# Fakespot vs Fast-DetectGPT, which AI detector is more accurate?

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

> Fast-DetectGPT is the better choice for most readers because it wrongly flags much less human writing, 15.9% of human texts against 34.9% for Fakespot. On AI text the two are close, catching 68.1% and 66.3%. Fakespot is safer only on academic abstracts, where Fast-DetectGPT flagged 40.0% of human ones and Fakespot 12.0%.

| Measure | Fakespot AI text detector | Fast-DetectGPT (Qwen2.5-1.5B) |
|---|---|---|
| Balanced accuracy | 65.7% | 76.1% |
| Catches AI text | 66.3% | 68.1% |
| Flags human text | 34.9% | 15.9% |
| Catches humanized AI text | 47.6% | 47.6% |
| AUROC | 0.741 | 0.815 |
| Flags human text: Academic abstracts | 12.0% | 40.0% |
| Flags human text: Q&A answers | 39.3% | 21.4% |
| Flags human text: Encyclopedia articles | 40.0% | 8.0% |
| Flags human text: Fiction | 12.5% | 0.0% |
| Flags human text: News reports | 70.8% | 8.3% |

## Who gets wrongly flagged

The gap between these two is almost entirely on human writing. Fakespot flagged 44 of our 126 human texts, all published before ChatGPT existed. Fast-DetectGPT flagged 20, and the genres each one trips over mostly differ.

Fakespot's worst genre was news: it called 70.8% of human news reports AI-written, along with 40.0% of encyclopedia articles and 39.3% of Q&A answers. Fast-DetectGPT flagged 8.3% of those news reports and 8.0% of the encyclopedia articles, and it passed every human fiction passage. Its own blind spot is academic writing. It flagged 40.0% of human abstracts, where Fakespot flagged 12.0%. The likely reason is how [Fast-DetectGPT](/ai-detectors/fast-detectgpt/) works: it treats wording that a language model finds predictable as a sign of AI, and abstracts are written in a dense, formulaic style.

## Which Claude models each one catches

Fast-DetectGPT pulled ahead on the models that are easiest to detect, catching 88.9% of Claude Haiku 5.5 texts against 81.0%, and 73.8% of Claude Sonnet 5.5 texts against 69.0%. [Fakespot](/ai-detectors/fakespot/) did a little better on the harder ones, with 65.1% of Claude Opus 5.5 against 63.5%, and 50.0% of Claude Fable 5.1 against 46.0%. After a humanizer rewrite of the Opus texts, both caught 47.6%.

Keep in mind that Fast-DetectGPT's threshold was tuned on our calibration split. Fakespot used the standard cut-off of 0.5, so it had no such help.

## Speed, formatting and famous texts

Fakespot is a RoBERTa-base classifier and ran at 0.07 seconds per 1,000 words on an Apple M4 Pro. Fast-DetectGPT is a zero-shot method that runs a whole language model, Qwen2.5-1.5B, and needed 0.99 seconds, the slowest in the test.

Fast-DetectGPT also reacts to formatting. With line breaks removed it caught 57.9% of AI texts, though its verdicts on human text did not change. Fakespot strips line breaks when it cleans its input, so it gave the same answers either way (see the [paragraph break test](/guides/paragraph-breaks-change-ai-detector-results/)). On 30 famous public-domain texts, Fast-DetectGPT flagged 17 and Fakespot 13. Hamlet's "To be, or not to be" soliloquy was flagged by Fast-DetectGPT and by no other detector.

## Which one to pick

For news, encyclopedia text, forum answers and fiction, Fast-DetectGPT is the safer tool. For academic abstracts Fakespot raises fewer false alarms, but it caught only 33.0% of AI abstracts against 84.0% for Fast-DetectGPT, so neither is much help there. Neither should be the only evidence against a writer. Even the better of the two flagged 15.9% of human texts, and the [false positives page](/false-positives/) shows how those errors add up across detectors.

## Questions people ask

### Is Fakespot or Fast-DetectGPT better at catching AI text?

They are close. Fast-DetectGPT caught 68.1% of AI texts and Fakespot 66.3%. Fast-DetectGPT did better on Claude Haiku 5.5 and Sonnet 5.5, Fakespot slightly better on Claude Opus 5.5 and Fable 5.1.

### Which one flags fewer human texts?

Fast-DetectGPT, with 20 of 126 human texts flagged against 44 of 126 for Fakespot. Academic abstracts are the exception, where Fast-DetectGPT flagged 40.0% and Fakespot 12.0%.

### Was the comparison fair to both?

Fast-DetectGPT had a small advantage. Its method has no published threshold for the model we used, so we set one on our calibration split, while Fakespot used its default cut-off of 0.5.