GPT Image 2 vs GPT Image 1.5: which renders readable text better?

What the evidence supports

Why GPT Image 2 is the better practical bet

The strongest case for GPT Image 2 is use-case fit. The materials around gpt-image-2 are not just about generating pretty images; they emphasize production assets that need to be readable, localized, on-brand, and formatted for real surfaces ^[32]. That is exactly where in-image text usually fails: slide text, app labels, product packaging, infographic annotations, multilingual copy, and dense layouts.

OpenAI’s public ChatGPT Images 2.0 launch page also foregrounds examples involving typography, editorial text, desktop UI, and other text-heavy visual scenes generated with ChatGPT Images 2.0 ^[31]. TechCrunch’s report adds the more explicit press-release language: Images 2.0 can handle small text, iconography, UI elements, dense compositions, and subtle stylistic constraints at up to 2K resolution ^[77].

Taken together, those sources make GPT Image 2 the more sensible default when readable text is part of the deliverable, especially for assets that would otherwise require manual design cleanup.

Why GPT Image 1.5 is still relevant

GPT Image 1.5 should not be treated as a pre-text-rendering model. Its rollout announcement says it brought more precise image editing, better prompt adherence, and improved text rendering, especially for denser and smaller text ^[69]. For simple use cases—large headlines, short labels, basic mockups, and workflows with human proofreading—it may still be good enough.

OpenAI’s API image-generation guide also keeps text rendering in the limitations column for the GPT Image models it names, including gpt-image-1.5: despite improvements over DALL·E, those models can still struggle with precise text placement and clarity ^[47]. That warning is a useful reminder not to treat any model as typo-proof.

Be careful with 99% typography claims

Several third-party or social sources make strong claims around 99% typography or glyph accuracy for GPT Image 2 ^[4][7][78]. Those claims may point in the right direction, but the reviewed record does not show enough methodology to treat them as settled benchmark facts.

For a 99% claim to be meaningful, the benchmark needs to disclose the prompt set, languages and scripts, number of generations, output sizes, model settings, scoring rules, whether failed generations were counted, and whether readability was judged at the final publication size. Without those details, a model could look excellent on a large poster headline while still failing on long paragraphs, fine print, chart labels, UI controls, or complex multilingual layouts.

Naming note: GPT Image 2 vs ChatGPT Images 2.0

The source set uses two related labels. Developer-facing materials use gpt-image-2: OpenAI’s prompting guide includes that model ID, and the Developer Community announcement says gpt-image-2 is available in the API and Codex ^[23][32]. OpenAI’s public launch page and TechCrunch coverage use ChatGPT Images 2.0 ^[31][77].

Because the provided sources do not include a single canonical sentence mapping every gpt-image-2 claim to every ChatGPT Images 2.0 claim, the safest wording is GPT Image 2 / ChatGPT Images 2.0 when discussing the overlapping evidence.

Which model should you use?

Choose GPT Image 2 first if your deliverable contains multiple text zones, small labels, infographic copy, product packaging text, UI elements, presentation text, localized ads, or multilingual content. That recommendation follows from how gpt-image-2 is positioned for readable production workflows and from the reported OpenAI claim that Images 2.0 handles small text, UI elements, and dense compositions ^[32][77].

Keep GPT Image 1.5 in consideration when your text is short, large, easy to proofread, or already acceptable in your existing workflow. Its own rollout specifically called out improved dense and small text rendering ^[69].

A fair way to test readable text yourself

If text correctness is business-critical, run a same-prompt bake-off before changing production workflows:

1. Exact-copy test: use the same headline, subhead, and short labels for both models.
2. Small-text test: include footnotes, chart labels, UI controls, or fine print at the final size you plan to publish.
3. Dense-layout test: generate an infographic, menu, dashboard, or slide with several separate text zones.
4. Edit-preservation test: make one text edit and check whether the rest of the image remains stable.
5. Multilingual test: include the languages and scripts your team actually ships.
6. Human scoring: evaluate spelling, omissions, substitutions, legibility, alignment, and whether the image can ship without cleanup.

The winner is not the model with the best showcase image. It is the model that produces correct, readable text most consistently on your prompts, at your target sizes, with your review process.

Bottom line

GPT Image 2 appears to be better for readable text in practical use, especially for dense, small, localized, and UI-like image text. The defensible claim is narrower than the hype: OpenAI-linked materials position GPT Image 2 / ChatGPT Images 2.0 around readable production output and fine-grained text handling, while GPT Image 1.5 also improved dense and small text rendering, and the reviewed sources do not provide a transparent public head-to-head readability benchmark ^[32][77][69].