AI detectors for localisation and translation teams
An AI detector for a localisation team is judged on two questions the vendor pages answer separately: how many languages the tool claims to read, and what happens to a human translation, a machine-translated draft or a post-edited one when it is scored. Two fields decide which cards appear in this view: a stated language count of 10 or more in languages, and the nonenglish_records count compiled on 9 November 2026, the number of indexed study records that print a result for the card on text that is not native English prose. Of the 72 cards, 14 state 10 or more languages, from Winston AI's 11 to Smodin's 100, and 6 of the 14 have at least one record on translated, non-English or non-native text; 8 have none. The records behind the second field are Weber-Wulff 2023 (9 human documents machine-translated into English, class 02-MT), Liang 2023 (91 TOEFL essays), Orenstrakh 2023 (10 Spanish theses and 10 Spanish ChatGPT texts), Ibrahim 2023 (120 ESL essays) and Chaka 2023 (translations into German, French, Spanish, Southern Sotho and isiZulu), each read in the primary source and ordered on the non-English evidence ranking. This page is one of the views under AI detectors by use case; the languages ranking orders every stated count, the machine-translated text question reads the two translation records in detail, and the full index shows all 72 cards.
- Field 1Languages stated
- Field 2Modality
- Field 3Non-English study records (result for this card)
- Field 4Source, read date
The language count comes first and is quoted as the vendor states it, because a count of 100 (Smodin) and a count of 11 (Winston AI) are both claims and neither is a test; the generators covered ranking treats model lists the same way. The record count is the index's own measure: a study that scored the card on translated, non-English or non-native text, counted once per record, with the result quoted where the record prints one for the card. The modality column is carried because two of the 14 are voice detectors whose language counts describe speech, not documents. Field rules are in the methodology.
Which fields decide fit for a translation team?
| Detector | Languages stated | Modality | Non-English study records (result for this card) | Source, read date |
|---|---|---|---|---|
Smodin | 100+ (API language code list of 104 values) | Text | 0 | smodin.io/ai-content-detector, 9 November 2026 |
Resemble Detect | 54 | Voice, image, video, music | 0 | resemble.ai/benchmarks, 9 November 2026 |
Undetectable AI | 50 | Text | 0 | undetectable.ai/ai-detector, 9 November 2026 |
Pindrop Pulse | 40 | Voice, video (meetings) | 0 | pindrop.com, 9 November 2026 |
Compilatio | 35 | Text, handwriting (OCR) | 1: Weber-Wulff 2023, 0 false accusations in 18 human documents, 9 machine-translated | compilatio.net/ai-detector-info, 9 November 2026 |
Copyleaks | 30 | Text, code, image, video | 3: Orenstrakh 2023 (all 10 Spanish human theses flagged as AI), Chaka 2023 (3 of 6 German, 5 of 6 French, 6 of 6 Spanish translations classified AI), Van Vlasselaer 2026 | copyleaks.com/ai-content-detector, 8 November 2026 |
Originality.ai | 30 | Text, code | 1: Liang 2023, one of 7 detectors averaging 61.22% false positives on 91 TOEFL essays (chart only, no per-tool figure) | originality.ai, 8 November 2026 |
TruthScan | 30 | Text, image, PDF, video, voice | 0 | truthscan.com/faq, 9 November 2026 |
Quetext | 25 | Text | 0 | quetext.com/ai-content-detector, 9 November 2026 |
Pangram | 24 | Text, image, video | 1: Van Vlasselaer 2026, 0 of 40 human papers by non-native graduate students flagged | pangram.com, 8 November 2026 |
Proofademic | 23 | Text, image (API), voice (API) | 0 | proofademic.ai/multilingual-detection, 8 November 2026 |
Walter Writes | 20 | Text, image (API), voice (API) | 0 | walterwrites.ai/ai-detector, 8 November 2026 |
Sapling | 16 | Text | 1: Liang 2023, one of 7 detectors averaging 61.22% false positives on 91 TOEFL essays (chart only) | sapling.ai/ai-content-detector, 8 November 2026 |
Winston AI | 11 | Text, image, handwriting (OCR) | 1: Weber-Wulff 2023, 0 false accusations in 18 human documents, 9 machine-translated | gowinston.ai/ai-content-detector, 8 November 2026 |
Values as each vendor states them, read 8 and 9 November 2026, sorted by the stated count. Turnitin (7 languages, with the dated list), QuillBot (6), GPTZero (5) and Phrasly (4) state fewer than 10 and are absent, though GPTZero holds the most non-English records in the index at 5 and Turnitin 2, as the non-English evidence ranking shows; the GPT-2 Output Detector (4 records), ZeroGPT (2), GLTR, Writer, DetectGPT and BrandWell (1 each) state no language count. Walter Writes' pricing page states "80+ languages supported" for the product as a whole and its detector page 20; the card carries the detector page's figure.
Why each card is in this view
- AIDI-D-0029Smodin
the largest stated count in the index, "100+ languages" on the detector page and a 104-value language code the API requires per request (smodin.io/ai-content-detector, docs.smodin.io, read 9 November 2026); no study record in any language.
