Best AI code review tools for engineering teams (2026): What ChatGPT, Claude & Gemini Recommend

CodeRabbit leads this edition. Ten AI models named it in 50 of 50 recorded answers (CodeRabbit 100%), and placed it first in 37 of 50 (CodeRabbit 74%). Qodo came second, named in 43 of 50 answers. The counts below rank the 13 products the models named for an engineering team choosing an AI code review tool. They measure how often a name appeared and how early. They do not measure how well a product works. Figures come from the edition recorded on 2 September 2026, and prices and capabilities come from the recorded answers themselves.

TL;DR

• CodeRabbit leads on frequency, on first place and on ordering, and it is the leading name in every one of the ten models.
• Being named often and being named first are different results. Qodo is named in 43 answers and placed first in 1.
• The models agree on the shortlist and disagree about the order, largely along vendor ecosystems.
• Prices in the answers disagree with each other, and three products carry no recorded price at all.
• A count here means a model wrote the product’s name. It is a visibility measure, not a quality score.
• Use the counts to build a two-tool shortlist, then trial both on your own pull requests.

What counts as an AI code review tool?

An AI code review tool reads a code change and writes findings an engineer can act on, usually inside a pull request and sometimes in the editor before the pull request exists.

The recorded answers sort the candidates into four groups. Dedicated pull-request reviewers include CodeRabbit, Qodo and Greptile. Native platform reviewers include GitHub Copilot code review and GitLab Duo. Coding tools with review features include Cursor Bugbot. Static-analysis platforms that added an AI layer include SonarQube, Snyk and DeepSource. A team deciding between them is often deciding which of those four jobs it actually needs done.

The answers also name products this edition does not track, including Semgrep, Aikido Security, Panto AI and Macroscope. Those names carry no count here, because a rank needs a measured figure behind it. This page counts names. It does not judge reviews.

1. CodeRabbit

Pick CodeRabbit if your team wants one reviewer on every pull request and would rather not run an evaluation cycle to find it.

Measured: named 50 of 50 (CodeRabbit 100%), named first 37 of 50 (CodeRabbit 74%), average position 1.58.

CodeRabbit is the only product the models reached for without exception, and the only one that opened an answer for every model in the panel. The record describes a dedicated pull-request reviewer: comments on the diff, a written summary of the change, one-click fixes, follow-up questions in the thread, and the same review available from the editor and the command line. Its counts read as a safe default rather than one option among several, which is what the CodeRabbit 74% first share means. The weakness in the record is cost drift, with answers tracing a move to usage-based billing as agent-generated pull requests raise review volume. Tuning review instructions is the other recurring note.

Pros

• Named in all 50 recorded answers, the only product to appear in every one
• Taken first in 37 of 50, which no other product approaches
• Supports GitHub, GitLab, Bitbucket and Azure DevOps, per the answers
• Reviews arrive in the IDE and the CLI as well as at the pull request
• One-click fixes and follow-up questions in the pull-request thread, per the record

Cons

• Recorded prices disagree, from a $15 private-repo tier in one answer to $24 Essentials and a $30 usage-based plan in others
• Three answers flag noise and non-issue comments until review instructions are tuned
• Hourly review limits and usage charges above the included credits appear in one answer
• Multi-repository depth and security features sit on higher tiers, per one answer

Pricing: from $24 per developer per month on the Essentials plan in one answer, recorded at $15 for private repositories and roughly $30 on a usage-based plan in others, with Team at $48 and Advanced at $72. Best for: teams that want one reviewer across every pull request, on any major Git host.

2. Qodo

Pick Qodo if your team runs many repositories and needs one rule set enforced across all of them.

Measured: named 43 of 50 (Qodo 86%), named first 1 of 50 (Qodo 2%), average position 3.56.

Qodo is what the models reach for when the question stops being about a diff. The record describes cross-repository context that flags a change in one repository breaking a contract in another, a rules system that teams version and enforce organisation-wide, review that also runs before the pull request in the editor and the command line, and test generation alongside the review. Self-managed and air-gapped deployment appear in the record, which is why the answers place it in regulated and platform-team conversations. Named in 43 of 50 answers, it is the most-named product that almost never opens an answer. One first-place mention in 50 is the widest gap in the edition.

Pros

• 43 of 50 answers name it, second only to the leader
• Rules are described as living, versioned and enforced across repositories
• Self-managed and air-gapped deployment appear in the record

Cons

• Named first once in 50 answers, the widest named-versus-first gap in the edition
• One answer warns that setup and policy work exceed what a simple pull-request bot needs
• Perplexity named it in three of five answers, its thinnest coverage of any model

Pricing: Pro Team from around $30 per user per month with credit packs, and a free individual path is recorded. Best for: multi-repository organisations that need standards applied consistently.

3. GitHub Copilot

Pick GitHub Copilot code review if the repositories already live on GitHub and the team would rather not add another vendor.

Measured: named 37 of 50 (GitHub Copilot 74%), named first 2 of 50 (GitHub Copilot 4%), average position 4.03.

