OpenAI announced Images 2.6 on November 4 for ChatGPT, Codex and the API. The company claims faster generation; API options include Flare and Sunburst, edit markup and a complexity setting. The useful question is how an image survives an entire editing workflow.
Evaluate a sequence of edits
One attractive result is not enough for a cover or product card. A practical check could involve creating a composition, changing its background and restoring the original viewpoint. Compare the object's shape, lettering and small details after every step. This is an editorial recommendation, not a report of our own model testing.
Specify separately what may change and what must stay fixed. For example, lighting can become warmer while product color and proportions remain unchanged. That gives you a clear basis for rejecting a visually appealing but inaccurate version.
Motion and vector output need different checks
The release documentation confirms GIFs, image markup and SVG output in the API. Review an entire GIF loop: a rough transition or a detail disappearing halfway through can be invisible in the first frame. For an SVG, check scaling, element edges and rendering in the application that will use it.
Do not automatically extend a still image's quality assessment to animation. Faces, logos and lettering need to remain consistent over time. Public-facing work also needs permission to use the input images and a check that the result does not misrepresent a real person or event.
What to try first
Start with a small, familiar task and preserve the original file. Compare versions using the same criteria: accuracy, legibility, consistency across edits and time to a usable result.
Time to the final version is more useful for a workflow than the speed of a single successful generation. This article explains the announcement and suggests an evaluation process; it is not an independent benchmark.
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