Try our New Bulk Scan Feature - AVAILABLE NOW!
Scan hundreds of URLs or pieces of content for AI, plagiarism and more in just minutes! Available to all users in our app.
Keyword density helper – This tool comes with a built-in keyword density helper in some ways similar to the likes of SurferSEO or MarketMuse the difference being, ours is free! This feature shows the user the frequency of single or two word keywords in a document, meaning you can easily compare an article you have written against a competitor to see the major differences in keyword densities. This is especially useful for SEO’s who are looking to optimize their blog content for search engines and improve the blog’s visibility.
You can also easily compare text by copying and pasting it into each field, as demonstrated below.
Ease of use
Our text compare tool is created with the user in mind, it is designed to be accessible to everyone. Our tool allows users to upload files or enter a URL to extract text, this along with the lightweight design ensures a seamless experience. The interface is simple and straightforward, making it easy for users to compare text and detect the diff.
Multiple text file format support
Our tool provides support for a variety of different text files and microsoft word formats including pdf file, .docx, .odt, .doc, and .txt, giving users the ability to compare text from different sources with ease. This makes it a great solution for students, bloggers, and publishers who are looking for file comparison in different formats.
Protects intellectual property
Our text comparison tool helps you protect your intellectual property and helps prevent plagiarism. This tool provides an accurate comparison of texts, making it easy to ensure that your work is original and not copied from other sources. Our tool is a valuable resource for anyone looking to maintain the originality of their content.
User Data Privacy
Our text compare tool is secure and protects user data privacy. No data is ever saved to the tool, the users’ text is only scanned and pasted into the tool’s text area. This makes certain that users can use our tool with confidence, knowing their data is safe and secure.
Compatibility
Our text comparison tool is designed to work seamlessly across all size devices, ensuring maximum compatibility no matter your screen size. Whether you are using a large desktop monitor, a small laptop, a tablet or a smartphone, this tool adjusts to your screen size. This means that users can compare texts and detect the diff anywhere without the need for specialized hardware or software. This level of accessibility makes it an ideal solution for students or bloggers who value the originality of their work and need to compare text online anywhere at any time.
The world needs reliable AI detection tools; however, no AI detection tool is ever going to be 100% perfect.
It’s important to understand the limitations of AI detector tools regarding AI detector accuracy, so that you can use them responsibly.
What does this mean for developers of AI detectors? They should be as transparent as possible about the capabilities and limitations of their detectors.
At Originality.ai, we believe that transparency is a top priority.
So, below we’ve included our analysis of Originality.ai’s AI detector efficacy, including accuracy data and false positive rates.
Then, to review third-party data on Originality.ai's AI detector accuracy, see this meta-analysis of multiple academic studies on AI text detection.
Try the patented Originality.ai AI Detector for free today!
This guide aims to answer the question of: What AI content detector is the most accurate?
Additionally, we are proposing a standard for testing AI detector effectiveness and AI detector accuracy, along with the release of an Open Source tool to help increase the transparency and accountability of all AI content detectors.
We hope to achieve this idealistic goal by…
If you have been asked or want to evaluate an AI content detector's potential use case for your organization, this article is for you.
This guide will help you understand AI detectors and their limitations by showing you…
If you have any questions, suggestions, research questions, or potential commercial use cases, please contact us.
Across all tests, Originality.ai has increased its accuracy, further establishing Originality.ai as the most accurate AI checker.
Note: When Lite became the new default in 2024, Standard 2.0.0 and Standard 2.0.1 retired.
Originality.ai offers the most accurate AI detector — so what?
Before diving into our accuracy rates, let’s first review why AI detectors are important — or rather essential — in 2025, starting with a challenge to OpenAI’s stance on AI detection.
In July 2023, OpenAI released an announcement that suggested AI detectors don’t work when it shut down its own detection tool.
So, do AI detectors work? OpenAI Says No.
However, oversimplistic views that “AI detectors are perfect” or “AI detectors don't work” are equally problematic.
