Qwen AI Detector

Use this free Qwen AI detector to check whether an English text shows the writing patterns of Qwen and other AI models. Paste 100 words or more into the checker above. V4, our current engine, gives the passage an AI-writing signal score from 1 to 99, a stability range and a verdict of Likely human-written, Uncertain or Likely AI-written, with the error rate measured for texts of that length. It is free, with no sign-up, and your text is not saved.

Qwen output was in V4’s training data, and in our sealed test the engine caught 254 of 255 Qwen texts of 150 words or more. This guide explains how the Qwen checker works, how to read what it tells you, what we measured on Qwen text and when to be careful with a score.

Can a Qwen AI detector identify Qwen text?

It can identify AI-like writing, and Qwen text almost always shows it. It cannot identify Qwen as the source. V4 was trained on nine model families at once and gives one score for AI-writing patterns in general; it has no way to tell Qwen apart from DeepSeek, GLM or any other assistant.

• What it does: scores English text of 100 words or more and places it in one of three verdicts.
• How well: 99.61% of unedited Qwen texts of 150 words or more were caught in the sealed test.
• What it does not do: name Qwen, tell the large model from the Flash model, or prove who wrote a text.
• What changes the result: human edits, paraphrasing tools and translation.

What the Qwen AI checker reads

The checker is a fine-tuned transformer text classifier. It moves through the passage in overlapping windows, averages them into one internal value and maps that onto the 1–99 score. It learned from texts by nine model families, Qwen among them, and from a large body of human writing dated 2021 or earlier. It was trained once and frozen; no live text ever adjusts it.

It reads wording and structure only. There is no database lookup, no web search and no plagiarism check, and it scores the passage as a whole rather than sentence by sentence. How the detector works has more on the engine.

The score is not the share of the text written by AI, and it is not a probability. A result alone does not prove who wrote a text.

Qwen text in English and in other languages

Qwen is widely used for writing in several languages, and many people use it to write in English as a second language or to translate a draft into English. This checker only covers English. A passage that Qwen wrote in another language, or a human draft that Qwen translated into English, is outside what we tested, and the score on it should not be read against the figures in this guide.

If you are checking English text from a writer whose first language is not English, keep in mind that learner writing is wrongly flagged more often than native writing (more on that below). Our language guidance explains what the checker supports.

How to check Qwen text

1. Take the text as it was given to you, without tidying it up first.
2. Paste 100 words or more into the checker. Passages of 150 words or more are more reliable.
3. Run the check and look at the verdict, the score and the stability range as a set.
4. Read the evidence line for your text’s length band.
5. Bring in context: drafts, sources, version history and how the writer usually writes.

Compare mode lets you check up to 3 passages side by side, which helps when you have earlier work from the same writer. You can also save a result as a PDF.

What a Qwen detector result means

The AI line is 65 at every length. Each length band has its own frozen internal threshold, and the score is mapped so that 65 always falls on it. The stability range shows how much the score moves when the source families behind the score map are resampled; the verdict comes from the frozen line, not from the range. Understanding results explains each part of the card.

A Qwen text that lands in the Uncertain or human zone is most likely short or edited. A high score is a signal about patterns that many assistants share, not a sign that Qwen in particular was used.

How V4 was trained on Qwen writing, and how well it detects it

Qwen writing was one of the nine families V4 learned from. To measure the frozen engine on Qwen text it had never seen, we scored a sealed set exactly once, on 17 October 2026, after generation had closed.

The Qwen set was collected from 10 to 13 October 2026 and spans 35 formats, such as blog posts, complaint letters, abstracts, biographies and how-to guides, in 6 tones. Half of the topics came from a general list and half were adapted from human source passages. The texts came through the Together API. The large mixture-of-experts model counted as strong-tier, with a quarter of its texts written with reasoning on; its chat template rejects reasoning fully off, so the other three quarters ran at a low reasoning setting. The Flash model ran at the provider default. Each text was reduced to its final reply, and duplicates were filtered out first.

What stands out:

• Both models near the top. The large model was caught 99.45% of the time over 181 texts; the Flash model was caught on all 74 of its texts, a smaller sample.
• By length: 98.21% at 100 to 149 words, 100.00% at 150 to 299, 98.90% at 300 to 599 and 100.00% at 600 or more.
• Without Qwen in training, in the development leave-one-family-out test, 98.81% of Qwen texts were still caught, one of the strongest left-out results.
• Against the previous engine, which caught 43.92% of the same texts.
• Rewriting tricks: there is no per-family attack figure; across the development set, synonym swapping did the most damage and still left 97.40% of AI texts caught.

The evidence page holds the complete sealed test, and the methodology explains how it was built.

One real example

Sample text (development set only): Qwen, college essay, 248 words. It received 95 with a range of 92–98, placing it in Likely AI-written. Below are its first 67 words:

The opening sentence invites attention by promising a safe exception to a social taboo, and the subject it raises is not race itself but the ethics of humor within a pluralistic society. Jokes that refer to racial identity occupy a contested space because they can reveal prejudice, reinforce stereotypes, or expose hypocrisy, yet they can also create solidarity among members of a group or critique dominant norms.

Edited Qwen text and false positives

Only one Qwen text of 150 words or more slipped under the line in the sealed test, but that test used raw output. Text a person has reworded, cut down or run through a translation tool was not measured and usually scores lower, so a low score after editing tells you little.

On the human side, 4 of 2,863 texts of 150 words or more from sources the engine never trained on reached 65, about 1 in 700. At 100 to 149 words the rate was about 1 in 100 (11 of 1,136). Learners of English reached the line about 3 times as often as native writers at the same lengths, and at 100 to 149 words 15 of 572 learner texts did. Formal speeches were also flagged more often, 17 of 931. Writers in any of these groups deserve extra care, and what to do if your text is flagged explains how to respond.

Using a Qwen checker in a fair review

A score is a starting point. Agree in advance which uses of AI are allowed, then use the result to decide what to ask about, not what to conclude. Never grade, reject or accuse anyone on a detector result alone, and give the writer a chance to show drafts and explain their process.

Qwen AI detector FAQ

Other AI model guides

The same checker covers every family we tested. See all model guides, or compare with GLM, the DeepSeek AI detector, Gemini and the ChatGPT detector.

Tested and written by the AI Detector Checker team. Last tested: 17 October 2026.