Codequiry vs Dolos vs MOSS vs JPlag

Codequiry is the only tool among these four that specifically detects AI-generated code from sources like ChatGPT, GitHub Copilot, Claude, and other AI assistants. This is achieved through machine learning models trained on millions of code samples to identify characteristic patterns of AI-generated code. Dolos, JPlag, and MOSS were not designed for this and cannot reliably detect AI-generated code.

Yes, for comprehensive code integrity in universities, Codequiry offers significant advantages:

• Web and GitHub scanning (JPlag doesn't have this)
• AI-generated code detection (JPlag doesn't have this)
• 51+ programming languages (JPlag supports fewer than 10)
• Cloud-based scalability (JPlag requires self-hosting)
• Side-by-side diff reports with evidence links (JPlag has basic reports)
• Bulk file upload and API access (JPlag requires manual workflow)

That said, JPlag is excellent for basic peer-to-peer similarity checking in research settings or small courses with limited budgets.

Dolos is a research-focused tool with excellent visualization but has limitations compared to Codequiry:

• Dolos supports fewer than 10 languages vs Codequiry's 51+
• Dolos doesn't scan web/GitHub sources
• Dolos lacks AI-generated code detection
• Dolos requires local installation while Codequiry is cloud-based
• Dolos is best for small cohorts (under 100) while Codequiry scales to thousands
• Codequiry offers more comprehensive reporting with side-by-side diffs and source attribution

Choose Dolos if you prioritize visual similarity graphs for small research groups. Choose Codequiry for production use in large courses or institutions.

No, MOSS cannot detect code copied from GitHub or any external web sources. MOSS only compares the source files you submit to it against each other. It has no capability to search the web, scan GitHub repositories, or check Stack Overflow. In contrast, Codequiry automatically scans GitHub repositories and 40 billion+ web sources to detect copied code from online sources. This is one of the most significant limitations of MOSS in today's environment where GitHub and online code repositories are prevalent.

Codequiry supports the most programming languages with 51+ languages including Python, Java, C, C++, C#, JavaScript, TypeScript, PHP, Ruby, Go, Rust, Swift, Kotlin, Scala, Haskell, R, MATLAB, and many more. In comparison, Dolos and JPlag support fewer than 10 languages each, while MOSS supports multiple languages but fewer than Codequiry. This extensive language support makes Codequiry suitable for diverse computer science curricula and enterprise environments with polyglot codebases.

Pricing varies significantly:

• MOSS: Free for academic use but requires manual setup and has very limited features (no web scanning, no AI detection)
• Dolos & JPlag: Open-source and free but require local installation, IT maintenance, and lack enterprise features
• Codequiry: Paid subscription service with plans starting for educators and scaling to enterprise level

While the free tools have no subscription cost, consider the total cost of ownership including: IT staff time for setup/maintenance, opportunity cost of undetected plagiarism (especially from web sources and AI), time spent on manual processes, and potential institutional reputation risk. For larger institutions, Codequiry's comprehensive features often result in lower TCO.

Codequiry uses multi-layer detection combining structural analysis, fingerprinting, AI heuristics, and web scanning, providing higher accuracy for real-world scenarios. MOSS and JPlag use token-based and similarity heuristics respectively, which work well for peer-to-peer comparison but miss external sources and AI-generated code. Codequiry's detection includes: variable renaming, code reordering, structural changes, comment stripping, formatting changes, and advanced obfuscation techniques. Additionally, Codequiry's web scanning catches plagiarism that MOSS/JPlag cannot detect at all (external sources), making it significantly more comprehensive for overall plagiarism detection in modern educational environments.

You only need one tool — choose based on your requirements. Most institutions use either Codequiry (for comprehensive detection including web/AI sources) or MOSS/JPlag/Dolos (for basic peer-to-peer comparison). Using multiple tools simultaneously is unnecessary and creates workflow complexity. Some research projects may use Codequiry for production plagiarism detection while using JPlag or Dolos for academic research on detection algorithms. For 95% of use cases, choose the single tool that best matches your needs and budget.

Codequiry supports bulk file upload through our zip file importer. You can download all student submissions from Canvas, Moodle, Blackboard, or any LMS as a zip file, then upload it to Codequiry for analysis—no file size limits. MOSS, JPlag, and Dolos require manual file handling and typically have more complex upload processes. Codequiry's drag-and-drop bulk importer makes it efficient to handle large classes.