Model guide · reviewed 5 September 2026
DeepSeek AI Detector
The tool below is the same detector as on the homepage.
English · 100+ wordsengine v3
Paste text to check
V3 uses the same trained core as V2 with new length-based thresholds and a 0-100 display map. How it works →
What the evidence shows
No DeepSeek AI Detector-specific figure exists: the sealed test covered two current OpenAI models and one unseen model lineage. Human text from unseen sources received a wrong AI signal about 2 in every 1,000 times at 150 words or more. The detector never identifies which model wrote a text; this guide describes writing patterns to review, not a measured detection rate. See the evidence page.
Important: The tool does not identify the source model. Model version, prompt, settings, language, task, and human editing can make outputs from different systems overlap substantially.
The DeepSeek text people bring to a detector is rarely ordinary prose. It is a worked explanation: a derivation, a debugging walk-through, a step-by-step answer that shows its reasoning before it commits to a conclusion. Technical writing of that kind behaves differently under any AI detector than an essay does, and the reasons are worth understanding before you read a score. This guide describes what those outputs look like and exactly what AI Detector Checker measures when you paste one in.
AI Detector Checker never identifies which model wrote a text. It returns a 0-100 AI-writing signal score, a score stability range, and one of three bands — Likely human-written, Uncertain or Likely AI-written — for English text of at least 100 words. Nothing here can show that DeepSeek, rather than another assistant or a person, produced a passage.
Step-by-step and reasoning-style output
Reasoning-style output tends to make its working visible, and that produces a recognisable surface.
- Enumerated steps of equal weight. Step 4 gets the same three sentences as step 1, even when step 4 is the only difficult one. Human explanations are lopsided: they race through the routine part and slow down where the reader will get stuck.
- Restated problem before the answer. A paraphrase of the question opens the response, which is useful in a chat window and redundant in a document.
- Assumptions declared and never revisited. “Assuming the input is sorted” appears at the top and is not mentioned again when it stops holding.
- A verification paragraph that verifies nothing new. A closing check restates the result in different words rather than testing it against a case that could fail.
- Clean prose around code. The code may be idiosyncratic; the sentences between the code blocks are unusually even, because they were written last and all at once.
The most useful thing to look for is not style at all. It is whether the explanation knows which step is hard. Technical writing by somebody who solved the problem carries the memory of getting stuck; generated explanations of the same problem usually do not, because nothing in producing them was difficult.
Why technical text is the hardest case for a detector
Technical and procedural writing is constrained on purpose. Terminology is fixed, sentence length converges on whatever is clearest, first person is discouraged, and the structure is imposed by the task rather than chosen by the writer. Those constraints remove most of the variation a pattern-based classifier relies on. In our sealed test the hardest human family for this engine was issue-tracker text — technical, procedural, written by developers — at about 4.8 wrong AI signals in every 100, against a release target of 3 in 100. That target was missed, we published it rather than re-tuning against a test we had already seen, and the raw counts are on the evidence page.
The direct implication for this page: a Likely AI-written band on a human-written technical explanation is a documented, measured outcome of this engine, not an anomaly. Treat it accordingly.
What DeepSeek output looks like, and what the detector measures
The left column is what a reviewer notices reading the text. The right column is what the frozen engine behind this site computes. Only the right column produces the score.
| What a reader notices in DeepSeek text | What AI Detector Checker measures |
|---|---|
| Steps of equal weight regardless of difficulty | How closely the whole passage matches patterns a frozen classifier learned from human and AI text across many sources |
| The problem restated before it is answered | Variation in sentence length across the passage |
| Assumptions declared once and never revisited | Matches against a fixed list of 60 generic framing phrases |
| A verification step that restates rather than tests | The presence or absence of numbers, named people and places, and first-person reference |
| Code that does or does not actually run | Nothing — code is read as text, never executed or checked |
| Whether the explanation knows which step is hard | Nothing at all — the engine sees the pasted words and nothing else |
Strip the code blocks before you paste, or the score partly reflects text the engine was never designed to judge.
Running the check, and reading what comes back
Paste at least 100 words of English prose into the tool above, or on the homepage — it is the same detector. Paste the explanation, not the code, and if a document alternates between narrative and worked steps, check them separately; compare mode takes up to three passages and reports each one on its own, never averaged.
What an AI detector score means explains the score, the stability range and the three bands, and how the detector analyzes text explains where the number comes from. For technical text the honest reading is narrow: the score describes how closely this passage resembles the AI writing in our training data, at a threshold that was set before the test and has not moved. It says nothing about whether the method described is correct, which is the question that actually matters in technical review.
Reviewing a reasoning chain properly
For technical work there is a review that beats any detector, because it tests the thing you actually care about: whether the reasoning holds.
- Break the stated assumption. Take the assumption declared at the top and construct one input that violates it. A worked solution written by someone who understood the problem usually anticipates that case; a generated one often falls over silently.
- Run the verification step yourself. If the closing paragraph claims the result checks out, check it. Verification paragraphs that restate rather than test are common, and they are the fastest thing in a technical answer to falsify.
- Ask what was tried and abandoned. Real problem-solving leaves discarded approaches behind. An explanation that goes straight to the answer with no wrong turns is either expert or synthetic, and a single question to the author separates the two.
- Check the hard step against a second source. Generated derivations are usually right about the routine algebra and wrong in the same place a careless person would be: the one step everybody finds difficult.
None of that is what this site measures, and it is more reliable than what this site measures. Use the score to decide whether a passage is worth this attention, never as a substitute for it.
Where this goes wrong
Short technical passages are the worst case this site knows about: procedural writing is already the hardest human family for this engine, and at 100 to 149 words about 2.4 in every 100 human texts drew a wrong AI signal in the sealed test, against about 2 in every 1,000 at 150 words or more. A 120-word bug report is close to the limit of what a check like this can responsibly say anything about. If your own writing was flagged, what to do if your text is flagged sets out the next steps; language support explains why results on anything other than English are unsupported.
What the evidence does and does not cover
No DeepSeek-specific figure exists on this site. The sealed test that released this engine covered two current OpenAI models and one model lineage the engine had never seen in training; DeepSeek was not among them. What this page can point to is the measured difficulty of the register — technical, procedural human writing — and that figure is published in full, including the target it missed.
DeepSeek AI detector FAQ
Can AI Detector Checker tell whether DeepSeek wrote an explanation?
No. The engine returns a 0-100 AI-writing signal score, a stability range and one of three bands for the whole passage, and it never identifies a product. A step-by-step explanation from DeepSeek, from another assistant, or from a careful engineer can all produce the same band.
Why is technical writing the hardest case for this detector?
Technical prose is constrained on purpose: fixed terminology, converged sentence length, no first person, structure imposed by the task. Those constraints remove the variation the classifier relies on. In our sealed test the hardest human family was issue-tracker text at about 4.8 wrong AI signals in every 100, against a release target of 3 in 100 — a target that was missed and published rather than tuned away.
Should I paste code into the box?
No. Strip the code blocks and paste the prose. Code is read as text and never executed or checked, so leaving it in makes the score partly reflect material the engine was never designed to judge. The explanation around the code is the part worth checking.
What is a better test than the score for a worked solution?
Break the assumption the answer declares at the top with one input that violates it, and run the closing verification step yourself instead of trusting it. Generated derivations are usually right about the routine algebra and wrong at the one step everybody finds difficult.
Is there a published detection rate for DeepSeek R1?
No. The sealed test covered two current OpenAI models and one unseen model lineage; DeepSeek was not among them. What this page can point to is the measured difficulty of technical, procedural human writing, which is published in full on the evidence page.