We still have an offer to OpenAI (or anyone willing to take us up on it) to back up their claim that AI detectors don't work with proceeds sent to charity. Learn more here.
AI Content Detectors need to be a part of the solution to undetectable AI-generated content.
The current unsupported AI detection accuracy claims and research papers that have tackled this problem are simply not good enough in the face of the societal risks LLM-generated content poses.
Here are some real-life scenarios when AI can pose significant problems:
Not to mention that multiple third-party studies have found that humans struggle to identify AI-generated content.
Then, there are also implications for SEOs and marketers.
AI Content is rising in Google, which presents a number of challenges. So, we created a Live Dashboard to monitor AI in Google Search Results.
Google can detect and does penalize AI content, and it's already happening via manual updates and Google Algorithm updates. Check out our study on Google AI Penalties.
Not to mention that in 2025, Google released updated Search Quality Rater Guidelines stating:
“The Lowest rating applies if all or almost all of the MC on the page (including text, images, audio, videos, etc) is copied, paraphrased, embedded, auto or AI generated, or reposted from other sources with little to no effort, little to no originality, and little to no added value for visitors to the website.” - Source: Google
Claimed accuracy rates with no supporting studies are clearly a problem.
We hope the days of AI detection tools claiming 99%+ accuracy with no data to support it are over. A single number is not good enough in the face of the societal problems AI content can produce, and the important role AI content detectors have to play.
The FTC has come out on multiple occasions to warn against tools claiming AI detection accuracy or unsubstantiated AI efficacy.
In 2025, the FTC addressed misleading accuracy claims from one company offering AI detection without the data to back it up:
“The order settles allegations that Workado [Content at Scale now BrandWell] promoted its AI Content Detector as “98 percent” accurate in detecting whether text was written by AI or human. But independent testing showed the accuracy rate on general-purpose content was just 53 percent, according to the FTC’s administrative complaint. The FTC alleges that Workado violated the FTC Act because the “98 percent” claim was false, misleading, or non-substantiated.” - Source: FTC
“If you’re selling a tool that purports to detect generative AI content, make sure that your claims accurately reflect the tool’s abilities and limitations.” source (page since removed from the FTC)
“you can’t assume perfection from automated detection tools. Please keep that principle in mind when making or seeing claims that a tool can reliably detect if content is AI-generated.” source (page since removed from the FTC)
“Marketers should know that — for FTC enforcement purposes — false or unsubstantiated claims about a product’s efficacy are our bread and butter” source (page since removed from the FTC)
We fully agree with the FTC on this and have provided the tool needed for others to replicate similar accuracy studies for themselves.
The misunderstanding of how to detect AI-generated content has already caused a significant amount of pain, including a professor who incorrectly failed an entire class.
AI detection tools' “accuracy” should be communicated with the same transparency and accountability that we want to see in AI’s development and use. Our hope is that this study will move us all closer to that ideal.
At Originality.ai, we aren’t for or against AI-generated content… but believe in transparency and accountability in its development, use, and detection.
Personally, I don’t want a writer or agency I have hired to create content for my audience and generate it with AI without my knowledge.
Originality.ai helps ensure there is trust in the originality of the content being produced by writers, students, job applicants or journalists.
Which is why transparency and accountability are of the utmost importance.
Pro Tip: Scanning high volumes of content for AI? Check out our Bulk Scan feature.
Along with this study, we are releasing the latest version of our AI content detector. Below is our release history.
1.1 – Nov 2022 BETA (released before Chat-GPT)
1.4 – Apr 2023
2.0 Standard — Aug 2023
3.0 Turbo — Feb 2024
Even easier-to-use Open Source AI detection efficacy research tool released.
2.0.1 Standard (BETA) — July 2024
1.0.0 Lite — July 2024
3.0.1 Turbo — October 2024
Multilingual 2.0.0 — May 2025
1.0.1 Lite — June 2025
Our AI detector works by leveraging supervised learning of a carefully fine-tuned large AI language model.
We use a large language model (LLM) and then feed this model millions of carefully selected records of known AI and known human content. It has learned to recognize patterns between the two.
