AI Detector provides online AI text detection services to help educators, recruiters, and content operators identify content generated by models such as ChatGPT.

AIDetector

Core parameters and statistics of AI Detector

AI Detector is an online detection platform for AI-generated text judgment. Users can obtain sentence-level AI probability scores by pasting text or uploading documents. Its product positioning is to help educators, recruiters, content operators, and corporate compliance teams quickly determine whether a piece of text was generated by large models such as ChatGPT, Claude, and Gemini, covering a variety of scenarios from a single quick verification to batch document review.

Projects	Public Information
Official Positioning	AI Content Detector — Online AI-generated content detection
Tool type	AI generated text detection (probabilistic feature analysis type)
Delivery form	Web online tools + API
Detection method	Perplexity + Burstiness + Multi-dimensional language pattern analysis
Detection range	ChatGPT, GPT-5.4, Claude, Gemini, Llama, DeepSeek and other mainstream models
Supported text types	English articles, academic papers, job applications, marketing copywriting, emails, etc.
Analysis method	Full-text scoring + sentence-by-sentence color heat map annotation + confidence percentage
Free plan	Supports free detection with limited word count/number of times
Paid plan	Starter $9/month Pro $29/month Organization $99/month
Served users	500,000+ active users
Text analyzed	5,000,000+ articles
Average scan time	About 3 seconds
Place of Belonging	United States

Detection principle: AI Detector is based on the two statistical characteristics of perplexity and burstiness, combined with multi-dimensional language pattern analysis to determine the source of the text. Perplexity measures the "predictability" of the text - AI-generated text usually has lower perplexity and smoother word order; burstiness measures the sentence length and the magnitude of changes in sentence structure - AI writing tends to have even sentence structure and fewer changes. The combination of the two can cover most pure AI output scenarios.

Accuracy Boundary: The official comprehensive accuracy rate is 99.1%. However, in scenarios where short text (<150 words), a large amount of manual rewriting, or mixed human-machine writing occurs, the confidence in the determination will drop significantly. The output of the detector is a "probability prompt" rather than a "conclusion" and should be used as an auxiliary reference rather than the sole basis for judgment in serious scenarios such as education.

Users and market recognition of AI Detector

Since its launch, AI Detector has accumulated more than 500,000 active users, analyzed more than 5 million texts, and the average scanning time is about 3 seconds. The product has been adopted by educators from many American universities (including MIT, Princeton University, University of Southern California, Yale University, University of Florida, etc.) for academic integrity review scenarios.

User structure distribution:

• Teachers and Educators (50,000+): Use AI Detector as an auxiliary tool to determine whether student homework is directly generated by AI, reducing the proportion of manual inspections that are not covered.
• Students (200,000+): Self-check before submitting assignments to ensure the originality of the content and avoid academic violations caused by unintentional use of AI assistance.
• Content creators and writers (100,000+): Verify the "human touch" of the content before delivering the manuscript to meet some Party A's requirements for purely manual writing.
• Enterprises and Institutions (5,000+): Integrate detection capabilities into the internal content review process for batch screening of marketing materials and compliance documents.

Third Party Approval: The product pricing page integrates the test results of Originality.ai, ZeroGPT, Winston AI, etc. as a cross-reference, indicating that its positioning is not to replace but to supplement the existing testing ecosystem. Currently, no independent third-party benchmark ranking report has been published. Users can evaluate the actual performance in their own scenarios through free trials.

Cost advantage of AI Detector

The cost structure of AI Detector can be split into three levels: C-side individuals, professional users, and enterprises. The billing logic and hidden costs of each level are different.

C-side/individual user

The free quota supports a limited number of words/times for a single test, which is suitable for occasional students or sporadic users. There is no need to register for a single test, just paste the text to get the results, and there is zero installation cost. For individual users whose monthly testing needs are less than 5-10 articles, the free quota is basically sufficient.

Professional Users (Content Creators/Teachers/Recruiters)

The paid plan offers three monthly tiers:

Plans	Monthly Fees	Credit Limit	Cost Per Trip	Core Differences
Starter	$9/month	500 credits/month	$0.0180/credit	Includes plagiarism detection, multi-engine cross-validation
Pro	$29/month	2,000 credits/month	$0.0145/credit	4 times the credit, 19% reduction in single cost
Organization	$99/month	7,500 credits/month	$0.0132/credit	Includes API access, dedicated support, lowest cost per transaction

The credit system is consumed based on the number of words detected, rather than billed per time, so the actual cost of detecting short text is lower than that of long text. The cost per test of the Pro plan is about 19% lower than that of the Starter, and the Organization plan is about 9% lower. For users whose monthly testing volume exceeds 2,000 credits, it is more economical to directly choose the Pro or Org plan.

