One of the best pieces of advice we received when we started was to seek truth in everything we do-whether it’s choosing the right problem to solve, building our product and features, or ensuring the highest code quality in what we offer.
Seeking truth is crucial for solving the right problems and staying on course. It’s easy to get carried away by statistics and early metrics, only to find yourself too far off track to make necessary corrections.
AI has made software development easier than ever. However, this is a double-edged sword-while it accelerates product and feature development, it does the same for your competitors.
If you’re solving a meaningful problem, you’re likely in a competitive market, making it critical to stand out and deliver the best AI code review solutions.
This raised some key questions:
- Where do we stand?
- How effective is our AI code reviewer?
- Does more funding always lead to a better product?
Therefore, we invested time and effort into building a benchmarking framework to identify the best AI-powered code review tools on the market.
We’re open-sourcing everything: our framework, the data used, the comments generated, the prompts applied, and the categorization process, so developers can make informed decisions when selecting the best pull request review solutions.
Benchmark Analysis: Panto AI vs. CodeRabbit
To conduct a fair comparison, we signed up with our competitors and reviewed a set of neutral pull requests (PRs) from the open-source community. Each PR was analyzed independently by both Panto AI and CodeRabbit.
The categorization of comments has been done entirely by the LLMs. These were the categories for the benchmark analysis between Panto AI and CodeRabbit:
Comment Categories
- Critical Bugs: Severe defects causing failures or security risks.
- Refactoring: Suggestions improving code structure or reducing duplication.
- Performance Optimization: Enhancing speed, memory, or scalability.
- Validation: Ensuring logic correctness and edge case coverage.
- Nitpicks: Minor stylistic or formatting issues.
- False Positives: Incorrect flags that waste developer time.
1. Critical Bugs
A severe defect that causes failures, security vulnerabilities, or incorrect behavior, making the code unfit for production. These bugs or issues require immediate attention.
Example: A SQL injection vulnerability.
2. Refactoring
Suggested improvements to code architecture, design, or modularity without changing external behavior. These changes enhance maintainability and reduce technical debt.
Example: Extracting duplicate code into a reusable function.
3. Performance Optimization
Identifying and addressing inefficiencies to improve execution speed, memory usage, or scalability.
Example: Use React.memo(Component) to prevent unnecessary re-renders.
4. Validation
Ensuring the correctness and completeness of the code concerning business logic, functional requirements, and edge cases.
Example: Checking if an API correctly handles invalid input or missing parameters.
Note: While valuable, repeated validation comments can become frustrating when they appear excessively.
5. Nitpick
Minor stylistic or formatting issues that don’t affect functionality but improve readability, maintainability, or consistency.
Example: Indentation, variable naming, and minor syntax preferences.
Note: Engineers often dislike these being pointed out.
6. False Positive
A review comment or automated alert that incorrectly flags an issue when the code is actually correct.
Example: A static analysis tool incorrectly marking a variable as unused.
Note: False positives waste engineers’ time and defeat the purpose of automated code reviews.
Benchmarking Methodology
To conduct a fair comparison:
- We signed up with competitors and reviewed 17 neutral open-source PRs with both Panto AI and CodeRabbit.
- We used OpenAI’s o3-mini API to classify comments instead of humans to reduce subjective bias.
- Tags like “Important,” “Security,” or “Critical” were removed from comments before classification to prevent LLM influence.
By open-sourcing this benchmark, we at Panto AI aim to provide complete transparency and help developers choose the best AI-powered code review tool for their needs. Let the results speak for themselves.
Focus on Critical Bugs
Panto has specialized our agentic workflow to address what’s most important. We don’t do graphs or poems. We are a no-noise, no-fluff PR review agent who focuses on flagging critical bugs. Panto AI is 20% better than CodeRabbit in flagging critical bugs.
Refactoring Made Actionable
Panto has spent a lot of time building agentic workflows to help organizations ship quality code. Refactoring is a crucial part for us, and we help engineers identify key parts of the code to make it suitable for scale. Panto AI is 75% better than CodeRabbit at flagging refactoring suggestions.
Performance Optimization
We have optimized Panto AI to have a lens that ensures the right amount of resources are consumed when code is built for scale. Panto is 5X better than CodeRabbit in performance optimization for the same set of 17 pull requests evaluated.