More details on our AI content detection.
Below is a brief summary of the 3 general approaches that an AI detector (or called in Machine Learning speak a “classifier”) can use to distinguish between AI-generated and human-generated text.
The feature-based approach uses the fact that there can potentially be consistently identifiable and known differences that exist in all text generated by an LLM like ChatGPT when compared to human text. Some of these features that tools look to use are explained below.
Burstiness in text refers to the tendency of certain words to appear in clusters or "bursts" rather than being evenly distributed throughout a document.
AI-generated text can potentially have more predictability (less burstiness) since AI models tend to reuse certain words or phrases more often than a human writer would.
Some tools attempt to identify AI text using burstiness (more burstiness = human, less burstiness = AI).
Perplexity is a measure of how well a probability model predicts the next word. In the context of text analysis, it quantifies the uncertainty of a language model by calculating the likelihood of the model producing a given text.
Lower perplexity means that the model is less surprised by the text, indicating the text was more likely AI-generated. High perplexity scores can indicate human-generated text.
Frequency features refer to the count of how often certain words, phrases, or types of words (like nouns, verbs, etc.) appear in a text. For example, AI generation might overuse certain words, underuse others, or use certain types of words at rates that are inconsistent with human writing. These features might be able to help detect AI-generated text.
Learn about the most commonly used ChatGPT words and phrases, as well as obvious ChatGPT sayings.
Studies have shown that earlier (ie 2019) LLMs would generate text that has similar readability scores.
This pertains to the use and distribution of various punctuation marks in a text. AI-generated text often exhibits correct and potentially predictable use of punctuation.
For instance, it might use certain types of punctuation more often than a human writer would, or it might use punctuation in ways that are grammatically correct but stylistically unusual. By analyzing punctuation patterns, someone might attempt to create a detector that can predict AI-generated content.
A zero-shot approach uses a pre-trained language model to identify text generated by a model similar to itself. Basically, asking itself how likely the content the AI is seeing was generated by a similar version of itself (note: don’t try asking ChatGPT… it doesn’t work like that).
A fine-tuning AI model approach uses a large language model such as BERT or RoBERTa and trains on a set of human and AI-generated text. It learns to identify the differences between the two in order to predict if the content is AI or Original.
The test below looks at the performance of multiple detectors using all of the strategies identified above.
This post covers the main and supporting tests that were all completed on the latest versions of the Originality.ai AI Content Detector.
The dataset(s) provided might be applicable for your use case or potentially if you are evaluating AI detection tools' effectiveness for another type of content you will need to produce your own dataset.
Use our Open-Source Tool to make running your data and evaluating detectors' performance much easier.
To make the running of tests easy, repeatable and accurate, we created and decided to open-source our tools to help others do the same. The main tool allows you to enter the API key for multiple AI content detectors and plug in your own data to then receive not just the results from the tool but also a complete statistical analysis of the detection effectiveness calculations.
This tool makes it incredibly easy for you to run your test content against all AI content detectors that have an available API.
The reason we built and open-sourced this tool to run tests is so that we can increase the transparency into tests by…
The speed at which new LLMs are launching and the speed AI detection is evolving means that accuracy studies, which take 4 months from test to publication, are hopelessly outdated.
Features of This Tool:
Link to GitHub: https://github.com/OriginalityAI/AI-detector-research-tool
In addition to the tool mentioned above, we have provided three additional ways to easily run a dataset through our tool…
We do not believe that AI detection scores alone should be used for academic honesty purposes and disciplinary action.
The rate of false positives (even if low) is still too high to be relied upon for disciplinary action.
Here is a guide we created to help writers or students reduce false positives in AI content detector usage.
Plus, we created a free AI detector Chrome extension to help writers, editors, students, and teachers visualize the creation process and prove originality.
Our newly released Version 1.0.1 Lite model is best for educators and academic settings, as it allows for light AI editing with popular tools like Grammarly (grammar and spelling suggestions).
Learn more about Originality.ai for Education.
