Can GPT Image 2 handle complex layouts? The evidence is still too thin

The strongest official claims that come close to complex-layout needs are about 4o image generation, not GPT Image 2. OpenAI’s 4o image generation system card addendum says 4o image generation can follow detailed instructions and reliably incorporate text into images. OpenAI’s launch post also says GPT-4o image generation excels at accurately rendering text, precisely following prompts and using 4o’s knowledge and chat context.

Those claims matter, but they do not prove that a model called GPT Image 2 has the same, or better, layout reliability. The sources that directly discuss GPT Image 2 are third-party summaries rather than OpenAI product announcements. getimg.ai says OpenAI has not announced GPT Image 2, while Kubeez describes Image V2 or GPT Image 2 reports as unconfirmed tests and reporting.

What is actually confirmed?

Why understanding a prompt is not the same as layout reliability

Complex layout is not a single skill. An infographic may require a title, sections, legends, numeric labels and proportional relationships to be correct at the same time. A multi-panel comic needs the right number of panels, a readable sequence, consistent characters and dialogue placed in the right boxes. A poster may need a headline, subhead, date, venue, call to action, brand area, spacing and alignment to all work together.

OpenAI’s official 4o image generation materials do discuss text rendering and instruction following, which are relevant to those tasks. But to say that GPT Image 2 can reliably do complex layout, stronger evidence would be needed: an official product confirmation, layout-specific examples, repeatable evaluations, success rates, known failure modes and documented limitations. The available sources do not provide that level of evidence for GPT Image 2.

So the most accurate wording is not that GPT Image 2 cannot do these things. It is that the available evidence does not prove GPT Image 2 can do them reliably.

How teams should test complex-layout claims

If a team wants to bring AI image generation into content production, product marketing or design workflows, it should separate “one good-looking output” from “repeatable delivery.” A useful test set should include at least three categories:

• Infographic tests: specify fixed blocks, a title, legends, columns, numeric labels and data order. Check for missing text, misplaced labels, mixed-up columns, incorrect number placement and unreadable typography.
• Multi-panel comic tests: specify the exact panel count, reading direction, scene in each panel, character appearance and dialogue bubble placement. Check whether panel count, sequence and character consistency hold up across runs.
• Poster-layout tests: specify headline, subhead, date, venue, call to action, brand area and image ratio. Check visual hierarchy, alignment, whitespace and text clarity.

The same prompt should be generated multiple times, with each output scored for grid violations, ordering errors, text legibility, element placement and image-to-text balance. OpenAI’s 4o image generation materials can help shape these tests because they specifically emphasize text rendering and prompt following. But until GPT Image 2 has official verification, teams should treat their own testing and review as essential.

A more careful way to say it

A rigorous formulation would be: OpenAI’s official materials show that its image generation API supports generating images from prompts and editing existing images, and its 4o image generation materials emphasize text rendering and detailed instruction following. However, the available sources are not enough to prove that GPT Image 2 has been officially released by OpenAI, or that it can reliably complete infographic, multi-panel comic and poster layout tasks.

A less careful claim would be: OpenAI has proved that GPT Image 2 understands complex layout. That statement blends an unconfirmed model name, officially documented image-generation abilities for other contexts and an unsupported claim about layout reliability. Based on the current evidence, it goes too far.