AI Detector Accuracy by Language: Why Detectors Fail Outside English (2026)
Every mainstream AI detector was trained and tuned on English first, and the numbers prove it. Accuracy drops 10 to 20 points the moment the text is in another language, and false-positive rates climb sharply on non-native writing. Copyleaks, the widest-coverage tool, scores about 91% on English and averages roughly 74% across non-English content; GPTZero holds about 82% on Spanish and French but slips to near 74% on Arabic and Mandarin. For multilingual classrooms and newsrooms, a non-English score is a reason to look closer, not a verdict.
English-first training is the root cause
Detectors measure how statistically predictable text is, and they have simply seen far less non-English writing to calibrate against.
Copyleaks, which leads on language coverage, reports about 91% accuracy on English with a roughly 7% false-positive rate, then averages near 74% across the 30-plus languages it supports. The gap is not a rounding error; it is the difference between a tool you can lean on and a coin flip in another script.
The accuracy ladder by language family
Independent 2026 tiers line up with training-data volume. Western European languages sit at 85 to 95% accuracy; Portuguese, Polish and Swedish at 70 to 85%; morphologically complex languages such as Arabic, Turkish and Finnish at 60 to 80%; and CJK, Chinese, Japanese and Korean, at 55 to 75%.
Low-resource languages often fall below 40%. GPTZero holds about 82% on Spanish and French but drops to roughly 74% on Arabic and Mandarin, the same pattern visible inside a single tool.
The false-positive trap for multilingual writers
The deeper problem is fairness. Stanford's language lab found ESL essays at B2 to C1 level received elevated AI scores at 2.1 times the rate of native-speaker essays, and Liang et al. measured a 61.3% false-positive rate on Chinese students' TOEFL essays versus about 5.1% for US students.
Formal, careful, second-language prose shares the uniform structure detectors mistake for machine text, so non-native writers are punished for writing well. That is an equity problem, not a feature.
Which tools actually cover many languages
Coverage varies as much as accuracy. Crossplag supports more than 100 languages on premium tiers and is built for bulk multilingual scanning, which puts it in contention for institutions with international submissions.
Copyleaks covers about 30. Turnitin remains English-first, with Japanese added only in 2025 and paraphrased-text detection still largely English-only. Pick the tool to the languages your writers actually use, not to the marketing headline.
Pangram's language-specific approach
Pangram Labs takes a different technical route: instead of one model for every script, it uses specialized tokenizers per language, which it claims holds accuracy above 99% across its top-20 internet languages without the English drop-off.
Independent testers credit it with the lowest false-positive design goal of the category, aimed at settings where a wrong accusation is costly. It is less established and more API- than consumer-oriented, but worth trying when fairness matters.
What to do if you write or grade in another language
Treat any non-English score as advisory, not evidence. Cross-check with a multilingual-aware tool, keep a human in the loop, and never act on a single number for a non-native writer.
A free, no-signup self-check such as Scribbr explains its flags in plain language, which is more useful than a percentage when you are deciding whether to flag a person. The practical rule is blunt: the further a language sits from English in the training data, the less the score is worth.
Bottom line
AI detectors fail outside English because they were trained on it first. Copyleaks scores about 91% on English and ~74% across 30-plus languages; GPTZero holds ~82% on Spanish and French but ~74% on Arabic and Mandarin; CJK and low-resource languages fall to 55% and below. The fairness problem is worse: ESL writers are false-flagged at 2.1x the native rate, and Chinese students' TOEFL essays hit 61.3% false positives versus 5.1%. Crossplag, 100-plus languages, and Pangram, specialized tokenizers with low false positives, are the multilingual picks. Treat any non-English score as advisory, cross-check, and self-review with a free explainable tool such as Scribbr.