Below are the best practices and methods used to evaluate the effectiveness of AI classifiers (i.e., AI content detectors). There is some nerdy data below, but if you are looking for even more info, here is a good primer for evaluating the performance of a classifier.
One single number related to a detector's effectiveness without additional context is useless!
Don’t trust a SINGLE “accuracy” number without additional context.
Here are the metrics we look at to evaluate a detector's efficacy…
The confusion matrix and the F1 (more on it later) together are the most important measures we look at. In one image, you can quickly see the ability of an AI model to correctly identify both Original and AI-generated content.
Identifies AI content correctly x% of the time. True Positive Rate TPR (also known as sensitivity, hit rate or recall).
Identifies human content correctly x% of the time. True Negative Rate TNR (also known as specificity or selectivity).
What % of your predictions were correct? Accuracy alone can provide a misleading number. This is in part why you should be skeptical of AI detectors' claimed “accuracy” numbers if they do not provide additional details for their accuracy numbers. The following metric is what we use, along with our open source tool to measure accuracy.
Combines Recall and Precision to create one measure to rank all detectors, often used when ranking multiple models. It calculates the harmonic mean of precision and sensitivity.
So, what should and should not be considered AI content? As “cyborg” writing combining humans and AI assistants rises, what should and shouldn’t be considered AI content is tricky!
Some studies have made some really strange decisions on what to claim as “ground truth” human or AI-generated content.
In fact, there was one study that used human-written text in multiple languages that was then translated (using AI tools) to English and called it “ground truth” Human content.
Source…
Description of Dataset:
Classifying the AI Translated Dataset (02-MT) as Human-written???
https://arxiv.org/pdf/2306.15666.pdf
We think this approach is crazy!
Our position is that if the effect of putting content into a machine is that the output from that machine is unrecognizable when comparing the two documents, then it should be the aim of an AI detector to identify the output text as AI-generated.
The alternative is that any content could be translated and presented as Original work since it would pass both AI and plagiarism detection.
As the way people write evolves, there is an increased use of AI tools in research and editing.
AI editing is the process of using an AI-powered tool as support to correct grammar, punctuation and spelling.
At Originality.ai, we offer an AI Grammar Checker to help you catch common errors like spelling mistakes, comma splices, or grammatical errors (like confusing when to use they’re vs. their).
However, where things get tricky with AI editing is when a tool offers AI-powered rephrasing features that effectively rewrite sentences for you. For instance, Grammarly, a popular writing tool, offers AI rephrasing that can trigger AI detection.
As AI editing tools become increasingly popular, we’ve set targets to define AI editing:
Here is what we think should and should not be classified as AI-generated content:
*AI Outline is defined as using AI (an LLM) to create a content idea, do some research, and/or create an outline. The level at which AI is used during this process may vary and could potentially affect the likelihood the text is detected as AI or human.
Some journalists, such as Kristi Hines, have done a great job at trying to evaluate what AI content is and whether AI content detectors should be trusted by reviewing several studies - https://www.searchenginejournal.com/should-you-trust-an-ai-detector/491949/.
Review a meta-analysis of AI-detector accuracy studies for further insight into the efficacy of AI-detectors.
In June 2025, we released the updated, more robust Lite 1.0.1 AI detection model.
Why?
The rapid evolution of advanced AI language models, such as OpenAI's GPT series, Anthropic's Claude, and Google's Gemini, can produce increasingly human-like text.
At the same time, "AI Humanizer" tools — designed specifically to obfuscate AI-generated content and evade detection systems — are also increasing in popularity.
In response to these developments, we developed Lite 1.0.1, designed to accurately identify content generated by the latest AI models and humanizer tools, while maintaining a low false positive rate (ensuring that human-written content is not incorrectly flagged as AI-generated).
We evaluate our AI detection model on outputs from various state-of-the-art and widely used language models to assess its robustness across generations of AI systems.
This testing included accuracy evaluations with some of the latest AI models. Here’s a quick overview:
To further test the robustness of our detection model, we evaluate its performance against AI-generated content that has been deliberately modified using AI humanizer tools.
