gpt-image-2.5-flare

GPT Image 2.5 Flare is a fast OpenAI image generation and editing model for text-to-image, single-image and multi-image editing, multiple sizes and quality tiers, and batches of one to eight images. It is suited to creative exploration, product experiences, marketing assets, and automated content workflows that need dependable API access. Use the playground below with representative prompts and input media, then adjust the parameters available for this model to evaluate results against your actual use case. Before a production rollout, compare output quality, subject consistency, processing time, failure handling, and estimated cost. The input methods, parameter ranges, and output options shown on this page describe the currently supported integration surface.

A luminous glass image-generation workspace with fast flowing light and precise reflections
Model ID
gpt-image-2.5-flare
Provider
OpenAI
API endpoint
/v1/images/generations
Pricing
5
Last updated
2026-09-12

Capabilities

  • Image generation through the current public endpoint
  • Editing or reference-image controls when exposed by the live schema
  • Current parameters including prompt, image_urls, size

Limitations

  • Capabilities and parameter ranges can change; pin and validate the parameter set used in production
  • Do not assume support for parameters absent from the live request schema
  • Asynchronous jobs require explicit polling, failure, timeout, and safety-status handling

Use cases

  • Product prototypes and automated content workflows
  • Batch media generation and programmatic creative tooling
  • Multi-model applications that need unified authentication, billing, and usage records

Pricing and billing examples

  • Input tokens | 5 | 1M tokens
  • Output tokens | 30 | 1M tokens

Request parameters

ParameterTypeRequiredDefaultOptions
modelstringYes
prompttextareaYes
image_urlsasset_listNo
sizesizeYesautoauto, 1024x1024, 1024x1536, 1536x1024
qualityselectYesautoauto, low, medium, high
nintegerYes1
output_formatselectYespngpng, jpeg, webp

API examples

cURL

curl -X POST 'https://api.uniall.ai/v1/images/generations' \
  -H 'Authorization: Bearer $UNIALL_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{"model":"gpt-image-2.5-flare","n":1,"output_format":"png","prompt":"Describe the output you want","quality":"auto","size":"auto"}'

Python

import json
import requests

payload = json.loads(r'''{"model":"gpt-image-2.5-flare","n":1,"output_format":"png","prompt":"Describe the output you want","quality":"auto","size":"auto"}''')
response = requests.post(
    "https://api.uniall.ai/v1/images/generations",
    headers={"Authorization": "Bearer " + UNIALL_API_KEY},
    json=payload,
)
response.raise_for_status()
result = response.json()

JavaScript

const response = await fetch("https://api.uniall.ai/v1/images/generations", {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${process.env.UNIALL_API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({"model":"gpt-image-2.5-flare","n":1,"output_format":"png","prompt":"Describe the output you want","quality":"auto","size":"auto"}),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
const result = await response.json();

Frequently asked questions

What API endpoint does gpt-image-2.5-flare use?

The endpoint is resolved from the current model capabilities and request schema and shown on this page.

How is gpt-image-2.5-flare priced?

The pricing section reads the current structured billing configuration, so editorial text does not need a rewrite after a price change.

Can I use Python?

Yes. The example uses the current endpoint and exact model identifier.

Are JavaScript and cURL examples included?

Yes. All examples share the same live request structure.

Which resolution, duration, or reference-image controls are supported?

Supported controls come from the current request schema. Do not assume fields that are not listed.

What should I validate before production use?

Validate parameters, timeouts, error handling, and asynchronous generation status behavior after model updates.