OpenAI announced on October 5, 2026 that it will add an invisible statistical watermark, called textGrain, to eligible ChatGPT and Codex text output in the European Union. The rollout is described as happening “over the coming weeks,” so as of October 7 it is planned, not confirmed as live for every eligible user. It is not a global ChatGPT default at launch.
Who gets the watermark, and when
According to OpenAI’s announcement, the consumer-facing rollout covers eligible ChatGPT and Codex text output in the EU, across plans. Outside the EU, the only announced route is for API customers, who can opt in to text watermarking on select models. OpenAI has not given a date for when each EU account or model will be switched on.
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OpenAI cites the EU AI Act as the reason for the EU-first approach, saying providers of generative AI must make generated text identifiable in a machine-readable way. Its Help Center adds that the EU AI Act Code of Practice does not require watermarks in outputs shorter than 200 tokens (about 150 English words) or in code snippets.
How textGrain works
textGrain does not hide anything in the text you copy. OpenAI’s Help Center says: “Text watermarking changes the statistical pattern of word choices; it does not insert hidden characters or add watermark-only tokens.” There are no invisible spaces or odd punctuation to strip out.
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Instead, when the model has several plausible next words or word pieces, textGrain nudges which one it picks. A single choice means nothing. Across a long enough passage, the nudges form a pattern that a matching detector can test for.
This explains why the method has built-in limits. Where there is little freedom of choice, such as mathematics or code, there is little room for a pattern. OpenAI notes that code is hard to watermark because there are fewer plausible next choices.
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How well does detection work?
All figures below are OpenAI’s own evaluations from its October 5, 2026 announcement. They are not independent tests and do not guarantee everyday performance.
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|---|---|---|
| 200-token watermarked passage | About 80% | Psychology text; target false-positive rate of 1% |
| 400-token watermarked passage | About 95% | Psychology text; target false-positive rate of 1% |
| 400 tokens, unedited | About 92% | Editing evaluation |
| 400 tokens, 10% of words replaced with synonyms | About 66% | Editing evaluation |
| 400 tokens, 25% of words replaced with synonyms | About 17% | Editing evaluation |
| Mathematics | Substantially lower | Less flexibility in word choice; no figure given in the material available |
The takeaway is that length, subject and editing all matter. OpenAI also says the detector can make both false-positive and false-negative errors, and it describes text watermarking and detection as early technologies with significant limitations.
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Can you check text for the watermark?
Not as an ordinary user. OpenAI’s public verification tool currently supports images and audio, not text. Access to the text detector is limited to approved researchers and expert organizations, and OpenAI reviews applications case by case.
Generic AI-text detectors you find online do not read textGrain. They guess from writing style, which is a different and much less certain exercise, so their results should not be presented as a watermark check.
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Asking ChatGPT itself does not work either. OpenAI’s Help Center states: “ChatGPT has no ‘knowledge’ of what content could be AI-generated or what it generated.” The model may simply invent an answer.
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What a result does and doesn’t mean
A detected watermark indicates that an OpenAI system generated or processed some part of the passage. That is all. In OpenAI’s words, “A watermark does not measure human contribution” and “A watermark does not verify accuracy.” It also does not identify a user or account, or settle ownership or legal responsibility.
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A missing signal is just as limited. It does not prove a human wrote the text. OpenAI says a signal can be missed because the text is short or constrained, has been edited, predates provenance signals, or came from an unsupported model or path.
- If you use ChatGPT to polish your own writing: a detector could in principle flag it, but the result would not say how much of it is yours.
- If you’re a teacher, editor or employer: you have no public tool for this, and a negative result would carry no weight as evidence of human authorship.
- If you copy text into a document: nothing extra travels with it. The pattern lives in the wording itself, so heavy rewriting weakens it.
How it differs from image and audio provenance
OpenAI already uses other provenance signals for supported images and audio. They are separate from textGrain.
| textGrain (text) | C2PA Content Credentials | SynthID (supported images and audio) | |
|---|---|---|---|
| What is checked | Statistical pattern in word choices | Metadata attached to a file | Embedded watermark |
| Public checking | Not available; approved organizations only | Public verifier supports images and audio | Public verifier supports images and audio |
| Main weakness | Weakens with editing; depends on length and subject | Metadata can be stripped | Not stated in the material available |
None of these signals speaks to truth, ownership, full authorship or human contribution.
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