# Fast-DetectGPT accuracy test on 900 texts

Source: https://toolbenchmarks.com/ai-detectors/fast-detectgpt/  
Tested: 30 October 2026. Publisher: Tool Benchmarks. Data licence: CC BY 4.0.

> Fast-DetectGPT caught 68.1% of AI-written texts in our test, the highest catch rate of any detector that passed most human writing. It also flagged 15.9% of human texts as AI, and 40.0% of human academic abstracts, so its verdict on scientific writing is close to a coin toss.

Verdict: The best catch rate among the usable detectors, with a false positive rate that rules it out as evidence, especially for academic writing.

| Measure | Result |
|---|---|
| Catches AI text | 68.1% (343 of 504) |
| Flags human text | 15.9% (20 of 126) |
| Catches humanized AI text | 47.6% |
| AUROC | 0.815 |
| Catches Claude Fable 5.1 | 46.0% |
| Catches Claude Haiku 5.5 | 88.9% |
| Catches Claude Opus 5.5 | 63.5% |
| Catches Claude Sonnet 5.5 | 73.8% |
| Flags human text: Academic abstracts | 40.0% |
| Flags human text: Q&A answers | 21.4% |
| Flags human text: Encyclopedia articles | 8.0% |
| Flags human text: Fiction | 0.0% |
| Flags human text: News reports | 8.3% |

## How Fast-DetectGPT works

Fast-DetectGPT comes from a paper by Bao and colleagues at ICLR 2024. It needs no detector training. A language model reads the text and measures how much more predictable each word is than the words the model would have chosen itself. AI text tends to stick to the model's preferred choices, so it scores high. We used Qwen2.5-1.5B, a model with 1.5 billion parameters, because it runs on a laptop.

The method comes without a threshold for this model, so we chose one on the 144 calibration texts, which are kept out of every reported number. The other detectors used their authors' threshold. That gave Fast-DetectGPT a small advantage, and it still flagged 15.9% of human texts.

## Where it does well and where it fails

It caught Claude Haiku 5.5 best, at 88.9%, and Claude Fable 5.1 worst, at 46.0%. News was its strongest genre: it caught 93.8% of AI news articles and flagged only 8.3% of human ones.

Academic abstracts went the other way. It caught 84.0% of AI abstracts, but it also flagged 40.0% of the human abstracts, all written before ChatGPT existed. Q&A answers were weak in both directions, with 43.8% of AI answers caught and 21.4% of human answers flagged. Fiction was the bright spot for false positives: no human fiction passage was flagged.

## Editing and length

A light rewrite with a humanizer cut its catch rate on Claude Opus 5.5 texts from 63.5% to 47.6%. Text length made little difference. It caught 74.2% of AI texts under 200 words and 69.0% of those with 300 words or more.

It was the slowest detector we tested, at 0.99 seconds per 1,000 words on an Apple M4 Pro, which still means a typical essay takes about a second.

## Questions people ask

### Is Fast-DetectGPT accurate?

Partly. It caught 68.1% of AI texts and flagged 15.9% of human texts in our test, which is among the best results here and still far from reliable.

### Does Fast-DetectGPT need training data?

No. It is a zero-shot method. It asks a language model how predictable each word was and compares that with what the model itself would have written. We ran it with Qwen2.5-1.5B.

### Why does Fast-DetectGPT flag academic abstracts?

Abstracts are written in a dense, predictable register, and predictable wording is exactly what the method treats as a sign of AI. It flagged 10 of 25 human abstracts in our test set.