By Alex Mercer · Updated
AI code review
Say a pull request adds a CSV export of a workspace’s invoices. Tests pass, and the route looks like its neighbors:
1router.get("/:workspaceId/invoices.csv", requireUser, async (req, res) => {2 const rows = await Invoice.findAll({ where: { workspaceId: req.params.workspaceId } });3 res.type("text/csv").send(toCsv(rows));4});
requireUser confirms the caller is signed in, not that they belong to this workspace. The other routes in this file also call requireMember. Without it, anyone with an account can download another company’s invoices. Add requireMember after requireUser.That’s AI code review: a reviewer built on large language models that reads every pull request in the context of your codebase and comments on bugs, security holes and broken team rules before the code merges.
What is AI code review?
AI code review is the use of AI models, usually large language models, to review code changes before they merge. An AI code reviewer runs on each pull request, reads the diff along with the code around it, and comments on the lines that look wrong, usually with a suggested fix.
Most AI code reviewers install as an app on your Git host and review every pull request automatically. Many also run in a terminal or editor, so you can check a branch before you push it.
A linter works differently. It checks code against fixed rules, like formatting or a banned function. The invoice route above passes every lint rule, because nothing in it is wrong on its own. It’s wrong because of what the other routes do, and reading that context is what an AI reviewer is for.
Human review is different again. An AI reviewer doesn’t know why the change exists or what your team agreed last week. It takes the first pass, so the people reviewing can spend their attention on intent and design.
Teams are adding it because coding agents now write more code than people can read closely. In March 2026, Anthropic said code output per engineer there had grown 200% in a year, and that code review had become a bottleneck. AI code review is how teams make sure every change still gets a careful read.
How AI code review works
The steps are much the same across tools. How well a tool handles steps 2 and 4 decides whether its comments are worth reading. Our deep dive explains why most AI reviewers are noisy.
- 01A pull request opensYour Git host notifies the reviewer, and usually notifies it again when new commits land.
- 02It gathers contextThe diff, the full files it touches, code that calls the changed code, rule files such as
AGENTS.md, and often the linked ticket. - 03It looks for problemsOne or more model passes read the change for bugs, security issues and breaks from your rules. Many tools split the work across agents, and some search the codebase or run commands as they go.
- 04It checks its findingsGood reviewers try to confirm each finding against the code and drop the ones they can’t support. This step decides how much noise reaches you.
- 05It commentsInline, on the line that’s wrong: what breaks, why, and a suggested fix. Most tools also summarize the pull request.
- 06It learnsFrom your replies and reactions, from which comments get fixed, and from rules you write in plain English.
What AI code review catches
The catches worth having look fine line by line and are wrong in context. Tests often miss them, because nobody thought to test that case. Cloudflare’s AI-built vinext had more than 1,700 tests ported from Next.js and still shipped an auth bypass in production builds, which we found with a codebase scan and fixed upstream.The examples are illustrative. For security in depth, see secure code review; for rules by language and framework, the code review standards.
Logic bugs
offset = page * perPageskips the first page, because the client sendspage=1for page one.Security holes
A download route joins the requested filename onto the uploads folder, so
../../.envreturns your secrets.Breaking changes
A pull request renames
user.nametouser.fullName, and two background jobs in another folder still readuser.name.Error handling
A
catchblock logs a failed payment and returns 200, so the order ships unpaid.Race conditions
Two requests read the same coupon balance before either writes it back, so a double-click spends the coupon twice.
Performance
A page of 500 orders runs 501 queries, because each row fetches its customer on its own.
Tests that don’t test
A new test mocks the function it’s meant to check, so it passes whatever the code does.
Your team’s rules
Your rule says money is stored in integer cents, and the reviewer flags a new
price FLOATcolumn.
Here’s a real one. On n8n’s public repository, our reviewer flagged a pull request that wrote a credential’s custom header into a configuration object shared across all OpenAI requests, so the header would have gone out with every later request, a cross-user credential leak. The author fixed it two hours later, before the pull request merged. See the comment on GitHub, or the example on cubic.dev.
What AI code review can’t do
An AI reviewer reads code well and knows only what it can see. Plan around these gaps.
Judge intent
It can spot the missing access check, but it can’t tell you whether the export should exist or matches what the customer asked for. Reading the linked ticket helps; a ticket rarely holds the whole intent.
Make design calls
A queue or a cron job, a new table or a JSON column: it can lay out the consequences, and someone on your team has to own the trade-off.
Know what nobody wrote down
It doesn’t know that a table holds 40 million rows in production, or that a partner still calls the old endpoint. If it isn’t in the code, the docs or the ticket, the reviewer is guessing, so write the important rules where it can read them.
Prove the code works
Most reviewers read code without running it. Keep your tests, and treat a clean review as one signal among several.
Be right every time
Models write wrong comments that sound plausible. A weak tool posts lots of them, and a team that learns to skim its reviewer misses the comment that mattered. That’s why false positives are the first thing to test.