- AIDI-D-0042Resemble Detect
54 languages for voice deepfake detection on the vendor's benchmarks page (resemble.ai/benchmarks, read 9 November 2026); a speech count, not a document count, and no indexed record; the ASVspoof 5 and SAFE 2025 challenges name no card.
- AIDI-D-0024Undetectable AI
50 languages stated on the detector page (undetectable.ai/ai-detector, read 9 November 2026); one study record, Kar 2024, on English text only.
- AIDI-D-0041Pindrop Pulse
40 languages for audio deepfake detection in the Pulse Inspect release (pindrop.com, read 9 November 2026); speech only, no indexed record.
- AIDI-D-0032Compilatio
35 languages, with the vendor's own reliability figure of 94% to 99% measured on about 7,400 texts in 24 languages for model 4.5.3 (compilatio.net/ai-detector-info, support article, read 9 November 2026); 0 false accusations in Weber-Wulff 2023's 18 human documents, 9 of them machine-translated, on the 2023 free tool.
- AIDI-D-0003Copyleaks
30 languages and the most non-English records of the 14 at 3 (copyleaks.com/ai-content-detector, read 8 November 2026): every Spanish text in Orenstrakh 2023 called AI, human or not; 3 of 6 German, 5 of 6 French and 6 of 6 Spanish machine translations of ChatGPT text classified AI in Chaka 2023; and the non-native graduate papers of Van Vlasselaer 2026.
- AIDI-D-0004Originality.ai
30 languages (originality.ai, read 8 November 2026); one of the seven detectors in Liang 2023, whose per-tool TOEFL figures are printed as a chart only, so the card carries the study's 61.22% average.
- AIDI-D-0034TruthScan
30 languages on the FAQ (truthscan.com/faq, read 9 November 2026); no study record.
- AIDI-D-0033Quetext
25 languages on the detector page (quetext.com/ai-content-detector, read 9 November 2026); no study record.
- AIDI-D-0006Pangram
24 languages (pangram.com, read 8 November 2026); 0 of 40 human papers by non-native graduate students flagged in Van Vlasselaer 2026, and 45.5% of 1,163 real theses flagged when run alone in the same study. The vendor's model card reports multilingual figures that the index does not count as a record.
- AIDI-D-0014Proofademic
23 languages on its multilingual detection page (proofademic.ai/multilingual-detection, read 8 November 2026); no study record.
- AIDI-D-0015Walter Writes
20 languages on the detector page (walterwrites.ai/ai-detector, read 8 November 2026); no study record.
- AIDI-D-0010Sapling
16 languages (sapling.ai/ai-content-detector, read 8 November 2026); one of Liang 2023's seven detectors, chart only.
- AIDI-D-0005Winston AI
11 languages (gowinston.ai/ai-content-detector, read 8 November 2026); 0 false accusations in Weber-Wulff 2023's 18 human documents, 9 of them machine-translated, as Go Winston in March 2023.
What the evidence says for translated text
Machine translation moved detectors in the one record built to test it. Weber-Wulff 2023 wrote 9 human documents in Bosnian, Czech, German, Latvian, Slovak, Spanish or Swedish, machine-translated them into English with DeepL or Google Translate, and scored them beside 9 human documents written in English: average binary accuracy across 14 tools was 94% on the English human class (01-Hum) and 69% on the machine-translated class (02-MT), and the authors describe a drop of about 20 points on the translated class (arXiv 2306.15666, Table 5 and Section 4, read 8 November 2026; Weber-Wulff 2023 record). Two of the 14 tools' false accusations in that study fell on translated texts: the GPT-2 Output Detector's 2 of 18 were both machine-translated documents, and Writer's 1 of 18 was too; Winston AI and Compilatio, the two cards in this view that were tested, recorded 0. A human translation scored by a detector trained on English prose is the document class the index has the least evidence on, and a machine-translated draft is the class with the measured drop.
Non-native writing is the class with the worst false positive record, and a localisation team's source texts are often written by non-native authors before translation. Liang 2023 reported an average false positive rate of 61.22% across seven detectors on 91 TOEFL essays against near-perfect accuracy on 88 US 8th-grade essays, and attributed the gap to lower lexical variety and simpler syntax (arXiv 2304.02819, read 8 November 2026; Liang 2023 record); Sapling and Originality.ai in this view were among the seven. Ibrahim 2023 found that the GPT-2 Output Detector gave 16 of 120 ESL essays (13.3%) an originality score under 95% (DOI 10.1186/s40468-023-00260-2, read 9 November 2026; Ibrahim 2023 record). Orenstrakh 2023 is the one record with text written in another language rather than translated: on 10 Spanish theses and 10 Spanish ChatGPT texts, Copyleaks and the OpenAI classifier called every submission AI-generated while the GPT-2 Output Detector, GPTZero, CheckForAI and GLTR called every ChatGPT text human (arXiv 2307.07411, Sections 4.1 and 5, read 9 November 2026; Orenstrakh 2023 record). Chaka 2023 translated six ChatGPT responses into five languages and found Copyleaks classified 3 of 6 German, 5 of 6 French and 6 of 6 Spanish translations as AI (DOI 10.37074/jalt.2023.6.2.12, read 9 November 2026; Chaka 2023 record).