Enterprise/Institutional Users

The Organization plan ($99/month) includes API access and dedicated technical support, and is suitable for organizations that need to embed detection capabilities into their own workflows (such as LMS, CMS, content moderation systems). For bulk purchases, you can contact sales to obtain a customized quotation. The specific volume and price discounts are not disclosed. Compared with self-built detection solutions, the SaaS subscription model eliminates the costs of model training, data annotation, and ongoing maintenance. However, long-term large-scale use requires attention to vendor lock-in and unit price increases after the credit limit is exceeded.

Hidden Cost Reminder: The credit limit is reset on a monthly basis, and the unused limit will not be accumulated to the next month; in batch detection scenarios, you need to pay attention to the upper limit of the number of documents uploaded in a single time (the Organization plan supports batch upload of up to 250 files), and multiple operations may be required for the excess parts.

Main functions of AI Detector

• AI Text Detection and Probability Score: core function, returns the overall AI generated probability score (percentage) after pasting text or uploading a document, covering the output recognition of mainstream models such as ChatGPT, GPT-5.4, Claude, Gemini, Llama, DeepSeek, etc. Rating results are presented on a 0-100% probability scale, with anything above 70% generally considered highly suspicious of AI generation.

• Sentence-level Highlighting: The detection results are marked sentence by sentence. Each sentence is accompanied by an independent AI probability score, and the color depth is used to indicate the level of confidence. Synergy effect: The overall score can only tell the user "whether the text is suspicious", while the sentence-by-sentence heat map directly locates which specific paragraphs and which expression patterns are judged as AI features, greatly reducing the line-by-line inspection cost of manual review.

• Multi-engine cross-validation: In addition to its own detection model, the results of well-known detectors in the industry such as Originality.ai, ZeroGPT, and Winston AI are integrated as reference comparisons. Synergy effect: A single detector has a judgment bias (some have low false positive rates but high false negative rates, and some have the opposite rate). Multi-engine aggregation is equivalent to providing a "majority ruling" mechanism to reduce the risk of misjudgment by a single engine.

• Plagiarism Check: All paid plans include a plagiarism detection function that compares uploaded text with public online resources and marks possible unmarked quoted passages. Synergy effect: Running in parallel with AI detection, one upload can simultaneously obtain evaluations in two dimensions: "AI generation probability" and "originality matching degree", covering the two main violations of academic integrity.

• Batch scanning and document export: The paid plan supports batch uploading of multiple documents (Pro plan has a limited amount, Organization plan has a maximum of 250 files). After the detection is completed, a detailed PDF report can be exported, including sentence-by-sentence scoring, confidence distribution and multi-engine comparison results. It is suitable for time-window-type high-load scenarios such as the final paper review season and batch resume screening.

• API integration: The Organization plan provides API access, allowing enterprises to embed detection capabilities into their own platforms or workflows. Detection results are returned through JSON structure, supporting automated post-processing and alarm triggering.

Model and version evolution of AI Detector

As an online SaaS product, AI Detector's detection model continues to iterate in the background, and the front-end version does not change significantly. The following is a version overview based on product public information:

Early Exploration Phase (~2024)

• AI Detector Beta: An early version of the product, which provides basic AI text judgment capabilities, supports single paste detection, and returns an overall probability score. At this time, the detection model is mainly trained on ChatGPT (GPT-3.5/GPT-4) output, and has limited coverage of other models.

Functional improvement stage (2025)

• Introducing the sentence-by-sentence heat map annotation function, upgrading from "entire article scoring" to "sentence-by-sentence positioning", qualitatively improving the detection accuracy and user interaction experience.
• Expand the coverage of detection models and increase the recognition capabilities of Claude, Gemini, Llama and other models.
• Launched multi-engine cross-validation function, integrating Originality.ai, ZeroGPT, and Winston AI as reference controls.
• Launched a three-level pricing system of Starter/Pro/Organization to clarify the boundary between free and paid.

Continuous operation phase (2026)

• The current online version continuously updates the detection model training data to adapt to changes in the output modes of new models such as GPT-5.4, Claude Sonnet 4, etc.
• Optimize the batch scanning user experience, support more file formats and larger single batch processing volume.
• Launched API access capabilities (Organization plan) and opened integration capabilities to enterprise customers.

