ChatGPT, Claude, Gemini, Copilot, Perplexity, Grok, DeepSeek, Meta AI, Le Chat. Grammarly, QuillBot, Jasper, Copy.ai, Writesonic, Rytr, Sudowrite, Notion AI, Wordtune, Anyword. Between them they now write a meaningful share of everything published online. Every one of them is good at producing text. Not one of them will tell you how that text reads once it leaves the tool, which sentences give it away, or whether the draft you are about to submit will survive the person or system reading it next. That is a separate job, and it is the one TextSight does.
The phrase has stretched to cover almost anything that touches text, which makes it useless for choosing a tool. It is more helpful to split the category in two, because the two halves fail in different ways and need checking for different reasons.
These are open-ended chat tools that happen to write exceptionally well. You bring the instruction, they bring the draft. There is no workflow around them, no brand voice memory unless you build it, and no house style unless you paste one in every time. What you get in exchange is range: the same tool that writes your cover letter will restructure a research summary or rewrite a product page.
These wrap a language model in a workflow for one job. A paraphraser that only paraphrases. A marketing tool that knows what a landing page looks like. A grammar layer that lives inside the documents you already write in. They trade range for fit, and for most people that trade is worth it, because the tool sits where the work already happens instead of asking you to leave for a chat window and come back.
Both families are built and measured on the same thing: how good the output text is. Neither is built to answer the question that comes next, which is how that output reads to a marker, an editor, a client, or a detector. That is not a flaw in any individual tool. It is a gap in the category.
These are the tools most drafts now start in. They are listed by what each is genuinely strongest at, rather than by a scoreboard, because the honest answer to "which is best" depends entirely on the job in front of you.
| Assistant | Strongest at | What it leaves you to check |
|---|---|---|
| ChatGPT | The default all-rounder. Long-form structure, rewriting to a brief, and iterating across a conversation. | Whether the finished draft still sounds like you rather than like the model's house voice. |
| Claude | Long documents and careful reasoning. Holds a lot of context and follows detailed style instructions closely. | Fluency can mask a flat, uniform rhythm across a long piece. |
| Gemini | Google's assistant, and the one already sitting inside Docs, Gmail and Workspace for many teams. | Text written inline, in the document, rarely gets a second read before it ships. |
| Microsoft Copilot | Drafting where the work already lives: Word, Outlook, Teams, Windows. | Same as above. Convenience is exactly what removes the review step. |
| Perplexity | Research and answers with sources attached, rather than open-ended drafting. | Summarised source material carries a recognisably machine cadence into your draft. |
| Grok | Conversational, fast, and wired into X for real-time context. | A distinctive register that reads as generated when pasted into formal work. |
| DeepSeek | Strong open-weight models, widely used where cost matters. | Output patterns from any model family still read as patterns. |
| Meta AI | Built on Llama and reachable inside WhatsApp, Instagram and Messenger. | Casual entry points make it easy to paste output straight into something that matters. |
| Mistral Le Chat | Fast European alternative with open-weight models behind it. | Same structural gap: it writes, it does not audit. |
The pattern in that right-hand column is the point. It is the same column for all nine, because the gap is not about any one model's quality. A better model produces better prose. It does not produce prose that tells you about itself.
These are narrower on purpose, and that is their strength. Each one is shaped around a specific job, and if that job is yours, the fit usually beats raw model quality.
| Tool | Built for | Where the check still belongs |
|---|---|---|
| Grammarly | Grammar, clarity and tone inside the apps you already write in, now with generative drafting too. | Clean grammar and natural voice are different things. Text can be flawless and still read as generated. |
| QuillBot | Paraphrasing, summarising, citations. The most-used paraphraser on the web. | Rewording changes vocabulary but often keeps the rhythm a detector reads. See our QuillBot comparison. |
| Jasper | Marketing copy at volume with brand-voice controls. | Brand voice is a template. Templates are exactly the pattern that flags. |
| Copy.ai | Go-to-market and sales copy, workflows over single drafts. | High-volume output means nothing gets read closely before it goes out. |
| Writesonic | SEO articles and long-form built around search briefs. | Publishing at scale is where an unchecked pattern does the most damage. |
| Rytr | Short-form copy on a budget. | Lighter models tend to produce more uniform, more recognisable output. |
| Sudowrite | Fiction and narrative prose, with tools shaped for storytelling. | Voice is the entire product in fiction. It is worth measuring, not assuming. |
| Notion AI | Drafting and summarising inside Notion pages and databases. | Text generated inside a doc tends to ship straight from that doc. |
| Wordtune | Rewriting individual sentences for tone and length. | Sentence-level rewriting without sentence-level measurement is guesswork. |
| Anyword | Marketing copy with predicted performance scores. | Predicted performance says nothing about how the copy reads as writing. |
A few of these do bundle an AI detector into the wider suite, and as a quick gut check that is genuinely useful. What they return is a document-level percentage: one number for the whole piece. That tells you something is wrong somewhere without telling you where, which leaves you rewriting blind.
