GPT Image 2.5 is an OpenAI image generation model family that ships in two variants, Flare and Sunburst. Flare is the faster default, tuned for natural lighting, rich texture, and complex typography. Sunburst is the precision variant, built for higher detail fidelity at the cost of slower generation. Both support text-to-image and image editing, and you can run either one from the generator on this page.

Flare optimizes for speed and is the right default for iteration; Sunburst optimizes for detail fidelity and holds the tightest control on edits, keeping changes strictly within the instruction. They accept the same inputs, expose the same five quality tiers, and cost the same, so the choice is about turnaround versus fidelity rather than budget.

Yes. The generator at the top of this page runs GPT Image 2.5 free, and new accounts start with free credits, so you can generate without paying up front. You do not need an OpenAI API key or a separate subscription to use it here.

Yes. GPT Image 2.5 treats background as a native setting with three options: auto, transparent, and opaque. Choosing transparent and exporting as PNG or WebP gives you a real alpha channel, which means logos, icons, and product cutouts do not need a separate background-removal step.

The GPT Image 2.5 edit endpoints accept up to 16 reference images in a single request, and the generator on this page is wired to that same limit. That is what makes multi-reference character work and multi-round revision practical: the referenced subject stays recognizable across style changes and across several rounds.

Complex typography is one of the capabilities GPT Image 2.5 Flare is specifically built for, which makes it more usable than most image models for posters, packaging, labels, and slide graphics. If a headline comes out wrong, sending the image back through image-to-image to fix just the text is usually faster than regenerating the whole composition.

GPT Image 2.5 exposes five tiers: low, medium, high, xhigh, and max. Low and medium are for fast iteration, high is the general-purpose tier, and xhigh and max are for final delivery where detail matters. Higher tiers cost more credits per image, so a common workflow is to explore cheaply and re-run only the chosen composition at a higher tier.

No. This page runs GPT Image 2.5 for you through a hosted generator, so there is no API key to provision, no billing account to attach, and no code to write. If you already build against an image API, the same model family is available from providers directly, but nothing here requires it.

GPT Image 2.5 is the same model family, but this is a different surface. Running GPT Image 2.5 here gives you explicit controls that a chat interface does not expose — variant choice, five quality tiers, background mode, aspect ratio, and output format — and it keeps your generations in one place alongside the other models on this site.

GPT Image 2.5 became available through hosted providers in September 2026, with the Flare and Sunburst variants published together on 2026-09-08. It is live in the generator on this page now.

On this page you pick the variant yourself and the generator shows which one is selected before you hit generate. That is the main practical difference from generating inside a chat assistant, where the variant is chosen for you and there is no indicator telling you which one ran. If you want the faster pass, select GPT Image 2.5 Flare; if you want the higher-fidelity pass, select Sunburst.

It is meaningfully better than earlier GPT Image generations at complex typography, which is why short headlines, labels, and packaging copy usually come out readable now. It is not solved: long paragraphs, dense small type, and unusual scripts still garble often enough that you should plan to check the output. The practical workflow is to generate, read the text, and send it back through image-to-image to fix just the wording rather than regenerating the whole image.

Some outputs still show fine repeating texture, over-sharpening, or a warm color cast, particularly across large flat surfaces. That behavior comes from the model itself, so no interface on top of it can promise clean output — and this page does not. Two things genuinely help: generate at a higher quality tier, and write prompts that ask for large uncluttered areas instead of dense detail everywhere.

The GPT Image 2.5 edit endpoints are built to change only what you ask for and to hold subject and composition across revisions, and in practice they hold up notably better than earlier generations over several rounds. Long chains are still the honest weak spot: the more rounds you stack, the more likely small details drift. If a long chain matters, keep the original as a reference image rather than editing the edit of an edit.

No. GPT Image 2.5 runs OpenAI's content moderation, and requests for real public figures, trademarked characters, and other restricted content are refused. That applies here exactly as it does anywhere else the model runs — this page does not loosen it, and no prompt trick on this site will get around it.