GPT Image 2 vs. GPT Image 1.5: what the evidence says about portraits and product photos

First, the naming is messy

In public discussion, people often use GPT Image 2 and ChatGPT Images 2.0 as if they are the same thing. The official OpenAI page cited here, however, presents ChatGPT Images 2.0. By contrast, GPT Image 1.5 has an OpenAI API model page that describes it as an image generation model with improved instruction following and prompt adherence.

A third-party platform, Fal.ai, uses the name GPT Image 2 and promotes it for photorealism, text rendering, and brand-consistent product photography. So in practice, this article uses GPT Image 2 / ChatGPT Images 2.0 to match how people are searching for the comparison—but separates official OpenAI material from third-party marketing pages, media hands-on tests, and user posts.

That distinction matters. A polished demo image is not the same thing as a controlled benchmark.

What is firmly documented

GPT Image 1.5 is a clear baseline

GPT Image 1.5 is well documented in OpenAI’s developer materials. OpenAI provides an API model page, image generation guidance, a GPT Image cookbook, and a GPT Image 1.5 prompting guide covering generation, image editing, and mask-based workflows.

That makes GPT Image 1.5 a usable baseline for comparison: it has documented settings, workflows, and prompting guidance. But those documents do not themselves provide a GPT Image 2 vs. GPT Image 1.5 quality benchmark for portraits or product photography.

ChatGPT Images 2.0 is most clearly framed around text and layout

OpenAI’s ChatGPT Images 2.0 page highlights examples involving multilingual text, comic-style pages, and more complex visual outputs. TechCrunch’s coverage focuses on the model’s ability to generate text inside images, while ZDNET’s early look says OpenAI is emphasizing precision, usability, and complex visual tasks, including pages that combine text and imagery.

So the safer conclusion is this: ChatGPT Images 2.0 has meaningful public support for improved in-image text, multilingual rendering, and complex page-style compositions. That does not automatically mean it is better for realistic faces, product materials, packaging accuracy, or overall image quality.

Why the portrait evidence is still weak

For photorealistic portraits, the useful question is not simply: which sample looks nicer? A serious comparison needs to evaluate identity consistency, facial structure, skin texture, eyes, teeth, hands, lighting, over-retouching, and whether the model preserves the person’s likeness when editing from a reference image.

The public evidence available here does not yet meet that bar. Some Reddit users have posted side-by-side comparisons claiming GPT Image 2 produces better outputs or more legible text. Those posts may be useful for spotting early patterns, but they are not large, independent, repeatable blind tests. They typically do not provide a full set of fixed prompts, identical inputs, comparable settings, enough samples, or all failed outputs—not just the best-looking ones.

That means the portrait claim remains unconfirmed. GPT Image 2 / Images 2.0 may improve some portrait workflows, but the public evidence cited here is not strong enough to say it reliably and significantly beats GPT Image 1.5.

Product photography: promising claims, but not a settled case

The product-photo case is a little more complicated because there are stronger claims from third-party sources. Fal.ai promotes GPT Image 2 as offering photorealism, pixel-perfect text rendering, and brand-consistent product photography. Digit’s hands-on comparison includes a product photography test and, in its example, judges 2.0 to be better.

Those are signals worth paying attention to. They are not the same as a broad, independent benchmark.

For product photography, the real production questions are specific: does the model preserve the product outline, proportions, packaging text, logo placement, material finish, reflections, shadows, perspective, and brand consistency? A single hands-on test or vendor product page cannot prove that performance will hold across cosmetics, food packaging, electronics, fashion accessories, furniture, and other product categories.

So the practical reading is: there are reasons to test GPT Image 2 / Images 2.0 for product work, but not enough public evidence to declare a confirmed, general-purpose upgrade over GPT Image 1.5.

Leaderboards help, but they do not answer this exact question

The Artificial Analysis Text to Image Arena source cited here lists GPT Image 1.5 (high) in first place with an Elo score of 1274, based on blind user votes in its Image Arena and an Elo rating system derived from those comparisons.

That is useful as a broad preference signal. But it is not a dedicated GPT Image 2 vs. GPT Image 1.5 benchmark for portraits and product photography. A general text-to-image leaderboard can tell you where models appear to sit in the market overall; it cannot, by itself, prove whether one model is consistently better for face realism, packaging text, product materials, or ecommerce hero images.

Evidence check

How to run a fair comparison

If the decision affects real production work, do not rely on social screenshots or vendor demos. Use GPT Image 1.5 as the baseline because it has official documentation and prompting guidance, then test GPT Image 2 / ChatGPT Images 2.0 against it with the same materials.

A fair A/B test should control at least these variables:

• Use the same prompts for both models.
• Use the same reference images, product photos, or portrait inputs.
• Keep aspect ratio, quality settings, and output format as comparable as possible.
• Generate multiple outputs per prompt instead of cherry-picking one winner.
• Randomize the outputs so reviewers do not know which model made which image.
• Score separate dimensions instead of asking only which image looks better.

For portraits, score identity preservation, face structure, skin texture, eyes, teeth, hands, lighting, and signs of over-smoothing or over-retouching.

For product photos, score product shape, proportions, packaging text, logo accuracy, material realism, reflections, shadow behavior, perspective, and brand consistency.

Those criteria are more useful than a broad beauty contest because they match what actually determines whether an image can be used in a campaign, catalog, product page, or client presentation.

What creators and product teams should do now

If your main use case is posters, infographics, social graphics, UI mockups, menus, presentation pages, or ads with a lot of embedded copy, ChatGPT Images 2.0 deserves priority testing. The public evidence is strongest around readable text, multilingual rendering, and more complex layouts.

If your main use case is realistic portraits, model imagery, ecommerce hero shots, or branded product photography, be more cautious. Do not switch a whole workflow just because someone says GPT Image 2 looks better. Instead, test it with your own products, reference images, brand assets, and production prompts. Track usable output rate, revision time, brand consistency, and failure modes.

Bottom line

The most defensible conclusion is: public evidence supports improvement signals for ChatGPT Images 2.0 in in-image text, multilingual rendering, and complex layout tasks. It does not yet provide enough reliable evidence to prove that GPT Image 2 / ChatGPT Images 2.0 is clearly, consistently, and verifiably better than GPT Image 1.5 for realistic portraits, product photography, or overall image quality.

In other words, the answer is not that there is no improvement. The answer is that the evidence is still insufficient. Some workflows may benefit, but teams should validate that with controlled tests using their own assets before changing production pipelines.