GLM AI Detector

Checking a text you think came from GLM? This free GLM AI detector scores English writing for the patterns that GLM and other AI models produce. Paste 100 words or more into the checker above, and V4, our current engine, returns an AI-writing signal score from 1 to 99, a stability range and a verdict: Likely human-written, Uncertain or Likely AI-written. You also see how often human writing of the same length was wrongly flagged in our tests. It costs nothing, needs no account and does not keep your text.

GLM writing was part of what V4 learned from, and the frozen engine caught 230 of 233 GLM texts of 150 words or more in our sealed test. This guide covers how to check GLM text, how to read the result, how GLM was trained and tested, and the cases where a score needs extra care.

Can a GLM AI detector tell whether GLM wrote a text?

It can tell you whether the writing looks AI-generated; it cannot tell you that GLM generated it. GLM, Qwen, Kimi and other assistants write in overlapping registers, and V4 learned from nine families together. Its score is about the writing, never about which model produced it.

• It can score English text of 100 words or more and return one of three verdicts.
• It can catch unedited GLM output: 98.71% at 150 words or more in the sealed test.
• It cannot name GLM, or tell the full model from the Flash model.
• It cannot prove authorship. Editing, paraphrasing and translation all shift the score.

How the GLM AI checker works

Behind the checker is one fine-tuned transformer text classifier. It reads the passage in overlapping windows and averages them into a single internal value, which is mapped to the 1–99 score. Training used texts from nine model families, GLM among them, and a large body of human writing from 2021 or earlier. The engine was then frozen, and it is never tuned on the texts people check.

It works only from the text you paste. It does not compare it with other documents, search online, or rate individual sentences; the score covers the whole passage. For more, see how the detector works.

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.

Why the GLM Flash model needs a closer look

GLM comes in a full model and a lighter Flash model, and the two did not score the same. The full model was caught 99.43% of the time on 176 texts of 150 words or more. The Flash model was caught 96.49% of the time on 57 texts, the lowest version figure among the commercial families in this test, with a wide interval because the sample was small.

In practice that means a GLM Flash text scoring under the line is a little more plausible than for most families. If you have reason to think a text came from a smaller, faster model, treat a human-zone or Uncertain result as unknown rather than as evidence of human writing, and check a longer sample if you can.

How to check GLM text

1. Use the text as submitted, not a cleaned-up copy.
2. Paste 100 words or more into the checker above; 150 or more makes the result steadier.
3. Run the check and read the verdict, the score and the stability range.
4. Read the evidence line for how human texts of that length scored in the sealed test.
5. Look for supporting evidence: drafts, sources, version history and the writer’s other work.

Up to 3 passages can be compared side by side, and any result can be saved as a PDF.

How to read a GLM detector result

The scale has three zones. A score of 1 to 34 is Likely human-written: the passage looks like most human writing of its length. 35 to 64 is Uncertain: above typical human writing but below the AI line. 65 to 99 is Likely AI-written, shown with a Moderate signal chip from 65 to 84 and a Strong signal chip from 85 to 99.

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 sits on it. The stability range shows how much the score moves when the source families behind the score map are resampled, while the verdict always comes from the frozen line. The full card is explained in understanding results.

How V4 was trained on GLM text, and how well it detects it

GLM output is one of the nine families in V4’s training data. To find out how well that carries over to GLM texts the engine had never seen, we scored a sealed set once, on 17 October 2026.

The GLM texts were gathered from 10 to 13 October 2026 and cover 35 writing tasks, from meeting notes and press releases to personal statements and story openings, in 6 styles, with half of the assignments rewritten from or modelled on human texts. They came through the Together API. The full model counted as strong-tier, but it ignores the reasoning switch, so all of its texts ran at the provider default; the Flash model ran at the default as well. Each sample is the model’s final reply alone, with reasoning sections and near-repeats removed.

What the results show:

• The Flash model was the harder of the two, at 96.49% over 57 texts, while the full model was caught almost every time.
• An unusual weak band. By length, 98.11% were caught at 100 to 149 words, 98.85% at 150 to 299, 97.37% at 300 to 599 and 100.00% at 600 or more. For most families the shortest band is the weakest; for GLM it was the 300 to 599 band.
• Without GLM in training, in the development test, 97.97% of GLM texts were still caught.
• The previous engine caught 24.46% of the same texts, under a quarter.
• Rewriting tricks such as typos, look-alike characters and synonym swaps were tested on all development texts together; synonym swaps were hardest and still left 97.40% of AI texts caught.

Every family and length band is on the evidence page; the methodology explains how the sets were built and kept apart.

One real example

To show what a typical result looks like, a 297-word blog post from GLM, drawn from development data and not the sealed set, scored 94 (range 91–97; Likely AI-written). It opens with these 66 words:

Hi all, during the time when I had Sepsis and was in the ICU, I lived through many strange things – some of them were real, some not. Let me explain. When I woke up in the intensive care, I was sure that the hospital was built inside a shopping centre. I was laying in the bed and I saw people walking around with shopping bags.

Edited GLM text and false flags

The test used GLM text exactly as generated. Human edits, paraphrasing tools and translation were not part of it, and they usually lower the score. A low score on edited text says little either way.

Human writing is sometimes flagged too. In the sealed test, 4 of 2,863 human texts of 150 words or more reached 65, about 1 in 700, from sources the engine had never trained on. At 100 to 149 words, 11 of 1,136 did, about 1 in 100. Learners of English were flagged about 3 times as often as native writers at the same lengths, and formal speeches reached the line in 17 of 931 cases. Anyone whose writing was flagged can find practical steps in what to do if your text is flagged.

Using a GLM checker responsibly

Let the score guide where you look, then rely on the evidence of how the text was written. Talk to the writer, check drafts and sources, and never decide a grade, a contract or a complaint on a score alone. Short passages, learner English and formal writing need extra caution.

GLM AI detector FAQ

Other AI model guides

The same checker covers every family we tested, each with its own results. See all model guides, or go to the Qwen AI detector, Kimi, MiniMax or the ChatGPT detector.

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