoint, on every model. Upload your source somewhere reachable and pass the link.

Pricing dimensions

Billing scales with the tokens an image consumes, which is driven by:

• Quality — each tier up the ladder costs materially more.
• Size — more pixels means more output tokens.
• Reference images — each input image contributes input tokens.
• n — every delivered image is billed.

Current rates are on the model page.

Bill formula

credits = ceil(price_per_image_usd × n × 1000)

1 credit = $0.001 USD, and $1 buys 1,000 credits. Credits never expire. Failed generations are refunded automatically, so you are only charged for images you receive.

Output schema

{
  "id": "task_...",
  "status": "completed",
  "output": {
    "image_urls": ["https://..."]
  },
  "usage": {
    "credits": 0
  }
}

Generated links expire. Mirror them to your own storage if you need them long term.

Errors

The full catalog lives at /docs/api/errors.

Tips

• Spell out layout, materials and lighting. This model rewards long, specific briefs and holds the requested direction as instructions get more detailed.
• For an edit, name what must NOT change as well as what must. Preservation is the model's strong suit and stating it makes the result more predictable.
• Reach for input_fidelity: "high" when a face, logo or texture has to survive verbatim, and low when you want more freedom from the source.
• Pick Sunburst when a long chain of edits has to stay coherent; pick Flare when throughput matters more than the last few percent of edit precision.
• jpeg encodes faster than png. Use it when latency matters and you do not need transparency.

Related

• GPT Image 2 — the shipping previous generation
• Images API overview
• Task polling