Most AI humanizer tools are designed to paraphrase or rephrase AI-written text in ways that make it more difficult for detection systems to identify. While some AI Humanizer tools like ours are designed to have the content sound more natural, but not to bypass detection.
As part of this testing, we evaluated accuracy on the most popular AI humanizers. Here’s a quick overview:
We evaluated Lite 1.0.1 accuracy across different edit percentage ranges (with percentages of edited characters changing 5% or 5-10% of the text) of the new model on multiple datasets including Grammarly Edits, GPT 4.1 edits and a 3rd party paper “Almost AI, Almost Human”.
The higher the percentage of the edited part, the higher the AI detection ability.
5% AI editing, we aim to call it human (we have a 5% false positive rate at 5% AI editing)
What if 10% of the text is AI-edited? We aim to call it human (we have a 10% false positive rate at 10% AI editing)
Above 10% AI-editing? We aim to call the text AI.
Learn more about AI detection score meaning.
With LLMs rapidly changing and new models being continuously released, we regularly test Turbo 3.0.1 and release new studies.
Quick Summary of Turbo 3.0.1 on the latest AI models:
As with most model releases by leading LLMs (like OpenAI or Anthropic), Originality.ai’s team of machine learning engineers is continuously working to improve our accuracy to 99%+.
Additionally, we also previously ran Originality.ai through tests based on research paper datasets and have shared the results for how Originality performed below.
Each of these datasets comes from a publicly available research paper.
Quick summary of our Turbo 3.0.1 Results across research paper datasets:
You can see the confusion matrix for each of these 5 tests below.
Studies and datasets we chose not to list face similar issues…
Here are additional studies completed by 3rd parties and their findings showing Originality to be the most accurate…
Summaries of these studies: Meta-Analysis of AI Detection Accuracy
The end result?
Across both internal testing and third-party studies, we continue to outperform competitors as the Most Accurate AI Detector.
Below is a list of all AI content detectors and a link to a review of each. For a more thorough comparison of all AI detectors and their features, have a look at this post: Best AI Content Detection Tools
List of Tools:
As these tests have shown, not all tools are created equal! There have been many quickly created tools that simply use a popular Open Source GPT-2 detector.
Below are a few of the main reasons we suspect Originality.ai’s AI detection performance and overall AI detector accuracy are significantly better than alternatives…
The AI/ML team and core product team at Originality.ai have worked relentlessly to build and improve on the most effective AI content detector!
Originality.ai Launches Lite 1.0.1
Originality.ai Launches Version 3.0.1 Turbo
Across multiple third-party studies, Originality.ai’s AI Detector was The Most Accurate Detector.
We hope this post will help you understand more about AI detectors, AI detector accuracy, and give you the tools to complete your own analysis if you want to.
Our hope is that this study has moved us closer to achieving this and that our open-source initiatives will help others to be able to do the same.
If you have any questions on whether Originality.ai would be the right solution for your organization, please contact us.
If you are looking to run your own tests, please contact us. We are always happy to support any study (academic, journalist, or curious mind).
Additionally, to learn more about how Originality.ai performs in third-party academic research and studies, review our meta-analysis of accuracy studies.
Try our AI detector for yourself.
No, that’s one of the benefits, only fill out the areas which you think will be relevant to the prompts you require.
When making the tool we had to make each prompt as general as possible to be able to include every kind of input. Not to worry though ChatGPT is smart and will still understand the prompt.
Originality.ai did a fantastic job on all three prompts, precisely detecting them as AI-written. Additionally, after I checked with actual human-written textual content, it did determine it as 100% human-generated, which is important.
Vahan Petrosyan
searchenginejournal.com
I use this tool most frequently to check for AI content personally. My most frequent use-case is checking content submitted by freelance writers we work with for AI and plagiarism.
Tom Demers
searchengineland.com
After extensive research and testing, we determined Originality.ai to be the most accurate technology.
Rock Content Team
rockcontent.com
Jon Gillham, Founder of Originality.ai came up with a tool to detect whether the content is written by humans or AI tools. It’s built on such technology that can specifically detect content by ChatGPT-3 — by giving you a spam score of 0-100, with an accuracy of 94%.
