Detect Claude AI text in seconds.
AI Checker spots Claude content with sentence-level accuracy. Free detector for Claude 1, Claude 2, Claude 3 Haiku, and 3 other Claude variants.
Last reviewed: .
Every major Claude version.
- Claude 1
- Claude 2
- Claude 3 Haiku
- Claude 3 Sonnet
- Claude 3 Opus
- Claude 3.5 Sonnet
Harder to detect.
Lightly edited and paraphrased Claude text typically scores 5-15% lower. Heavy human editing reduces confidence further — always review the sentence-level breakdown.
How AI Checker spots Claude.
Five fingerprints that Claude leaves behind, even after editing.
- 1Frequent parenthetical asides — Claude's stylistic tic
- 2Higher burstiness than GPT — varied sentence lengths
- 3Tendency to flag uncertainty ("I should note", "however")
- 4Long winding sentences with multiple subordinate clauses
- 5Preference for em-dashes over commas for emphasis
The Claude fingerprint.
Claude is the hardest major LLM to flag because Anthropic trained it for stylistic variation as a feature. Unlike ChatGPT, which optimizes for clarity, Claude optimizes for nuance — long sentences mixed with short ones, parenthetical hedges, and a writerly cadence that scores well on burstiness. AI Checker hits ~94% accuracy on unedited Claude output, but drops to 85-90% on Claude 3 Opus when prompted for casual tone. The strongest signal isn't perplexity or burstiness but lexical fingerprinting: Claude has distinct preferences (em-dashes, parenthetical phrasing, hedge words like "in some sense") that show up regardless of topic. For educators and editors reviewing suspected Claude submissions, the sentence-level breakdown is essential — Claude often produces 1-2 stylistically suspicious sentences in an otherwise human-looking paragraph.
What Claude writing looks like.
When considering the question of authenticity in modern writing — and this is a question that has only grown more pressing in recent years — we find ourselves grappling with several layered concerns. The first, perhaps obviously, is one of attribution: who, in some sense, can be said to have written a piece when an AI assistant has shaped its prose? The second concern, more subtle but equally important, has to do with the changing nature of expertise itself.
How Claude detection has evolved.
Claude's resistance to detection is the result of an intentional design choice by Anthropic: the model was trained with explicit reward signals for stylistic variation, which is exactly the dimension that single-signal burstiness detectors measure. The result is that Claude output looks more human than ChatGPT output on paragraph-level burstiness, even when the underlying token-probability profile is just as uniform. AI Checker's approach to Claude detection is therefore architecturally different from ChatGPT detection: rather than relying on burstiness, our Claude head emphasizes lexical fingerprinting against Anthropic's specific training distribution. Distinctive Claude markers include em-dash usage at roughly 2-3x the rate of typical English prose, parenthetical asides used as hedge devices, the phrase "in some sense" appearing in roughly 4% of long-form responses, and a structural preference for opening sentences with a clause that's then qualified mid-sentence. These markers persist across topic, length, and prompt style. The trickiest case is Claude 3.5 Sonnet with explicit informal-tone prompting — AI Checker's accuracy drops to 82% on unedited output and falls below 75% with one round of paraphrasing. For high-stakes detection scenarios involving suspected Claude content, we strongly recommend reviewing the sentence-level breakdown rather than relying on the single percentage; Claude's stylistic consistency means that if any sentence-level signal is high, the document is likely Claude-derived even when the headline number is moderate.
AI Checker accuracy on Claude.
Numbers from our internal benchmark suite. Refreshed quarterly.
| Metric | Value | Source |
|---|---|---|
| Unedited Claude 2 accuracy | 95.8% | Internal benchmark, Q1 2026 |
| Unedited Claude 3 Sonnet accuracy | 93.7% | Internal benchmark, Q1 2026 |
| Unedited Claude 3 Opus accuracy | 92.4% | Internal benchmark, Q1 2026 |
| Paraphrased Claude accuracy | 85.6% | Internal benchmark, Q1 2026 |
| Informal-prompt Claude 3.5 accuracy | 82.0% | Internal benchmark, Q1 2026 |
Three signals, one score.
Every Claude detection score is a fusion of three independent signals: perplexity (how predictable the text is to a reference language model), burstiness (variation in sentence length and rhythm across the passage), and lexical fingerprinting (model-specific phrasing tells calibrated against Claude output specifically). Single-signal detectors fail on Claude because each individual signal can be partially evaded — fusing all three is what produces the headline accuracy numbers above.
For long-form submissions, the score you see is a weighted aggregate of sentence-level signals; for short submissions (under 100 words), confidence intervals widen because the statistical fingerprint becomes less reliable. We surface that uncertainty in the breakdown so you can avoid over-trusting short-text scores. Claude detection models are retrained on each major release from Anthropic; current calibration tracks the variants listed above.
For deeper background on how the underlying detection pipeline works, read our technical primer — it covers perplexity, burstiness, and lexical fingerprinting in plain language with worked examples.
Frequently asked questions
Is Claude detection free?
Yes. AI Checker offers a free tier for detecting Claude text without signup. The free tier supports up to 10,000 characters per check with full sentence-level breakdown.
How accurate is Claude detection?
On unedited Claude output, AI Checker reaches 95-98% accuracy. Accuracy stays above 90% on lightly edited or paraphrased Claude content. Heavy human editing reduces detection confidence — always review the sentence-level breakdown for nuance.
Can Claude be used in a way that avoids detection?
Heavy paraphrasing and manual editing can lower detection scores, but multi-signal detection (perplexity, burstiness, lexical fingerprinting) usually still catches at least one signal. AI Checker reports a probability rather than a verdict — treat scores as evidence, not proof.
Does AI Checker detect all Anthropic models?
Yes. AI Checker is calibrated for every major model from Anthropic, including the latest variants. We retrain on each major release to keep detection signatures current.
Is my submitted text private?
Yes. Text submitted to AI Checker is processed in memory and is not used to train models. We do not sell or share your content. Free tier submissions are not stored beyond the immediate analysis.
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