Can AI Detectors Tell If You Rewrote AI Content? We Tested the Best Ones

We started with raw AI-generated drafts across four content types: a blog post, a marketing email, a press release, and an opinion piece. We ran each through a baseline detection check, then applied three levels of editing:

Light editing, which covered surface-level changes: swapping synonyms, adjusting a few sentence openings, and fixing obvious AI tells like overused phrases.

Moderate editing, which involved restructuring some paragraphs, adding specific examples or data points, and rewriting transitions.

Heavy editing, which meant rewriting sentence by sentence while keeping the core ideas, adding original perspective, and substantially varying the rhythm throughout.

We then ran each version through the top detection tools and recorded how scores changed at each editing level. The results told a clear story about which detectors are more resistant to editing and which break down quickly.

Light editing barely moves the needle on any of the serious detectors. If you're running AI output through a quick synonym replacement and calling it done, most tools will still catch it. The underlying sentence structures, the predictable paragraph patterns, and the tonal uniformity remain intact regardless of vocabulary changes.

Moderate editing produces more variation across tools. Some detectors showed meaningful score drops after structural changes; others remained stubbornly high even when the text had been substantially reorganized. This is where the quality differences between tools became visible.

Heavy editing produced the most interesting results. All tools showed lower AI-likelihood scores after thorough rewrites, but there was significant variation in how low those scores went. The best tools still showed elevated readings even after genuine rewriting, which is actually a useful feature: it means they're detecting more than just surface patterns.

Here's how each tool performed.

1. Walter Writes AI - Most Reliable on Edited Drafts

Walter’s AI detector outperformed every other tool we tested specifically on the moderate and heavy editing scenarios. The reason comes down to architecture: the team built their detection model alongside humanization tools, which means they trained it to understand the modifications humans make to AI text, not just the patterns in raw AI output.

In practice, this means Walter Writes AI was more likely to maintain elevated readings even after moderate rewrites, which is the behavior you want from a serious detection tool. It wasn't fooled by synonym swaps. It responded to structural changes but didn't collapse entirely after moderate edits. And it maintained meaningful readings even after heavy editing in most cases.

The output is a probability score rather than binary, which is appropriate: after heavy editing, the right answer usually is "this probably started as AI but has been substantially reworked," not a clean pass or a hard flag. Reddit users testing AI detectors have flagged it as one of the most accurate detector available.

Supports 80+ languages, auto-detects input language. API access available. Based in Montreal, Canada, with a stated privacy-first policy.

Website: walterwrites.ai

2. AI Text Detector - Solid on Lightly Edited Content

AI Text Detector handled raw and lightly edited AI content reliably. Under moderate editing conditions, scores dropped more than with Walter Writes AI, suggesting the model responds more to surface-level changes. Still, for a free tool with no account requirement and 50,000-character capacity, it delivered more than expected.

Best use case: quick checks on content that hasn't been heavily revised. If you want to know whether a draft is primarily AI-generated before putting significant editing time into it, this is the fastest way to find out.

Website: aitextdetector.ai

3. Grammarly AI Detector - Consistent on Clean Drafts, Variable on Edits

Grammarly performed reliably on raw AI output and lightly edited content. Under moderate to heavy editing, results became less consistent. Content that had been thoroughly restructured often cleared at scores lower than tools trained specifically for this scenario would show.

This isn't necessarily a flaw for Grammarly's intended use case: it's primarily a writing and editing tool, not a dedicated forensic detection platform. For writers checking polished drafts, the performance is reasonable.

Website: grammarly.com/ai-detector

4. Ahrefs AI Content Detector - Strong for Web Content Formats

Ahrefs performed well on blog and web content formats, which aligns with their platform focus. Detection held up reliably through light editing and partially through moderate edits on article-format content. Performance was less consistent on formats outside the typical web content range.

Website: ahrefs.com/writing-tools/ai-content-detector

5. Quillbot AI Detector - Better as Part of a Revision Workflow

Quillbot's detection is most useful when combined with their editing tools. The ability to detect, revise using Quillbot's paraphraser, and re-check in one session makes the workflow efficient for writers trying to bring a score down. As a standalone detector, performance under editing conditions was moderate.