Felix Rose-Collins
ranktracker.com
ChatGPT lacks empathy and originality. It’s also recognized as AI-generated content most of the time by plagiarism and AI detectors like Originality.ai
Ashley Stahl
forbes.com
Originality.ai Do give them a shot!
Sri Krishna
venturebeat.com
For web publishers, Originality.ai will enable you to scan your content seamlessly, see who has checked it previously, and detect if an AI-powered tool was implored.
Industry Trends
analyticsinsight.net
Tools for conducting a plagiarism check between two documents online are important as it helps to ensure the originality and authenticity of written work. Plagiarism undermines the value of professional and educational institutions, as well as the integrity of the authors who write articles. By checking for plagiarism, you can ensure the work that you produce is original or properly attributed to the original author. This helps prevent the distribution of copied and misrepresented information.
Text comparison is the process of taking two or more pieces of text and comparing them to see if there are any similarities, differences and/or plagiarism. The objective of a text comparison is to see if one of the texts has been copied or paraphrased from another text. This text compare tool for plagiarism check between two documents has been built to help you streamline that process by finding the discrepancies with ease.
Text comparison tools work by analyzing and comparing the contents of two or more text documents to find similarities and differences between them. This is typically done by breaking the texts down into smaller units such as sentences or phrases, and then calculating a similarity score based on the number of identical or nearly identical units. The comparison may be based on the exact wording of the text, or it may take into account synonyms and other variations in language. The results of the comparison are usually presented in the form of a report or visual representation, highlighting the similarities and differences between the texts.
String comparison is a fundamental operation in text comparison tools that involves comparing two sequences of characters to determine if they are identical or not. This comparison can be done at the character level or at a higher level, such as the word or sentence level.
The most basic form of string comparison is the equality test, where the two strings are compared character by character and a Boolean result indicating whether they are equal or not is returned. More sophisticated string comparison algorithms use heuristics and statistical models to determine the similarity between two strings, even if they are not exactly the same. These algorithms often use techniques such as edit distance, which measures the minimum number of operations (such as insertions, deletions, and substitutions) required to transform one string into another.
Another common technique for string comparison is n-gram analysis, where the strings are divided into overlapping sequences of characters (n-grams) and the frequency of each n-gram is compared between the two strings. This allows for a more nuanced comparison that takes into account partial similarities, rather than just exact matches.
String comparison is a crucial component of text comparison tools, as it forms the basis for determining the similarities and differences between texts. The results of the string comparison can then be used to generate a report or visual representation of the similarities and differences between the texts.
Syntax highlighting is a feature of text editors and integrated development environments (IDEs) that helps to visually distinguish different elements of a code or markup language. It does this by coloring different elements of the code, such as keywords, variables, functions, and operators, based on a predefined set of rules.
The purpose of syntax highlighting is to make the code easier to read and understand, by drawing attention to the different elements and their structure. For example, keywords may be colored in a different hue to emphasize their importance, while comments or strings may be colored differently to distinguish them from the code itself. This helps to make the code more readable, reducing the cognitive load of the reader and making it easier to identify potential syntax errors.
With our tool it’s easy, just enter or upload some text, click on the button “Compare text” and the tool will automatically display the diff between the two texts.
Using text comparison tools is much easier, more efficient, and more reliable than proofreading a piece of text by hand. Eliminate the risk of human error by using a tool to detect and display the text difference within seconds.
We have support for the file extensions .pdf, .docx, .odt, .doc and .txt. You can also enter your text or copy and paste text to compare.
There is never any data saved by the tool, when you hit “Upload” we are just scanning the text and pasting it into our text area so with our text compare tool, no data ever enters our servers.
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All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
This table below shows a heat map of features on other sites compared to ours as you can see we almost have greens across the board!
AI & Plagiarism Detector for Serious Content Publishers
(4.3/5 out of 460 reviews)
Originality.ai
64 Hurontario St
Collingwood, Ontario
L9Y 2L6