Ask any assistant on this page whether the passage it just wrote reads as AI-written. You will get an answer. It will be confident, it will be different if you ask again, and it will have been produced by the same kind of model that wrote the text in the first place. That is not a measurement. It is a second opinion from the author.
This matters because the text does not stop when the tool does. It goes to a marker running detection on a submission, an editor who has read ten similar drafts this month, a client paying for a voice they recognise, or a search engine assessing whether a page is worth ranking. Each of those readers is asking a question your writing assistant was never built to answer.
The costs are unevenly distributed but they are all real:
The honest framing: this is not about beating a detector. It is about knowing what you are sending before you send it, and being able to fix the specific lines that misrepresent your work. If your draft reads as templated, the right response is to make it read less templated, not to disguise it.
TextSight is not another writing assistant, and it is not trying to replace the one you use. It does the job that sits immediately after drafting, and it is built around a loop rather than a verdict.
The loop matters more than any single step. A number on its own tells you to worry. A number plus the exact sentences causing it tells you what to do, and a re-scan tells you whether doing it helped. That is the difference between a check and a verdict.
If you want the detail on what the score measures and what it does not, the Authenticity Score page covers the methodology, and the AI detector page covers detection on its own.
These produce whole drafts in one go, which means the pattern is consistent across the entire piece rather than concentrated in a few lines. Do the full draft in your assistant, do your own edit pass first, then scan. Editing before scanning matters: your own revisions are the cheapest way to break up a uniform rhythm, and they leave the rewriter with less to do. Then rewrite the sentences still flagged after your pass, and re-scan.
This is the highest-risk pattern, because the text is generated inside the document that ships. There is no natural moment where you paste it somewhere else and look at it fresh. Build that moment in deliberately: before you send, share or publish, take the near-final document into TextSight. For anything going to a client, an examiner or a public URL, that pause is worth the two minutes.
These operate at sentence level, which pairs unusually well with sentence-level detection. Use them for what they are good at, then use TextSight's highlights to find the lines they smoothed grammatically but left reading as templated. Clean grammar and human rhythm are genuinely different properties, and a tool optimising for the first will not necessarily deliver the second.
Volume is the whole problem here. One generated page is a small risk; two hundred sharing a template is a pattern that a reader or a ranking system will notice. Scan a representative sample rather than every asset, find the structural habits that repeat, and fix them at the brief or prompt level so the next batch starts better. Business tier includes REST API access if you want this inside an existing pipeline.
Voice is not a side-effect of fiction, it is the product. Scan chapters rather than the whole manuscript, and pay attention to whether dialogue and narration flag differently. Uniform rhythm across characters is usually the tell worth fixing, regardless of what any detector says about it.
A page listing nineteen competitors should be equally straight about its own limits, so:
The full methodology is on the accuracy methodology page.
TextSight is priced as an addition to whatever you already pay for drafting, not as a replacement for it.
Full details on the pricing page.
There is no single answer, because they are built for different jobs. General-purpose assistants win on reasoning and long-form structure. Grammarly and Wordtune win on fitting into the documents you already write in. QuillBot wins on paraphrasing and summarising. Jasper, Copy.ai, Writesonic and Anyword win on marketing output at volume. Sudowrite is purpose-made for fiction. The more useful question is what you do with the draft afterwards, because that step is the same regardless of which tool produced it.
You can, and it will answer, but the answer is not a measurement. It is generated by the same kind of model that wrote the text, it is not calibrated against anything, and it will change between runs. A detector is a different kind of tool with a different job.
The detector is tested on major AI writing tools including ChatGPT, Claude, Gemini and Llama 3. Because the purpose-built assistants are themselves built on models from these families, their output tends to carry similar patterns. Accuracy still varies by content type, style and how heavily the text was edited.
No, and we will not claim it. No tool can honestly promise that any text passes any third-party checker. What the rewriter does is improve the specific sentences that read as templated, which is what a careful self-edit does. If you need to disclose AI assistance, disclose it.
That is the intended pairing. Draft wherever you draft. Bring the near-final piece here, read the highlights, fix the flagged lines, re-scan, then ship. You are buying a job your assistant was not built around, not a duplicate of it.
Paste a piece your assistant wrote. See the score, the flagged sentences, and the reason for each. Three scans a day, free, no signup.
Detection, rewriting, and honest comparisons.