If you have searched for the best AI code review tool recently, you have probably seen a lot of claims. But as a developer, you know what matters is how it actually performs when run on real code.

That is what this benchmark report is about.

We built a Truth Set of 65 known issues across different severities and ran Bito against it, along with a few other tools. Each agent was scored based on how many real problems it could catch.

The results you see here are based on that snapshot. We keep updating the benchmark as the agents improve. You can always find the latest numbers at bito.ai/benchmarks.

This blog walks through what we tested, how we measured it, how Bito performed, and how this could help your team decide what to try next.

How we measured Bito’s AI code review tool performance

When it comes to finding the best AI code review tool, performance in actual code matters more than feature lists or UI walkthroughs. This benchmarking of AI code reviews focused on how well each tool reviews real pull requests and how useful its feedback actually is.

Benchmarking Criteria: coverage and precision

We looked at two things.

• Coverage: This measures how many of the known issues in the code were flagged by the tool. Higher coverage means the tool picks up more of what would normally get caught in a team review.
• Precision: This tracks how many of those suggestions were accurate and worth applying. It helps separate helpful insights from noisy or incorrect advice.

Language support

Languages included in the benchmark:

• TypeScript
• Python
• JavaScript
• Go
• Java

Each test was built on real examples with real issues. These were bugs, patterns, and mistakes that show up all the time in active codebases.

Every tool was run on the same benchmark. Same codebase. Same issue set. This keeps the results clean and makes it easier to compare what each tool can actually do.

Bito’s AI code review benchmark results

We designed the AI code review benchmarking to evaluate how effectively Bito’s AI Code Review Agent detects real issues in actual codebases.

Each file in the truth set contained known problems, helping benchmark how well each AI code analysis tool handles real-world software issues. These included logic bugs, structural issues, performance bottlenecks, documentation gaps, and concerns affecting code maintainability.

Overall issue detection rate

As I’ve already mentioned, Bito was tested across five programming languages: TypeScript, Python, JavaScript, Go, and Java.

The agent reviewed each file without any prompt tuning or additional configuration. We measured its performance based on the proportion of known issues it correctly identified and the accuracy of its suggestions.

The average coverage across all five languages was 69.5 percent.

 This indicates Bito’s consistent ability to detect a significant portion of real-world issues across diverse codebases.

Comparison with other tools

To contextualize Bito’s performance, we ran the same benchmark on several other tools. Coderabbit achieved an average coverage of 65.8 percent, coming closest to Bito.

Other tools like Entelligence, Graphite, Gemini, Copilot, Qodo, and Codeant showed lower coverage rates and exhibited more variability across different languages.

Detection by issue type

Beyond overall coverage, we analyzed the types of issues Bito detected. Logic and structural issues came off as the most detected issues types compared to other categories.

These types of problems are often more challenging to catch and can lead to significant bugs if overlooked during code reviews.

Severity-based detection

We also assessed how well Bito detected issues based on their severity levels. Across all tested languages, Bito consistently identified over 75 percent of high-severity issues.

Detection rates for medium-severity issues were stable at just over 60 percent. The tool was calibrated to minimize low-severity detections to reduce noise and focus on more critical problems.

Cost efficiency

Manual code reviews are resource-intensive, typically costing between $1,200 and $1,500 per 1,000 lines of code.

Bito’s automated code review reduced this cost to approximately $150 to $300 per 1,000 lines, representing a 75 to 85 percent reduction.

This efficiency gain translates to faster development cycles and smoother CI/CD code review automation, reducing time spent on iterative review processes.

What this means for your engineering team

If you’re running reviews across multiple repos and stacks, you want something that stays accurate no matter what language your team is writing in. That’s what the benchmark shows. Bito stays consistent.

It flags the real issues that actually affect code quality and review velocity. That means fewer back-and-forth. Less context switching. And more time for your team to focus on the kind of review feedback that actually helps.

If you want to see how Bito fits in your workflow, book a demo to talk to our team or run it on your own code like I did.

FAQs About the AI Code Review Benchmark

How was Bito benchmarked?

We ran it on real codebases in five languages. Each file had known issues, and we measured how many it caught and how accurate the feedback was.

How does Bito compare to other tools?

It had the highest average coverage in our tests. CodeRabbit was close, but others missed more issues or didn’t perform consistently across languages.

What types of issues does Bito catch best?

Bito is strongest at catching logic and structural problems. These are the issues that tend to slip through and cause real bugs later.

How much does it save compared to manual reviews?

Manual reviews can cost over $1,000 per 1,000 lines of code. Bito brings that down to a few hundred, with faster turnaround.