GPT Image 2.5 Text Rendering: 4 Tests That Catch Costly Copy Errors

GPT Image 2.5 can produce useful short, high-contrast text for concepts, posters, labels, and controlled edits, but it should not be treated as a replacement for formal typography, copy proofing, or legal approval. The recommended workflow separates legibility, character-level accuracy, editability, and regulatory suitability, with generated images requiring review at high magnification against a saved source-copy record. High-risk content such as prices, dates, legal claims, dense layouts, approved brand fonts, multilingual copy, and accessibility-critical information should be added as editable typography overlays rather than generated directly in an image. The material proposes four controlled tests for headlines, schedules, product labels, and constrained text edits, although their actual results were unavailable because of an expired test login, and advises rejecting any output with altered characters, extra wording, or changed protected regions. It also recommends structured prompts that quote exact text, specify placement and exclusions, compare Flare and Sunburst routes using consistent tests, and measure total cost per accepted image by including retries and review time.

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