Understand the code for you
A clean review doesn’t mean anyone on your team understands the change, and approving it is still a person’s call. With AI-written code that gap grows fast, a problem we’ve written about as comprehension debt.
AI code review vs manual review vs static analysis
Use all three: static analysis for style and known patterns, AI code review for bugs on every pull request, and people for intent and design.
| Manual review | Static analysis | AI code review | |
|---|---|---|---|
| What it reads | The diff, plus what the reviewer remembers | Code, checked against fixed rules | The diff, related code, your docs and rules, often the ticket |
| Speed | Hours to days | Seconds | Minutes |
| Logic bugs | When the reviewer has time and context | Rarely | Yes, including ones that span files |
| Security issues | Depends on the reviewer | Known patterns, like injection and leaked secrets | Known patterns, plus ones specific to your app, like a missing access check |
| Intent and design | Yes | No | Partly, from the ticket and context |
| Consistency | Varies by reviewer and day | Same result every run | Applies your rules every run, though wording can vary |
| Noise | Low | High until tuned | Low in good tools, high in weak ones |
How to tell a good AI code reviewer from a noisy one
Give a new reviewer a week, then read its comments the way you’d read a new teammate’s. Look for these signs.
It names a failure, not a worry
Useful: “If
itemsis empty,totalisundefinedand checkout shows NaN.” Noisy: “Consider adding error handling here.”It shows its evidence
It points to the caller, rule or schema that makes the line wrong, so you can check the claim in seconds.
It stays quiet on clean pull requests
A reviewer that always finds something teaches your team to skip its comments, the good ones included.
It leaves style to your linter
Formatting and naming nits belong in CI, which catches them the same way every time.
It learns
Reply “this is intentional” once and it stops repeating the comment. It follows the rules you write in plain English.
Then count. Run your two best candidates on the same two weeks of pull requests and tally the comments that led to a code change. That number tells you more than any demo.
What the independent benchmark says
For an outside view, Martian’s Code Review Bench scores AI reviewers on real open-source pull requests, matching each comment against what the developer went on to change. It ranks them by F1, which balances how many real issues a reviewer catches against how many of its comments are wrong. The top five in its last-month view, checked Oct 10, 2026 (17,505 scored pull requests, 15 tools):
| Rank | Tool | F1 score |
|---|---|---|
| #1 | cubic | 64.0% |
| #2 | Kody AI | 63.4% |
| #3 | Greptile | 63.2% |
| #4 | CodeRabbit | 61.8% |
| #5 | Claude | 60.9% |
The top three are within a point of each other, and the board moves daily. Martian’s own methodology warns that its live data can’t settle head-to-head comparisons: bots on the same repository see each other’s comments, and different kinds of projects pick different tools. Use it to build a shortlist, then test on your own code.
This page belongs to cubic (cubic.dev), the AI code review tool at #1 on that board, so weigh our view accordingly. The full ranking, with prices and Git hosts for each tool, is in our list of the best AI code review tools.
The best AI code review tools in 2026
On Martian’s board the top five sit within about three F1 points, so choose on fit: which Git hosts a tool supports, how it bills, and where it runs. Prices are as of October 2026, from each vendor’s pricing page.
cubic
#1 on MartianOur product. Reads each pull request with context from your repository, docs and tickets, learns from your senior reviewers’ past comments, and, on Pro and Max, fixes issues when you ask. GitHub only. Free for public repositories; paid plans from $30 per developer a month billed yearly.
Kody AI
#2 on MartianKodus’s open-source (AGPL) reviewer, listed as Kody AI on Martian’s board, for GitHub, GitLab, Bitbucket, Azure DevOps, Gitea and Forgejo. Self-host it for free, or use the hosted Teams plan at $8 per developer a month billed yearly, plus your own model tokens.
Greptile
#3 on MartianBuilds a graph of your repository and reviews each pull request against it. Runs on GitHub, GitLab and Bitbucket in its cloud, with self-hosting on Enterprise. $30 per seat a month includes 50 credits; a Base review costs one credit, a Plus or Apex review three or ten, and each extra credit costs $1.
CodeRabbit
#4 on MartianRuns dozens of linters and security scanners alongside its AI review, on GitHub, GitLab, Bitbucket and Azure DevOps. From $24 per developer a month billed yearly, and free for public repositories.
Claude
#5 on MartianAnthropic’s GitHub reviewer. Its managed Code Review, a research preview on Team and Enterprise plans, averages $15-25 per review, billed as usage. For most teams that isn’t worth paying for; run Claude Code’s local
/code-reviewbefore you push instead.
cubic reviews GitHub only. On GitLab or Bitbucket, start with Kodus, Greptile or CodeRabbit; on Azure DevOps, Kodus or CodeRabbit. Our full ranked list covers more tools, and our head-to-head pages compare cubic with CodeRabbit, GitHub Copilot, Cursor Bugbot, Qodo, Codacy and Sourcery.
