OpenAI points Images 2.5 at the edits that used to degrade the rest of the frame

TechRadar's reviewer describes the loop that came before this release: re-prompt an image that came back wrong, watch it come back wrong in a new way, then abandon it and start from the first prompt ^[16]. The two claims that speak to that loop are narrower than the release notes. OpenAI says identifiable details, including distinctive facial features, now carry across multiple generations and edits inside a single conversation ^[5], and that earlier edits stay stable as later ones are applied, each one building on the last ^[6].

Most of the rest is the pitch. More natural lighting and richer textures ^[2]. Templates for flyers and product photos, inline comments, and prompt sharing so a colleague can pick up your idea ^[8]. TechRadar, after 24 hours with it, says calling the feature surface overcrowded would be an understatement ^[17], and the genuinely useful thing it found sits outside the model: comments in the Edit toolbar work as instructions pinned to a point in the picture, carried out when you hit send ^[14]. That takes the job of saying which element you mean off the prompt and puts it on the canvas.

The speed numbers need a squint. OpenAI told 9to5Mac that Flare delivers higher-quality images than GPT-Image-2 at 50% lower latency ^[10], while SiliconANGLE reports the company describing Flare as between two and four times as fast as GPT-Image-2 ^[9]. Halving latency doubles the images you get per minute, 1/(1-0.5) = 2, which is the floor of that range and the only part of it the latency figure supports ^[20]. A capacity plan built on 4x will come up short.

What none of the reporting carries is a rate ^[22]. There is no published figure for how often a subject stops looking like itself by the fifth edit, and no benchmark for changes landing outside the region named in the prompt. That leaves the acceptance test to whoever owns the workflow, and it is a cheap one: ten reference photos of the subject you actually use, a five-edit chain on each, then two counts, subjects still recognisable at the end and changes that landed where nobody asked for them. The sort that matters is whether the subject has to stay identical across assets and whether an asset gets edited more than once. Only the corner where both are true needed this release. Single-pass social images were fine before it.

Who it is for is in OpenAI's own positioning: Sunburst trades speed for precision and is built for premium visual workflows with tighter control across edits, such as campaign creative and polished product imagery ^[11]. That describes the team producing the same mascot at forty sizes across a fortnight of revisions, which is also the team best placed to find out whether the edit-stability claim survives contact with a real brand guideline.