The gap between the two fields in the table is the finding. The 14 cards state 488 language slots between them and hold 8 non-English records between 6 of them, and no indexed record scored any detector in more than five languages; Compilatio's 24-language measurement is the vendor's own and Pangram's multilingual model card likewise. Turnitin, outside this view at 7 languages, publishes release dates for its Spanish (12 September 2024) and Japanese (2 April 2025) models and states that paraphrase detection is English only, the one dated language roadmap in the index (Turnitin languages question). A localisation team should read a language count as a list of inputs the tool accepts, not of languages it has been shown to score, and no detector score should be the only evidence in a decision about a translator's work.
Sources
- https://smodin.io/ai-content-detector (read 9 November 2026)
- https://docs.smodin.io/reference/v2-ai-detection-single (read 9 November 2026)
- https://www.resemble.ai/benchmarks (read 9 November 2026)
- https://undetectable.ai/ai-detector (read 9 November 2026)
- https://www.pindrop.com/company-news/pindrop-launches-pulse-inspect (read 9 November 2026)
- https://www.compilatio.net/en/ai-detector-info/ (read 9 November 2026)
- https://support.compilatio.net/hc/en-us/articles/17408769118993 (read 9 November 2026)
- https://copyleaks.com/ai-content-detector (read 8 November 2026)
- https://originality.ai (read 8 November 2026)
- https://truthscan.com/faq (read 9 November 2026)
- https://www.quetext.com/ai-content-detector (read 9 November 2026)
- https://www.pangram.com/ (read 8 November 2026)
- https://proofademic.ai/multilingual-detection/?utm_source=aidetectorindex.com&utm_medium=owned&utm_campaign=for_localisation_teams (read 8 November 2026)
- https://walterwrites.ai/ai-detector/?utm_source=aidetectorindex.com&utm_medium=owned&utm_campaign=for_localisation_teams (read 8 November 2026)
- https://sapling.ai/ai-content-detector (read 8 November 2026)
- https://gowinston.ai/ai-content-detector/ (read 8 November 2026)
- https://arxiv.org/pdf/2306.15666 (read 8 November 2026)
- https://arxiv.org/pdf/2304.02819v3 (read 8 November 2026)
- https://doi.org/10.1186/s40468-023-00260-2 (read 9 November 2026)
- https://arxiv.org/pdf/2307.07411 (read 9 November 2026)
- https://doi.org/10.37074/jalt.2023.6.2.12 (read 9 November 2026)
Revision log: 2026-11-09: view created at index version 2026.11.9
FAQ: AI detectors for translation teams
Which AI detectors state 10 or more languages?
Fourteen of 72 cards on 8 and 9 November 2026: Smodin (100+), Resemble Detect (54, voice), Undetectable AI (50), Pindrop Pulse (40, voice), Compilatio (35), Copyleaks, Originality.ai and TruthScan (30), Quetext (25), Pangram (24), Proofademic (23), Walter Writes (20), Sapling (16) and Winston AI (11). Each count is the vendor's statement.
Do AI detectors flag machine-translated text?
Weber-Wulff 2023 measured it: average accuracy across 14 tools fell from 94% on human English text to 69% on 9 human documents machine-translated into English (arXiv 2306.15666, read 8 November 2026). The GPT-2 Output Detector's two false accusations and Writer's one in that study were all on translated texts; Winston AI and Compilatio recorded 0.
Which of the 14 have been tested on non-English text?
Six, with 8 records between them: Copyleaks (3: Orenstrakh 2023, Chaka 2023, Van Vlasselaer 2026), Compilatio, Winston AI, Pangram, Originality.ai and Sapling (1 each). Eight state 10 or more languages and have no record on any non-English text. The non-English evidence ranking orders all 14 cards in the index with a record.
Has a detector been tested on text written in another language?
One indexed record: Orenstrakh 2023 ran eight detectors on 10 Spanish human theses and 10 Spanish ChatGPT texts, where Copyleaks and the OpenAI classifier called all 20 AI-generated and four tools called every ChatGPT text human (arXiv 2307.07411, read 9 November 2026). Chaka 2023 tested translations of ChatGPT text, not human writing.
Are non-native authors flagged more often?
Liang 2023 found an average 61.22% false positive rate across seven detectors on 91 TOEFL essays against near-perfect accuracy on US student essays (arXiv 2304.02819, read 8 November 2026); Ibrahim 2023 found 13.3% of 120 ESL essays under a 95% originality cut on the GPT-2 Output Detector (DOI 10.1186/s40468-023-00260-2, read 9 November 2026). Vendors have since published improvement claims, recorded on each card.
Does a large language count mean the tool was tested in those languages?
No. The counts are vendor statements; the only vendor-measured multilingual figure on a card in this view is Compilatio's 94% to 99% on about 7,400 texts in 24 languages, measured by the vendor for model 4.5.3 (support.compilatio.net, read 9 November 2026), and no indexed study scored any detector in more than five languages.

















