# Best AI detector for news articles (tested)

Source: https://toolbenchmarks.com/best-ai-detector-for/news-articles/  
Tested: 30 October 2026. Publisher: Tool Benchmarks. Data licence: CC BY 4.0.

> Fast-DetectGPT is the best AI detector for news articles in our test. It caught 93.8% of AI-written news and flagged 2 of 24 human Wikinews reports (8.3%), a balanced accuracy of 92.7%. Desklib came second at 84.9% but flagged 5 of 24. With only 24 human reports, the 95% interval for Fast-DetectGPT's false positive rate still runs up to 25.8%.

| Detector | Balanced accuracy | Catches AI text | Flags human text |
|---|---|---|---|
| Fast-DetectGPT (Qwen2.5-1.5B) | 92.7% | 93.8% | 8.3% |
| Desklib AI text detector | 84.9% | 90.6% | 20.8% |
| MAGE | 79.2% | 91.7% | 33.3% |
| Fakespot AI text detector | 64.6% | 100.0% | 70.8% |
| E5-small LoRA detector | 62.0% | 94.8% | 70.8% |
| OpenAI RoBERTa detector | 55.7% | 15.6% | 4.2% |
| TMR (Target Mining RoBERTa) | 50.0% | 100.0% | 100.0% |

## Catch rates and false flags on news

Most detectors caught AI-written news better than any other kind of AI writing. Every detector except the OpenAI RoBERTa detector, which was built in 2019 for GPT-2, caught more than 90% of it. The human side went badly. TMR flagged all 24 human Wikinews reports, and Fakespot and E5-small LoRA flagged 17 each, so their near-perfect catch rates on AI news tell you very little.

The likely reason is the form itself. A wire-style report puts the main fact in the first sentence, attributes every claim ("police said", "according to the report"), stays in the past tense and keeps opinion out. Ask a chatbot for a news story and you get the same shape, so a classifier that has learned what AI news looks like has little left to separate it from a reporter's copy. A 167-word Wikinews report on the 67th Golden Globe Awards was flagged by six of the seven detectors, including both Fast-DetectGPT and Desklib.

## Why Fast-DetectGPT wins on news

[Fast-DetectGPT](/ai-detectors/fast-detectgpt/) reached 92.7% balanced accuracy on news, the highest score any detector got in any genre. It flagged 2 of 24 human reports and caught 93.8% of AI ones. Two caveats apply. Its threshold was tuned on our calibration split, which none of the other detectors had. And with 24 reports the intervals overlap heavily: 2.3% to 25.8% for its false positive rate, 9.2% to 40.5% for Desklib's.

News is also where Desklib loses its usual edge. Across all our human texts it flags the fewest of any useful detector, but here it flagged 5 of 24 reports (20.8%), more than Fast-DetectGPT and more than in any other genre. It still caught 90.6% of AI news. Desklib did much better on Wikipedia, which has a similar neutral tone; see the [encyclopedia results](/best-ai-detector-for/encyclopedia-articles/).

## Using a detector in a newsroom

Everything above is raw AI output. A story that an editor has reworked, or that a writer drafted with AI and then rewrote, is likely harder to catch. In our [humanized text test](/humanized-ai-text/), a light rewrite cut the catch rates of most detectors. The [leaderboard](/ai-detectors/) ranks all seven detectors across every genre.

## Questions people ask

### Can AI detectors tell if a news article was written by AI?

For raw AI output, usually. Every detector except the OpenAI RoBERTa detector caught more than 90% of AI news articles. The trouble is human news, which Fakespot and E5-small LoRA flagged 17 times out of 24 and TMR flagged every time.

### Why does real news get flagged as AI?

A likely reason is that news reports follow a fixed pattern, and AI models copy it closely when asked for a news story. News was Desklib's worst genre, with 5 of 24 human reports flagged, and Fakespot and E5-small LoRA each flagged 70.8%.

### Should an editor run freelance copy through an AI detector?

Fast-DetectGPT is the most useful of the seven for news, but our 24 human reports cannot pin its false positive rate down closely. It could be anywhere from 2.3% to 25.8%, so use a flag only as a reason to ask the writer about the piece.