How to roll out AI code review
Treat a new AI reviewer like a new teammate on probation. Start its rules from the code review standards for your stack.
- 01Install it on two or three busy repositories, not the whole organization.
- 02Run it next to your human reviewers for two weeks, without changing who approves what.
- 03Write down the rules your senior reviewers keep repeating, in plain English, where the tool can read them.
- 04Reply when a comment is wrong. Good tools learn from replies and reactions.
- 05After two weeks, check how many of its comments led to a code change, and compare time to first review and time to merge with the two weeks before.
- 06Roll it out everywhere once those numbers hold up. Decide later whether it may approve low-risk pull requests on its own.
More on AI code review
Guides
Tool reviews
Used by teams that can’t afford bugs
“I’ve been a dev for 13+ years and I’m still routinely humbled by what cubic catches. We’ve tried other tools but cubic is way better—there’s no comparison.”
- Browser Use85% faster PR mergesAverage time to merge is now about three hours.Read the story →
- Cartography76% faster PR mergesA Linux Foundation project, now merging in about 10.5 hours on average.Read the story →
- Firecrawl70% less time on manual reviewsThe team reviews code 3x faster with cubic.Read the story →
Also used by n8n, Cal.com, Resend, Granola, Legora, Daytona, Tinfoil, and Better Auth. All customer stories
Questions and answers
- What is AI code review?
- AI code review is the use of AI models, usually large language models, to review code changes before they merge. An AI code reviewer reads each pull request with the code around it and comments on bugs, security issues and breaks from your team’s rules, usually with a suggested fix.
- How is AI code review different from a linter?
- A linter checks code against fixed rules and gives the same answer every run. AI code review reads what the code does in the context of your codebase, so it catches logic bugs and missing access checks that pass every lint rule. Keep both.
- Is AI code review accurate?
- The good tools are useful, and none is perfect. On Martian’s Code Review Bench (last-month view, checked Oct 10, 2026), F1 scores ran from 64.0% for the top tool, cubic, to 48.7% for the tool ranked #15. F1 balances how many real issues a reviewer catches against how many of its comments are wrong. Test any tool on your own pull requests before you trust it.
- Can AI replace code reviewers?
- No. It takes the first pass and catches bugs people miss, but it can’t judge whether a change is the right one. The vendors agree: Anthropic says its reviewer won’t approve pull requests because that’s still a human call, and by default GitHub doesn’t count Copilot’s reviews toward required approvals.
- Is AI code review safe for private code?
- It depends on the tool. Check where your code is processed, whether it’s kept after the review, whether anyone trains models on it, and whether the vendor has a SOC 2 report. cubic encrypts code in transit and at rest, its model providers are contractually barred from training on it, and it is SOC 2 Type 1 compliant. If code can’t leave your network, you need a self-hosted tool: Kodus on any plan, Greptile on Enterprise, or CodeRabbit on Enterprise with 500 or more seats.
- How much does AI code review cost?
- Pricing comes in three shapes, as of October 2026. Per developer: CodeRabbit starts at $24/dev/mo yearly ($30 monthly). Kodus starts at $8/dev/mo yearly ($10 monthly) + model costs. cubic: Free for public repositories, with 20 PR reviews a month on the free plan. Paid plans start at $30 per developer a month billed annually, or $40 monthly. Public-repository reviews are subject to fair-use limits, and the free Starter allowance is shared across the organization. Per review: Greptile bundles review credits with a monthly developer seat ($30/dev/mo with 50 credits, then $1 a credit). A Base review uses one credit. By usage: GitHub Copilot spends AI credits plus GitHub Actions minutes. Anthropic’s managed Claude Code Review starts at the Team plan ($20/seat/mo yearly) + ~$15-25 a review, and Enterprise is also supported. For most teams it isn’t worth paying for.
- Does AI code review work with GitLab and Bitbucket?
- Some tools do, but cubic doesn’t: it reviews GitHub pull requests only. CodeRabbit supports GitHub, GitLab, Bitbucket and Azure DevOps. Greptile supports GitHub, GitLab and Bitbucket. Kodus supports all four, plus Gitea and Forgejo.
- Can AI code review fix bugs?
- Yes. Many tools attach a suggested change you can commit from the pull request. Some go further and hand the fix to a coding agent: with Fix with cubic, on Pro and Max, cubic pushes a fix commit to the pull request branch or opens a fix PR, and it never pushes directly to main or protected branches.
- Can ChatGPT do code review?
- Yes, in two ways. Paste a diff into ChatGPT and it will review it, but it sees only what you paste and won’t run on every pull request. OpenAI’s Codex also reviews GitHub pull requests on ChatGPT Plus, Pro, Business and Enterprise plans. On Martian’s Code Review Bench, that reviewer, ChatGPT Codex Connector, ranks #13 of 15 (last-month view, checked Oct 10, 2026).
Judge an AI reviewer by the comments your team acts on.
Try cubic on your next GitHub pull request. It’s free for public repositories, and paid plans start with a 7-day trial, no card needed.