GPT Image 2.5 poster workflow: multilingual text and layout checks

A poster can look finished while containing a wrong date, omitted accent or invented slogan. This workflow uses gpt-image-2.5-flare or gpt-image-2.5-sunburst for visual concepts, with a separate copy manifest and a deliberate decision about where to render final text. It is not a text-rendering accuracy benchmark.

Choose between generated lettering and later typesetting

Approach	Useful starting point	Main acceptance condition
Generate image and lettering together	Exploring a short headline integrated with the artwork	Every visible character is checked
Generate artwork, typeset later	Exact dates, URLs, accessibility or many locale variants	Final copy comes from the approved manifest

For a campaign with many translations, a shared background with separately typeset text can avoid regenerating the illustration for every copy change. This is a workflow choice, not a measured claim that it is always cheaper. Include editing time as well as generation cost.

Keep a copy manifest outside the API input: locale, exact headline, subtitle, date, destination URL and approval version. Preserve punctuation, accents, line-break intent and the distinction between a draft translation and approved copy. Do not allow the image prompt to become the only record of the final wording.

Generate a small poster concept

This illustrative request is a design exercise using a fictional event. Verify current model access and the full schema before sending it to POST /v1/tasks; generation incurs usage.

{
  "model": "gpt-image-2.5-flare",
  "input": {
    "prompt": "A portrait exhibition poster concept with a single abstract blue paper sculpture on an ivory background. Large readable headline exactly: FORM / 形. Smaller subtitle exactly: A study in paper. Keep generous margins and clear separation between text and sculpture. No other words, dates, logos or QR codes.",
    "resolution": "1k",
    "aspect_ratio": "2:3",
    "quality": "medium",
    "output_format": "png"
  }
}

After checking the contract, Sunburst can be a separate candidate using gpt-image-2.5-sunburst. Keep the same copy, prompt, crop and settings for a variant comparison. Save each task ID and poll the original task as described in the Flare guide. A failed spelling check is a creative rejection, not a reason to retry the transport automatically.

Review text independently of visual appeal

Read every visible line against the approved copy. Check missing or duplicated characters, punctuation, capitalization, accents, unexpected translations and extra invented text. An OCR pass can flag possible differences, but decorative type and mixed scripts can confuse OCR; human review remains necessary.

Then inspect the target crop and actual delivery size. Text readable when enlarged may be illegible in a feed card. Check line breaks, contrast and separation from artwork. A PNG extension and a larger quality setting do not guarantee legibility.

For a clickable destination or QR code, place the verified link or independently generated code during final layout, then test it. Do not assume a code drawn into an image resolves to the intended destination. Keep essential information available as real page text or an appropriate text alternative when publishing on the web.

Compare complete production workflows

Score exact-copy correctness separately from composition. For each accepted poster, record generation attempts, manual corrections, layout time and final charge. When one word changes, compare re-typesetting the approved artwork with regenerating the entire image; use your own observed production effort rather than a token-price shortcut.

See image model comparison for platform controls and batch budgeting for rejected attempts. Identity and native concepts checked 2026-11-03 against Flare, Sunburst and OpenAI’s image guide; platform fields follow the repository schema. No generated output is presented as evidence here.