Blooginga
For most of 2024 and 2025, AI image generators had one embarrassing party trick that everyone politely ignored: they could not render text. A poster saying "GRAND OPENING" came out as "GRANE OPEMING" or worse. A book cover mangled the title in five different ways. The workarounds were painful — generate the image, mask the broken text, hand-paint a fix in Photoshop, hope nobody zoomed in.
The latest generation of image models, including GPT Image 2, has finally made readable in-image text a baseline feature. I have been testing it against the previous benchmarks for two weeks. Here is what actually changed, what still breaks, and how I now use it in client work.
What "good text rendering" actually means
It is not just "the letters are correct." The hard parts are:
Multi-line layouts that respect kerning and alignment.
Mixed-language scenes (English headline + Chinese subtitle, for example).
Text that sits on a textured background without dissolving into it.
Small text (under 24px in the final render) that stays sharp.
GPT Image 2 is the first consumer-grade model I have tested that handles all four without a manual fix-up. It is the difference between an AI image you post on social and an AI image you can drop into a print deck.
Prompt patterns that work
The 51-prompt gallery at gpt-image.io/prompts is a useful starting point, but here is the pattern I keep coming back to:
Editorial poster, 24x36 inches, top headline in bold condensed sans: "SUMMER 2026", sub-line in serif: "A new collection by Studio Mira", warm grain, slight paper texture, soft vignette, 300dpi.
The model treats the headline as a layout object, not as a description of "some text somewhere." That is the trick. If you describe the typography (role, weight, position) instead of just the words, you get a usable result on the first try.
Photorealism vs illustration: pick a lane
GPT Image 2 also scores very high on photorealism — in blind user tests it beats most open models on skin texture, fabric weave, and color fidelity. But it still defaults to a slightly "stock photo" look on portrait prompts. If you want a hand-drawn editorial style, you have to push it:
Pen-and-ink editorial illustration of [subject], crosshatching, off-white paper, subtle ink bleed, no color, magazine cover layout.
Without that nudge, it will give you a hyperreal photo instead of the illustration you wanted. Be explicit about the medium.
Where it falls short (June 2026)
A few honest caveats after two weeks of daily use:
Hands are better than last year, but the left hand of a person holding a phone still fails about 1 in 5 times.
Crowds (more than 6 people) start to mush together.
Logos of real brands are not consistent — do not use it to mock up a real Nike or Apple campaign.
Long copy inside an image (more than about 12 words) still benefits from a manual pass.
If you know these limits, you can route around them and the model is a real production tool. If you do not, you will spend your afternoon fixing "GRANE OPEMING" again.
Workflow I now recommend
Draft the layout in words (headline, sub, call-to-action).
Use the prompt library at gpt-image.io/prompts to find a base style.
Generate three variations, pick the best, upscale.
Run a single manual pass in Figma for type kerning and color matching to your brand.
Step 4 is now 10 minutes instead of an hour. That is the real win.
Try it
You can test GPT Image 2 with free starter credits at GPT Image 2. The prompt library is free to read and copy, with no signup required — useful as a reference even if you end up using a different model.
This article is not sponsored; the author is an independent user of the tool. [Visit GPT Image 2](GPT Image 2)
Learn more: GPT Image 2