Website: quillbot.com/ai-content-detector

6. Surfer SEO AI Detector - Reliable for SEO Content

Surfer's detector showed consistent results specifically for SEO blog content. For other content formats, performance was less uniform. For teams already using Surfer, it worked well within its intended scope.

Website: surferseo.com/ai-content-detector/

7. Writesonic AI Content Detector - Best for Writesonic-Generated Content

As expected, Writesonic's detector was most reliable on content generated by Writesonic's own tools. On content from other AI systems, performance was less consistent under editing conditions. The inside knowledge of their own generation model is an advantage that doesn't fully transfer.

Website: writesonic.com/ai-content-detector

8. Undetectable AI Detector - Worth a Spot Check

Undetectable AI offers both a humanizer and a detector, and their detection tool is worth using for a second opinion on borderline cases. We included it as a supplementary check rather than a primary tool. Running content through multiple detectors on important pieces is always more informative than relying on one.

Website: undetectable.ai

If you're using AI to draft content and then editing it, the level of editing matters more than most people assume. Synonym swapping doesn't hide AI origin from serious detectors. What does register is genuine structural revision: rewritten sentences, varied paragraph lengths, added specific examples, and original voice woven throughout.

The tools that hold up best under editing conditions, Walter Writes AI being the clearest example, are built with an understanding of how editing modifies text rather than just how raw AI output looks. These are the ones worth relying on if you want an accurate picture of where your content actually stands.

For detection purposes, the practical takeaway is: if you want to know whether edited AI content will pass a serious check, test it with a tool that was built to handle that scenario. Not all of them were.

For marketers, the cost of publishing content that reads as AI-generated isn't always immediate, but it accumulates. SEO performance tends to lag for content that lacks genuine human perspective and specific expertise. Editorial relationships suffer when publications feel like they're receiving polished AI output instead of original work. Client relationships take hits when the brand voice starts feeling generic.

For writers, the professional stakes are even more direct. Getting flagged by an editor for AI content, accurately or as a false positive, is a difficult conversation to walk back. Running your own check before submission gives you the information you need to either revise or explain your process with confidence.

Detection scores are probabilistic, not definitive. A piece that scores high after editing may still be substantially your own work. A piece that scores low after editing may still be primarily AI-generated. The score reflects statistical patterns, not authorship.

No tool on this list should be treated as proof of anything. They're informational tools that help you understand where your text sits on a spectrum. What you do with that information, and the editorial and ethical decisions that surround it, remains entirely yours.

False positives are real and disproportionately affect non-native English speakers, writers trained in structured academic environments, and those with formal or repetitive writing styles. If you're in one of these groups, calibrate by testing known-human samples before relying on any detection tool.

1. What type of editing most effectively reduces AI-likelihood scores? 

Structural changes make the most difference: rewriting sentence patterns, varying paragraph length, adding specific examples, and inserting original perspective throughout. Surface-level edits like synonym replacement or light rephrasing have minimal impact on serious detectors.

2. Is there a point where AI content is so thoroughly edited it becomes original work? 

That's a philosophical question as much as a technical one. From a detection standpoint, thoroughly rewritten content often reads as human. From an authorship standpoint, whether heavily edited AI output counts as original work depends on context, who's making the judgement, and what standards apply.

3. Do all AI detectors use the same underlying model? 

No. Different tools use different training data, architectures, and thresholds. This is why results vary between detectors and why running a check on multiple tools is more informative than relying on one.

4. How does editing for tone versus editing for structure affect detection scores?

Structural editing typically has more impact. Tone changes that don't alter sentence patterns or paragraph rhythm tend to register less with detectors than actual restructuring.

5. Can I use these results as evidence in a dispute about AI use? 

No. Detection results are probabilistic and tool-dependent. They're not reliable evidence for any formal claim about authorship. Use them for your own quality control, not as proof of anything about anyone's writing process.

6. How do the tools handle text that mixes human and AI writing? 

This varies. Some tools evaluate the whole document holistically and return an overall score. Others may be more sensitive to AI-like sections within a larger piece. The tools trained on realistic editing scenarios, like Walter Writes AI, tend to handle mixed content more consistently.

7. Should I test my tool periodically to see if its accuracy has changed? 

Yes. AI writing tools improve constantly, and detectors need to update their models to keep pace. Periodically running known samples through your preferred tool is the best way to gauge whether it's still calibrated to current AI output.