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

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

> Fast-DetectGPT is far more accurate, with a balanced accuracy of 76.1% against 50.0% for TMR, though its threshold was set on our calibration split. TMR caught more AI text, 83.3% against 68.1%, but it also flagged 83.3% of human texts, against 15.9% for Fast-DetectGPT. TMR's verdicts mostly follow paragraph breaks.

| Measure | Fast-DetectGPT (Qwen2.5-1.5B) | TMR (Target Mining RoBERTa) |
|---|---|---|
| Balanced accuracy | 76.1% | 50.0% |
| Catches AI text | 68.1% | 83.3% |
| Flags human text | 15.9% | 83.3% |
| Catches humanized AI text | 47.6% | 81.0% |
| AUROC | 0.815 | 0.605 |
| Flags human text: Academic abstracts | 40.0% | 16.0% |
| Flags human text: Q&A answers | 21.4% | 100.0% |
| Flags human text: Encyclopedia articles | 8.0% | 100.0% |
| Flags human text: Fiction | 0.0% | 100.0% |
| Flags human text: News reports | 8.3% | 100.0% |

## What line breaks do to each

TMR and Fast-DetectGPT are the only detectors in our test whose catch rate moved much when we removed line breaks and scored every text as a single paragraph. The effect was very different in size.

[TMR](/ai-detectors/tmr/) flagged all 101 human texts that had more than one paragraph. As single paragraphs, it flagged 25.7% of them, and its catch rate on AI text fell from 83.3% to 38.7%. Most of its "AI" verdicts were reactions to the layout.

[Fast-DetectGPT](/ai-detectors/fast-detectgpt/) gave exactly the same verdicts on human text either way, 15.9% flagged. Its catch rate on AI text fell from 68.1% to 57.9%. Fast-DetectGPT reads the text through Qwen2.5-1.5B, and one possible explanation is that the model finds some AI text less predictable once its paragraphs are run together. The [paragraph break guide](/guides/paragraph-breaks-change-ai-detector-results/) has the numbers for all seven detectors.

## Famous texts

The two flagged more public-domain classics than any other detector: TMR 28 of 30 and Fast-DetectGPT 17. TMR passed only two classics, Hamlet's "To be, or not to be" soliloquy and the Federalist No. 10, and those were exactly the two that Fast-DetectGPT flagged and no other detector did. A language model has likely seen famous passages like these many times, which would make them look predictable to Fast-DetectGPT.

## Academic abstracts, the one exception

Abstracts are where TMR comes off better on false positives. It flagged 16.0% of human abstracts and Fast-DetectGPT 40.0%. That is the genre where TMR mostly sees single paragraphs, and its catch rate on AI abstracts was also 16.0%, so it passed most abstracts whoever wrote them. In every other genre, TMR flagged all human texts.

## Choosing between the two

Fast-DetectGPT, without much doubt. It ranked first on our leaderboard and TMR last. Fast-DetectGPT had the advantage of a threshold tuned on our calibration split, while TMR used the 0.5 from its model card, but that cannot explain a gap this wide. TMR's balanced accuracy of 50.0% is what a detector that flags everything would score. TMR's even catch rates across Claude models, from 81.0% to 88.1% for Claude Fable 5.1, are likely a side effect of the same habit. For academic writing, neither detector is safe.

## Questions people ask

### Why does TMR have a higher catch rate than Fast-DetectGPT?

Because it flags almost any text with paragraph breaks, human or AI. With the breaks removed, TMR caught 38.7% of AI texts and Fast-DetectGPT 57.9%.

### Is there anything TMR does better?

It flagged fewer human academic abstracts, 16.0% against 40.0%. But it caught only 16.0% of AI abstracts, against 84.0% for Fast-DetectGPT.

### Which flagged more famous texts?

TMR, with 28 of 30 public-domain classics. Fast-DetectGPT flagged 17, the second highest count of the seven detectors.