Version Observation: AI Detector does not release a "version number" in the traditional sense, but achieves the continuous evolution of detection capabilities through hot updates of back-end models. This means that users can obtain the latest model without manual upgrades, but it also brings a hidden cost - it is impossible to confirm "whether the detection results at a certain point in time are reproducible" through a fixed version number like traditional software, which may become a problem in compliance scenarios that require evidence preservation.

Technical advantages of AI Detector

Detection mechanism: from statistical features to multi-dimensional pattern analysis

AI Detector's detection model is based on two classic statistical features, but is not limited to this:

• Perplexity: Measures the "surprise" of the model to the text. The perplexity of AI-generated text is usually lower than that of human writing because large language models tend to select word sequences with the highest probability, and the overall trend is smoother. Human writing will have more "unexpected" word choices, resulting in higher confusion and greater fluctuations.
• Burstiness: Measures the variability of sentence length and sentence structure. AI output usually has a uniform sentence structure - the distribution of long and short sentences, the frequency of clause use, and the length of paragraphs are relatively stable. Human writing naturally has greater fluctuations in sentence structure - some paragraphs are densely packed with short sentences, and some paragraphs contain long, complex sentences.
• Multi-dimensional language pattern analysis: In addition to statistical features, the model will also analyze hundreds of micro-features such as vocabulary diversity (type-token ratio), repetition pattern frequency, transition word usage distribution, paragraph structure rules, etc., to comprehensively generate a final probability score.

Why this combination works: Pure AI-generated text has regular deviations in statistical features that are not obvious to the human eye but are strong signals to statistical models. Perplexity and suddenness form the first line of defense, and multi-dimensional analysis provides a higher degree of distinction in mixed human-machine texts.

Architectural advantages: lightweight SaaS delivery

AI Detector adopts a pure web architecture. Users do not need to install any software and can use it just by opening the browser. The direct benefits of this are:

• Zero Deployment Cost: Educational institutions require no IT department involvement, free accounts for teachers and students to get started.
• Cross-platform compatibility: Accessible from any device that can run a browser (Windows, macOS, Linux, Chromebook).
• Continuous Update: Users benefit immediately after the backend model is iterated, without the need to manually upgrade the client.

Multi-engine architecture: Reduce single point misjudgment

In addition to its own model, AI Detector integrates the results of multiple detection engines such as Originality.ai, ZeroGPT, and Winston AI. The engineering value of this "multi-model voting" architecture lies in the fact that different detectors may judge the same text differently - some prefer low false positives (prefer false negatives to false positives), and some prefer high recall (prefer false positives to false positives). Referring to multiple results simultaneously allows users to make a more comprehensive judgment on the credibility of the determination, rather than blindly trusting a single number.

Engineering pitfall guide (for API integrators)

1. Credit Quota Management: API calls consume credit. Quota monitoring and over-limit alarm logic need to be implemented in the code to prevent accidental over-consumption caused by a large number of calls. It is recommended to trigger an alarm when 20% of the credit limit remains, and to pause batch tasks before the credit limit is exhausted.
2. Concurrency and rate limit: The API of the Organization solution has frequency control restrictions (the specific RPM is not disclosed). When submitting large batches of documents, queued submission and exponential backoff retry mechanisms need to be implemented to avoid being temporarily banned due to overclocking.
3. Result Reproducibility: The continuous hot update of the back-end model means that the same text may receive different scores when detected at different time points. For scenarios that require evidence preservation (such as academic violation appeal files), it is recommended to save both the timestamp of the returned results and the product version identifier (such as the build ID of the page footer) during detection, rather than just saving the score itself.

How to use AI Detector

AI Detector provides two entrances, Web and API, respectively facing different usage scenarios.

Web use

Complete detection in three steps:

1. Input text: Open https://aidetector.com, paste the content to be detected in the text box (supports direct input within 5,000 words), or select "Analyze Docs" to upload the document file. You can also directly enter the web link for analysis through "Analyze URL".
2. Select detection mode: Run AI detection by default. You can optionally check "Plagiarism Check" to run plagiarism detection at the same time. Click the "Detect AI" button to start analysis.
3. View results: After waiting for about 3 seconds, the page returns the overall AI probability score, sentence-by-sentence color heat map, and confidence distribution. You can click "Export" to export the complete inspection report in PDF format.

Free quota description: Unregistered users can perform a limited number of free tests; after registering a free account, you can obtain more monthly testing quotas. Specific amount value.