Filtering Non-Actionable Validation
Code reviews are subjective, and one of the biggest enemies of AI automating code reviews are comments that nudge engineers to “ensure,” or “verify.” These usually aren’t actionable.
Panto has a filter layer that lets these as inline comments only if they are very, very critical in the summary of changes or mostly rejects them. In the sample set considered, Panto was very deterministic and did not provide any “good to have a.k.a validation” suggestions.
Minimizing Nitpicking
The most hated code reviews by engineers, irrespective of whether they are from bots or humans, are nitpicking when it’s not required. Panto is a no-noise, no-fluff code review bot. CodeRabbit provided 3 times more nitpicking comments on the same set of 17 pull requests evaluated.
False Positives
False positive comments are criminal. We do have some scope to improve here, and we are on it. We see CodeRabbit as being well with its filtering engine to not let any false positive comments out. At Panto, we are tying our laces on this to get better.
Speed of Reviews
Speed is a superpower. I’ve never met a dev who said, “I can wait.” We observed that CodeRabbit can take up to 5 minutes to provide comments on a raised PR, whereas Panto AI’s reviews are typically delivered in less than a minute. This may seem like a small difference, but waiting 5 minutes to fix issues can be incredibly frustrating.
Comparison Table: Panto AI vs. CodeRabbit
| Feature / Focus | CodeRabbit | Panto AI |
|---|---|---|
| Critical Bug Detection | Moderate; flagged 44% of test issues | High; flagged 20% more critical bugs than CodeRabbit |
| Refactoring Suggestions | Limited; less proactive | Strong; 75% better at identifying key refactoring opportunities |
| Performance Optimization | Rare; minimal suggestions | Frequent; 5× better at flagging optimizations in tested PRs |
| Validation Comments | Often generates non-actionable suggestions | Filtered; only critical validation flagged |
| Nitpicking / Noise | Higher; 3× more minor style comments | Minimal; no-noise, no-fluff feedback |
| False Positives | Low | Low, actively improving |
| Review Speed | Up to 5 minutes per PR | Typically under 1 minute per PR |
| Integration & Setup | Easy, low configuration | Easy, lightweight, minimal tuning |
| Best-fit Teams | Fast-moving, agile teams needing PR clarity | Teams optimizing for sustainable velocity, maintainability, and high signal |
| Overall Approach | Lean, conversational, developer-friendly | Context-aware, high-signal, actionable, and scalable for larger codebases |
Making Data-Driven Decisions
CodeRabbit and Panto AI are among the strongest AI code review tools for modern development. CodeRabbit provides lean, conversational feedback and faster PR turnaround, while Panto AI delivers high-signal, context-aware insights with superior critical bug detection, refactoring suggestions, and performance optimization.
Choose This Tool If…
Panto AI
- You want high-signal, actionable PR feedback without overwhelming reviewers.
- Your team works across mixed-maturity, multi-language repositories.
- Critical bug detection, refactoring guidance, and performance insights are key KPIs.
- You value fast reviews (<1 minute) and minimal nitpicking.
- You want a tool that supports sustainable velocity and long-term code quality.
CodeRabbit
- You need fast, conversational PR reviews for high-velocity development.
- Your focus is on streamlined collaboration and clear, readable feedback.
- Your team prioritizes low setup overhead and minimal configuration.
- You are fine with slightly lower coverage of critical bugs in exchange for reduced review noise.
- You want a tool optimized for greenfield or rapidly changing repositories.
How Have We Reached Where We Are?
Panto AI focuses on actionable feedback without distractions:
- No graphs, poems, or unnecessary diagrams.
- Optimized agentic workflows for identifying what matters most.
- Deterministic filtering for validation and minor comments.
- Prioritizes speed, delivering reviews in seconds rather than minutes.
If you are a dev who is thinking of having an extra pair of hands to review your code, you know who to choose. If you are an engineering manager or CTO, thinking of getting an AI coding agent to save code review time and effort for your team — Here’s a framework for you to make data-backed decisions.
Appendix
- Open Source Framework
- Open data — Excel Sheet
- Sheet 1: Analysis Summary
- Sheet 2: Data, Repo Links, Comments, Classifications
