DeepSeek AI Detector

Want to know whether a text was written with DeepSeek? This free DeepSeek AI detector checks English writing for the patterns that DeepSeek and other AI models leave behind. 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. No account is needed and nothing you paste is stored.

DeepSeek writing was part of V4’s training, and in our sealed test the engine caught 256 of 261 DeepSeek texts of 150 words or more. Below you will find how to run a DeepSeek check, how to read each part of the result, what we learned about DeepSeek in particular and where a score stops being useful.

Can a DeepSeek AI detector prove DeepSeek wrote a text?

No detector can prove that. What this checker can do is tell you how strongly a passage shows AI-writing patterns, and DeepSeek output usually shows them clearly. What it cannot do is separate DeepSeek from ChatGPT, Qwen, Kimi or any other assistant: V4 learned from nine families together, and it never names a model.

• It does score English passages of 100 words or more and give one of three verdicts.
• It does catch most unedited DeepSeek text: 98.08% at 150 words or more in the sealed test.
• It does not identify DeepSeek, its larger Pro model or its Flash model as the source.
• It does not settle authorship. A person’s edits, a paraphrasing tool or a translation can all move the score.

One naming note, because it confuses people: V4 is the name of our detector engine, not a DeepSeek release. DeepSeek uses similar version numbers for its own models, which appear only in the test table below.

How the DeepSeek AI checker works

The checker is a single fine-tuned transformer text classifier. It reads your passage in overlapping windows, averages what it sees into one internal value and turns that value into the 1–99 score. The engine was trained once and frozen: its training data held texts from nine model families, DeepSeek among them, and a large body of human writing from 2021 or earlier. Nothing is adjusted on the texts people submit.

Because it reads patterns rather than looking anything up, the checker does not compare your text with other documents, does not search the web and is not a plagiarism check. It judges the passage as a whole, not sentence by sentence. More detail is in 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.

DeepSeek’s reasoning mode and what gets checked

DeepSeek models can think through a problem before answering, and the app can show that reasoning on screen. What reaches a reader, though, is the final answer: the essay, email or report someone copies out. That is also what we tested. In the sealed set, a quarter of the larger model’s texts were written with reasoning switched on and the rest with it off, any visible reasoning was stripped, and only the answer itself was scored.

So when you check a DeepSeek text, paste the answer, not the reasoning. If a pasted passage still contains a visible chain of thought, remove it first; it is a different kind of text and was not part of the measurement.

How to check DeepSeek text

1. Start from the original. Use the text exactly as it was handed in or published.
2. Paste 100 words or more into the box at the top of the page; 150 or more gives a steadier reading and fewer false flags on human writing.
3. Run the check and note the verdict, the score and the stability range.
4. Read the evidence line, which says how often human texts of the same length reached that zone in our sealed test.
5. Look beyond the score: drafts, notes, sources and earlier writing tell you far more about how a text was produced.

For a long report, check it in sections as well as whole; up to 3 passages can be compared side by side. Results can be saved as a PDF.

Reading a DeepSeek detector result

The line is 65 at every text length. Each length band has its own frozen internal threshold, and the score is mapped so that 65 sits exactly on it. The stability range shows how much the score moves when the source families behind the score map are resampled; it does not change the verdict. Understanding results explains every line on the card.

DeepSeek has one point to keep in mind here. It is the family V4 finds hardest among the eight commercial families we tested, so a DeepSeek text landing in the human or Uncertain zone is a little more plausible than for most families, especially with a long text. Treat such a result as no answer rather than as evidence of human writing, and read the passage yourself.

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

DeepSeek output is in V4’s training data, alongside eight other families. To measure what that achieved, the frozen engine was scored once, on 17 October 2026, on fresh DeepSeek texts that had been locked away before training and never used to tune it.

Those test texts were created from 10 to 13 October 2026 through the Together API, across 35 task types ranging from argumentative essays and grant paragraphs to casual emails and social threads, in 6 styles. Half of the prompts were derived from human seed texts so that the topics matched real writing, as for every family. The larger Pro model was treated as a strong-tier model, with a quarter of its texts written with reasoning on; the Flash model ran at the provider’s default. Repeated and near-identical texts were filtered out before scoring.

What the numbers show:

• The larger model is the harder case. The Pro model was caught 97.47% of the time over 198 texts; the Flash model was caught on every one of its 63.
• Long texts were the weak band. By length, 98.00% were caught at 100 to 149 words, 98.68% at 150 to 299, 98.72% at 300 to 599 and 97.20% at 600 words or more, unusual for a family.
• Training on DeepSeek matters. In the development leave-one-family-out test, with DeepSeek removed from training, 95.12% of its texts were caught, the lowest of the eight commercial families against an average of 97.90%. With DeepSeek in training, the frozen engine reached 98.08% in the sealed test.
• Against the previous engine. V3 caught 35.63% of the same texts; V4 catches 98.08%.
• Rewriting tricks. Attacks were run on the pooled development data, not per family; even synonym replacement, the strongest, left 97.40% of AI texts above the line.

The full set of families and human sources is on the evidence page, and the methodology describes how the sets were built.

One real example

An example from development data, never used in the sealed test: this parliamentary speech in a plenary debate by DeepSeek runs to 294 words, scored 97 (stability range 94–99) and fell in the zone Likely AI-written. The first 73 words:

Mr President, I rise to address the proposal the European Commission has placed on the table today concerning the implementation of the Cartagena Protocol on Biosafety. The Protocol is not an abstract instrument; it is the first binding international framework designed to protect biological diversity from the potential risks posed by living modified organisms. The Commission’s proposal, already the subject of considerable scrutiny in committee, now demands our full attention in this House.

Missed DeepSeek text, edits and false flags

Five of the 261 DeepSeek texts of 150 words or more stayed under the line, all from the larger model. Edited text widens that gap: once a draft is reworded, shortened or translated, the score usually drops and the sealed figures no longer describe it. A low score is therefore weak evidence that a passage is human.

False positives run the other way. In the sealed test, human writing from sources the engine never trained on reached 65 about 1 in 700 times at 150 words or more (4 of 2,863) and about 1 in 100 times at 100 to 149 words (11 of 1,136). Learners of English were flagged about 3 times as often as native writers at the same lengths, and formal speeches were the one register where V4 did worse than the previous engine, with 17 of 931 reaching the line.

If a text of yours was flagged, collect drafts and version history, offer a longer sample and read what to do if your text is flagged.

Using a DeepSeek checker in a fair review

A score helps you decide where to look, not what to conclude. Ask the writer how the text was produced, compare it with their earlier work and check the sources it cites. Never fail, reject or discipline anyone on a detector result alone, and be especially careful with short passages, learner English and formal speeches.

DeepSeek AI detector FAQ

Other AI model guides

The same checker covers every family we tested. Browse all model guides or compare with the Qwen AI detector, Kimi, GLM, the ChatGPT detector and the Claude AI detector.

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