API integration (Organization plan)

Organization paid plans include API access. Typical API calls are as follows:

curl -X POST https://api.aidetector.com/v1/detect \
  -H "Authorization: Bearer <YOUR_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Your content to analyze here...",
    "include_plagiarism": true,
    "model": "default"
  }'

The results are returned in JSON format, including the overall score, sentence-by-sentence analysis, and independent results for each engine. The specific endpoint URL, request parameters and frequency control limits are subject to the official API documentation.

Browser extension

AI Detector provides a Chrome browser extension that can directly call the detection function when browsing any web page without switching to the main website. The extension supports Google Docs integration to view AI detection results directly in the document editing interface.

Product Pricing for AI Detector

AI Detector uses a "free trial + monthly subscription" pricing model, with all prices in USD and paid through Stripe.

Plans	Monthly Fees	Credit Limit	Cost Per Trip	Key Features
Free	$0	Limited limit	—	Basic AI detection, limited number of words/times
Starter	$9/month	500 credits	$0.0180/credit	AI detection + plagiarism detection + multi-engine cross-validation + sentence-by-sentence analysis + batch scanning
Pro	$29/month	2,000 credits	$0.0145/credit	4x Starter credit, 19% reduction in single cost
Organization	$99/month	7,500 credits	$0.0132/credit	Includes API access, dedicated technical support, lowest single cost

Credit limit description: Credit is consumed based on the number of words detected, not the number of tests. Short texts (<500 words) cost less credits, longer texts cost more. The specific consumption ratio is subject to the official pricing page. The quota is reset monthly, and the unused portion will not be accumulated.

The real boundary of paid vs free: The monthly quota for free users is suitable for occasional testing (expected to be about 3-5 standard-length English papers). After exceeding it, you need to wait for the next month to reset or upgrade the paid plan. It is more cost-effective for high-frequency users (such as teachers or content reviewers who check 10+ articles per week) to directly choose the Pro plan. Enterprise-level batch use (such as institutional procurement of testing services embedded in internal systems) should choose the Organization plan or contact sales to obtain a customized quote.

Refund and Cancellation: The subscription plan supports cancellation at any time and will not be charged in the next billing cycle. The specific refund terms are subject to the official website Terms of Use.

Application scenarios of AI Detector

• Education Academic Integrity Review: Teachers check whether papers, assignments, and experimental reports submitted by students are directly generated by AI. Key points of verification: Pay attention to the false positive rate - some original content (especially non-native writing by ESL students and science and engineering papers with regular sentence patterns) may be misjudged as generated by AI. It is recommended to make a comprehensive judgment based on the writing process records (Google Docs version history, writing process replays, etc.) instead of just relying on the test scores to make a qualitative decision.

• Recruitment Resume Screening: HR detects whether application documents such as cover letters, personal statements, portfolio descriptions, etc. are generated in batches by AI. Realistic Benefits: Reducing the initial screening from completely manual reading to "AI marking + manual key review" is expected to reduce the time spent on resume screening by 40-60% (deduced value, subject to actual scenarios). It should be noted that some job seekers may use AI-assisted wording optimization but still retain the core personal experience. The detection and determination of such mixed text should be used as a signal that "further interview verification is required" rather than a direct basis for elimination.

• Content Platform Submission Review: Self-media platforms and corporate content teams review whether submissions or outsourced manuscripts are generated using AI but have not been declared. Operation process: Run AI detection and plagiarism detection in batches before content is published, and mark manuscripts with AI probability scores exceeding the threshold (e.g. >80%) as "requiring manual review of sources". Long-term operation can accumulate the "writing fingerprint" baseline of a specific writer, helping to distinguish between two different levels of AI usage behavior: "habitual use of AI-assisted polishing of language" and "completely generated by AI".

• Corporate Compliance and Brand Risk Control: The corporate legal affairs or brand team detects whether there is undisclosed AI-generated content in public statements, press releases, and white papers released to the outside world. Some industry regulators have begun requiring companies to disclose the proportion of AI-generated marketing content, and AI Detector can be used as one of the tools for internal self-examination.

• Academic Research Assistance: Researchers detect the presence of unclaimed AI-generated content in published papers or preprints for meta-research or publication integrity investigations. This scenario requires special attention to the false positive rate of the detector for text dense with professional terms. It is recommended to conduct calibration tests with text samples representative of the field before formal use.

Applicable groups of AI Detector

• Teachers and Education Managers: Need to batch detect the AI components of student assignments, papers, and application documents. Adaptation value: The sentence-by-sentence heat map helps locate specific paragraphs that are judged to be AI-generated, so that targeted discussions can be conducted with students. Unfit Boundary: It cannot replace interviews or writing process reviews; for ESL students or students with regular writing styles, the false positive rate may be high and additional manual review is required.

