No. If OpenAI’s textGrain detector finds no watermark, that does not prove a person wrote the text. A signal can be missed because a passage is short, constrained, edited, translated, produced by an unsupported model, or generated before watermarking was enabled. A positive result is limited too: it suggests OpenAI model involvement, not who wrote the passage or whether it is accurate.
What OpenAI textGrain does
OpenAI describes textGrain as a text watermarking technology. During generation, it subtly influences the model’s choices among possible next words or word pieces, embedding a statistical pattern in the wording. It does not add a visible mark, hidden characters, invisible spaces, unusual punctuation, or special watermark-only tokens. A reader cannot inspect the text and see the watermark.
At each generation step, the system uses a secret key to favor certain token choices. The probability distributions are balanced so their average matches the model’s original likelihoods. A detector with the matching key and settings then checks whether the passage follows the expected pattern more often than chance. OpenAI explains this mechanism in its Help Center guidance.
Why no detected watermark is inconclusive
A missing signal has several possible explanations; human authorship is only one of them. OpenAI’s text provenance tools are designed to detect supported OpenAI signals, not to determine whether any text anywhere was written by AI.
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- There may be too little text. Short passages contain fewer word choices for a detector to evaluate. OpenAI’s Help Center says the EU Code of Practice does not require watermarking outputs under 200 tokens—about 150 English words—or code snippets. That threshold is not a guarantee that every longer passage will be detectable.
- The text may leave little room for varied wording. Math and other constrained responses can be difficult to detect because precise answers offer fewer alternatives in how to phrase them.
- The text may have changed. Editing, synonym replacement, or translation can weaken or remove the statistical pattern.
- The model or timing may not be covered. The content may have come from an unsupported model or product, another provider, or a period before watermarking applied to that output.
These limitations mean a detector’s silence cannot distinguish a human-written passage from AI-generated text whose signal is absent, unsupported, or no longer detectable.
How well does detection work?
OpenAI’s published figures illustrate why a detection result needs context. In its 2026 evaluation, OpenAI reported detecting about 80% of 200-token passages and about 95% of 400-token passages on content such as psychology at a target false-positive rate of 1%. These are conditional results for the stated passage lengths and subject matter, not a guarantee for a particular text.
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Editing reduced detection in OpenAI’s 2026 tests of 400-token passages: replacing 10% of words with synonyms lowered detection from about 92% to 66%, and replacing 25% lowered it to 17%. OpenAI also reported substantially lower detection for math than psychology because math answers offer fewer wording choices.
Language matters as well. In a specific multilingual experiment using 500 synthetic English prompts translated into 24 official EU languages, the OpenAI Help Center reported detection of 69.0% for Spanish and 42.2% for Romanian at a 1% false-positive rate. OpenAI says it adjusted watermark strength for languages below 60%. Those figures describe that experiment, not the expected performance for every text in those languages.
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False positives are possible: a detector can report a watermark when none is present. False negatives are possible too: it can miss a watermark that is present. OpenAI reports no meaningful performance differences from watermarking on its Astra benchmark evaluations; that is the company’s own benchmark report, not independent validation.
What a positive result can—and cannot—tell you
A detected watermark supports a narrow inference: an OpenAI system likely generated or processed some of the text. It does not establish the extent of that involvement. A person may have written, edited, or substantially contributed to the passage, and the result does not identify the user or organization that prompted the system.
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Nor does a watermark establish who owns the text, who is responsible for it, whether disclosure is legally required, or whether its claims are true and presented in context. A provenance signal is evidence about possible system involvement, not a complete account of authorship or reliability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How textGrain differs from AI-text classifiers
TextGrain checks for a secret statistical pattern embedded in generation choices. Classifier-based AI detectors instead analyze text after it has been written and infer likely AI authorship from patterns such as word choice. They look for different evidence and have different failure modes: editing or translation can weaken a watermark, while a classifier can misclassify text based on its inferred patterns.
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Neither kind of tool proves that a person or a model wrote a passage. Results from different products should not be ranked as though they were measured under identical conditions unless they have been tested against the same texts, languages, passage lengths, and error criteria.
Who can use the text detector, and where watermarking is rolling out
In its October 5, 2026 announcement, OpenAI said API customers worldwide could opt in to text watermarking for select models, and that it remained off by default in the API. OpenAI also said it would add watermarking to eligible ChatGPT and Codex text output in the EU over the coming weeks. This is an announced rollout, not confirmation that every eligible account or output is already covered. Model availability is selected in account settings and can change.
OpenAI said access to its text detector was initially limited to approved researchers and expert organizations through a case-by-case application. The detector reports whether it detects an OpenAI watermark; it does not reveal the user, prompt, or conversation. This access restriction concerns text provenance. OpenAI’s image and audio verification tools have separate availability.
What to say when a detector finds no watermark
Describe the result precisely: “The detector did not find a supported OpenAI watermark in this passage.” Do not turn that into “a person wrote it.” If authorship matters, a missing signal alone is not enough to settle the question; it may need to be considered alongside other relevant evidence, such as drafts or a documented writing process.
Likewise, if a detector finds a watermark, report that as evidence of likely OpenAI system involvement, not proof that the model wrote the entire passage or that a person made no meaningful contribution.
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