Updated Monthly

AI Detector Accuracy Benchmark

Independent monthly benchmark comparing 10 AI content detectors on accuracy, false positive rates, and speed across 5 AI models.

April 2026 Edition · 500 text samples · Transparent methodology

April 2026 key insights

Overall accuracy is improving

The average accuracy across all 10 detectors rose from 84.2% in January to 85.7% in April — a 1.5 percentage point gain in 4 months.

Claude detection remains the biggest gap

The accuracy spread for Claude 3.5 content is 22.4 points (72.4% to 94.8%) — the largest gap of any model. Tools trained primarily on GPT data continue to struggle.

False positives trending down

Average false positive rate dropped from 9.8% in January to 8.8% in April. Most tools are getting better at not flagging human text.

Open-source model detection improving

LLaMA 3 and Mistral detection improved across the board as more detectors add open-source model data to their training sets.

April 2026 rankings

Overall accuracy, per-model breakdown, false positive rates, and month-over-month change for each detector.

#DetectorOverallGPT-4oClaude 3.5Gemini ProLLaMA 3MistralFP RateMoM
11aidetectors.io95.2%96.1%94.8%93.7%95.4%96%3.1%+0.3
2Originality.ai91.4%93.2%90.1%89.8%91%92.8%6.2%+0.1
3Copyleaks89.7%92%87.4%88.1%89.2%91.8%7.8%-0.2
4GPTZero88.4%92.8%83.2%84.6%86.1%85.3%9.7%+0.5
5Winston AI87.1%90.4%84.8%83.5%86.2%90.6%8.5%-0.1
6Content at Scale84.6%88.2%81%80.7%83.5%89.6%8.9%+0.0
7Turnitin AI83.8%89.4%79.2%78.8%81%90.6%12.1%-0.4
8ZeroGPT82.1%86.5%78.1%77.3%80.4%88.2%14.2%+0.2
9Sapling79.3%83.4%75.8%74.2%78.1%85%11.5%-0.3
10Writer.com76.8%81.2%72.4%71%75.3%84.1%12.3%+0.1

Historical trend (2026)

MonthTop ScoreAvg Score (All 10)Avg FP Rate
Jan 202694.1%84.2%9.8%
Feb 202694.6%84.7%9.5%
Mar 202694.9%85.1%9.2%
Apr 202695.2%85.7%8.8%

Methodology

Our benchmark is designed to be rigorous, transparent, and reproducible. We follow the same methodology each month to ensure consistent, comparable results.

Test corpus (500 texts)

  • 200 human-written texts — essays, journalism, blog posts, academic papers, creative writing. Includes ESL writers from 10+ countries.
  • 100 GPT-4o texts — generated with default settings on matching topics
  • 75 Claude 3.5 Sonnet texts — generated with default settings
  • 50 Gemini Pro texts — generated with default settings
  • 50 LLaMA 3 70B texts — generated via Groq API
  • 25 Mistral Large texts — generated via Mistral API

What we measure

  • Overall accuracy: Correct classifications out of 500 total texts
  • Per-model accuracy: Detection rate for each AI model separately
  • False positive rate: Percentage of human texts wrongly flagged as AI
  • Processing speed: Average time per 400-word text

Rules

  • All tests use each tool's default settings — no custom thresholds or optimizations
  • Each text is 300-600 words in length
  • No texts are paraphrased, edited, or mixed — pure AI output or pure human writing
  • Fresh text samples are generated each month to prevent overfitting
  • We test the publicly available version of each tool (no beta access or special arrangements)

Disclosure

This benchmark is published by aidetectors.io. We include ourselves in the benchmark and report all results truthfully regardless of outcome. We encourage other detectors to publish their own independent benchmarks. If you spot an error or want to suggest improvements to our methodology, contact us at [email protected].

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Cite this research

Journalists, researchers, and educators are welcome to cite this benchmark. Please use the following citation:

aidetectors.io. "AI Detector Accuracy Benchmark — April 2026." aidetectors.io, April 2026. https://www.aidetectors.io/ai-detector-accuracy-benchmark

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