• Students (High School/College/Graduate Students): Self-test before submitting assignments to ensure originality of content. Adaptation value: The free quota is sufficient for occasional detection, the threshold for use is low, and the results are instant. Unfit Boundary: It cannot be used as a pass to "guarantee not to be accused of AI plagiarism" - the detector has a false negative rate, and fully AI-generated text may also be judged as "possibly human".

• Recruiters and HR teams: Initial screening of job applications for content that may be batch-generated by AI. Adaptation value: The batch upload function supports processing dozens of job application documents at one time, improving the initial screening efficiency from full manual to "AI marking + key review". Unfit Boundary: The boundary between AI-assisted wording optimization and full AI generation is blurred, and test results should not be used as the only screening criteria; it is recommended to make a comprehensive judgment based on structured interviews and work samples.

• Content Creators and Freelance Writers: Verify the "human touch" of your manuscript before delivery. Adaptation value: Multi-engine cross-validation provides a more comprehensive reference for judgment, and the exported report can be used as a delivery certificate. Misfit Boundary: Agreed standards with parties that require "100% human writing" should be defined by negotiation between the two parties, rather than determined by a single detector.

• Enterprise Content Review and Compliance Team: Batch review of AI-generated content in marketing materials, public statements, and compliance documents. Adaptation Value: API integration embeds detection capabilities into existing content management systems (CMS) or document approval processes. Not suitable for the boundary: Security compliance certifications such as SOC2/GDPR are not disclosed. Enterprises involving highly sensitive or regulated content need to assess data privacy risks by themselves; back-end storage and transmission of test results require additional confirmation of encryption measures.

• Not applicable: Users who need to detect non-English texts in depth (AI Detector has the highest detection accuracy for English, but the coverage and accuracy of other languages ​​are obviously insufficient); users who need to detect long multi-format documents (such as scanned PDFs/text in pictures) in real time (currently, it mainly supports directly pasting text and uploading standard document formats, and does not support OCR recognition).

Summary and Outlook

AI Detector has established a clear product positioning in the AI ​​content detection market with a claimed accuracy of 99.1% in 3 seconds for rapid detection and sentence-by-sentence heat map annotation - lightweight, fast, multi-engine cross-validation. For three typical scenarios: education integrity review, recruitment resume preliminary screening and content submission review, it provides flexible options from free to $99/month, covering the full spectrum of needs from individual scattered use to enterprise batch integration.

Current Limitations and Uncertainties:

1. Accuracy ceiling: A common limitation of all AI detectors - the detection confidence of short text, manually rewritten text, and human-machine hybrid text is insufficient, and the trade-off between false positive rate and false negative rate cannot be completely eliminated. AI Detector's 99.1% accuracy is typically for medium- to long-length, purely AI-generated standard text test sets, and performance in real-world scenarios may be even lower.
2. English priority limitation: The detection model is mainly optimized for English text, and the detection accuracy for other languages ​​is significantly lower than English. Users with multilingual requirements are currently not the target audience for this product.
3. Insufficient model transparency: Details such as the specific architecture of the detection model, the scale of training data, and the independent accuracy of each engine are not disclosed, making it difficult for users to independently verify the claimed accuracy from a technical level.
4. Compliance certification is not disclosed: When facing enterprise customers, the status of security compliance certifications such as SOC2, ISO 27001, and GDPR is not disclosed, which may become an obstacle to procurement in highly regulated industries such as finance, medical, and government.
5. Non-English and missing OCR: The detection of text in images (scans, screenshots) is not supported, limiting the applicability in some workflows.

Procurement/Adoption Risk Assessment: It is recommended to use the free plan to conduct batch actual testing of 50-100 articles on typical texts in your own scenarios, count the false positive rate and false negative rate, and confirm its performance on actual business data instead of just looking at official claims. If the false positive rate exceeds the acceptable threshold (such as >5%), you need to consider using multiple detectors (such as Originality.ai, GPTZero, Winston AI) for cross-determination, or return to the manual sampling plan. Before enterprise-level procurement, you need to confirm compliance terms with sales such as data storage location, transmission encryption, and whether model training data contains text submitted by customers. For long-term use, you need to pay attention to credit line pricing changes and vendor lock-in risks - historical comparative analysis of test result data. If you are deeply tied to a single supplier, replacement costs will increase over time.

Related tools: originality-ai, gptzero

Version Info

• AI Detector Online ：There is no official precise date yet. Continuously updated online testing services. （~2026-07）
• AI Detector Beta ：There is no official precise date yet. The early version provides basic AI text judgment. （~2024-01）