Research
AI Detector Accuracy Benchmark
Our benchmark comparing 10 AI content detectors on accuracy and false positive rates across 5 AI models. Published by aidetectors.io, which is included in the results.
Last run April 2026 · 500 text samples · Next run scheduled · Methodology below
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 change since the previous run for each detector.
| # | Detector | Overall | GPT-4o | Claude 3.5 | Gemini Pro | LLaMA 3 | Mistral | FP Rate | vs Mar 2026 |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 1aidetectors.io | 95.2% | 96.1% | 94.8% | 93.7% | 95.4% | 96% | 3.1% | +0.3 |
| 2 | Originality.ai | 91.4% | 93.2% | 90.1% | 89.8% | 91% | 92.8% | 6.2% | +0.1 |
| 3 | Copyleaks | 89.7% | 92% | 87.4% | 88.1% | 89.2% | 91.8% | 7.8% | -0.2 |
| 4 | GPTZero | 88.4% | 92.8% | 83.2% | 84.6% | 86.1% | 85.3% | 9.7% | +0.5 |
| 5 | Winston AI | 87.1% | 90.4% | 84.8% | 83.5% | 86.2% | 90.6% | 8.5% | -0.1 |
| 6 | Content at Scale | 84.6% | 88.2% | 81% | 80.7% | 83.5% | 89.6% | 8.9% | +0.0 |
| 7 | Turnitin AI | 83.8% | 89.4% | 79.2% | 78.8% | 81% | 90.6% | 12.1% | -0.4 |
| 8 | ZeroGPT | 82.1% | 86.5% | 78.1% | 77.3% | 80.4% | 88.2% | 14.2% | +0.2 |
| 9 | Sapling | 79.3% | 83.4% | 75.8% | 74.2% | 78.1% | 85% | 11.5% | -0.3 |
| 10 | Writer.com | 76.8% | 81.2% | 72.4% | 71% | 75.3% | 84.1% | 12.3% | +0.1 |
Historical trend (2026)
| Month | Top Score | Avg Score (All 10) | Avg FP Rate |
|---|---|---|---|
| Jan 2026 | 94.1% | 84.2% | 9.8% |
| Feb 2026 | 94.6% | 84.7% | 9.5% |
| Mar 2026 | 94.9% | 85.1% | 9.2% |
| Apr 2026 | 95.2% | 85.7% | 8.8% |
Methodology
This is the aidetectors.io benchmark, not a third-party study. We follow the same methodology on every run so results stay comparable. The most recent run was April 2026; the next run is scheduled and will replace these figures when it completes.
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
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 for each run 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 and is not independent: we run the tests, we include our own detector, and we report all results regardless of outcome. Treat it as a vendor-run benchmark and weigh it accordingly. We encourage other detectors to publish their own tests. If you spot an error or want to suggest improvements to our methodology, contact us at [email protected].
Try the detector yourself
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Detection details
- PerplexityHow predictable each word is
- BurstinessVariation in sentence length
- N-gram patternsRepeated phrase signatures
- Stylometric fingerprintAuthor-level writing traits
Cite this research
Journalists, researchers, and educators are welcome to cite this benchmark. Please note it is published by aidetectors.io. Suggested citation:
aidetectors.io. "AI Detector Accuracy Benchmark — April 2026." aidetectors.io, April 2026. https://www.aidetectors.io/ai-detector-accuracy-benchmark