GitHub Copilot code review is the low-procurement option. The record describes review inside GitHub with suggested fixes, automatic review rules and agentic context gathering, and the same review reachable from the CLI, desktop and mobile clients, several editors, and Azure DevOps in public preview. Teams already paying for Copilot can switch it on without a second vendor, which is why it is named so often without a pricing conversation attached. It also carries the clearest cost warning in the record. Answers state that review draws on the same AI credits as chat and agent mode, and that runs have consumed GitHub Actions minutes since June 2026.

Pros

• 37 of 50 answers name it, third in the edition
• Review sits inside GitHub with suggested fixes and automatic review rules
• Available from GitHub, the CLI, editors, mobile clients and Azure DevOps in public preview
• Bundled with plans a team may already pay for

Cons

• Costs land in the same AI credit pool as chat and agent mode, plus GitHub Actions minutes since June 2026
• GitHub-only scope limits it outside GitHub shops, per two answers
• One answer calls its review shallower than dedicated reviewers, with the model chosen for you

Pricing: bundled with eligible Copilot plans from about $10 per user per month, billed through AI credits and GitHub Actions minutes. Best for: GitHub-native teams that already pay for Copilot.

4. Greptile

Pick Greptile if your recurring review misses are architectural and the diff alone does not show them.

Measured: named 36 of 50 (Greptile 72%), named first 0 of 50 (Greptile 0%), average position 3.36.

Greptile is the option for teams whose review misses are architectural. The record describes indexing the whole codebase rather than the changed lines, which lets it flag a downstream consumer a diff would not show, and it titles that capability plain-English custom rules. Startup terms appear in the answers: a free single-developer plan, a discount before Series A, free access for open source, and reviews live shortly after installing the Git app with no configuration file. Named in 36 of 50 answers and first in none, it places highest of any product here that never opens an answer. The trade recorded against it is noise, with one comparison counting more false positives for Greptile than for the alternatives tested.

Pros

• 36 of 50 answers name it, fourth in the edition
• Indexes the whole codebase rather than the diff, which is what the record cites it for
• Startup terms include a free single-developer plan and a pre-Series A discount

Cons

• Never named first in 50 answers
• One comparison counts more false positives for it than for any other tool tested
• ChatGPT names it in one of five answers, its thinnest model coverage
• Intensive review modes consume more credits than a standard review, per one answer

Pricing: $30 per seat per month including 50 credits, with extra credits at $1, and a free Starter plan for one developer. Best for: large, interconnected codebases where the diff hides the risk.

5. CodeAnt AI

Pick CodeAnt AI if you would rather buy review, scanning and engineering metrics as one platform than assemble three.

Measured: named 26 of 50 (CodeAnt AI 52%), named first 3 of 50 (CodeAnt AI 6%), average position 4.35.

CodeAnt AI sells consolidation. The record describes one platform covering AI review, SAST, secrets detection, infrastructure-as-code scanning and engineering metrics, with analysis running in the editor, the command line and CI, and fixes opened as pull requests rather than left as comments. That bundle explains a third place on first mentions against a lower frequency elsewhere. The split is the interesting part. Named in 26 of 50 answers, it appears in none of the answers from the three OpenAI models, in all five from Claude and in all five from Perplexity. A team asking ChatGPT what to use will not see this name.

Pros

• 26 of 50 answers name it, fifth in the edition
• Bundles review with SAST, secrets detection, infrastructure-as-code scanning and engineering metrics
• All four major Git hosts are covered in the record

Cons

• None of the 15 answers from the three OpenAI models names it
• Average position 4.35 leaves it outside the leading four on ordering

Pricing: $24 per user per month, with a 14-day trial recorded. Best for: teams replacing a review tool, a scanner and a metrics tool with one vendor.

6. Cursor

Pick Cursor Bugbot if the team already writes code in Cursor and wants the review in the same window.

Measured: named 23 of 50 (Cursor 46%), named first 6 of 50 (Cursor 12%), average position 4.3.

Cursor Bugbot is the review that comes with the editor. The record describes review tied to the Cursor environment, an autofix step, and an adversarial design in which generation and review run on different models so the reviewer is not grading its own work. The answers raise that design as a question for teams to settle rather than a settled strength. The counts are unusual. 23 of 50 answers name it, and 6 place it first, the second-highest first count in the edition behind the leader. The disagreement is sharper than the total, because all five Gemini answers name it, four of five Claude Opus 5 answers name it, and none of the answers from GPT-5.6 Sol or either Perplexity model does.

Pros

• 23 of 50 answers name it, tied on count with SonarQube
• Named first in 6 of 50 answers, second only to the leader
• Autofix and diff review sit inside the Cursor editor, per the record

Cons

• Neither Perplexity model names it in any answer, and GPT-5.6 Sol names it in none
• Value depends on the team already working in Cursor, which three answers state plainly
• Recorded platform scope is Cursor plus GitHub rather than the four major hosts

Pricing: $20 to $40 per user per month, usage-based, as recorded. Best for: teams standardised on Cursor as the primary coding environment.

7. SonarQube

Pick SonarQube if the binding requirement is a compliance gate that can be audited.

Measured: named 23 of 50 (SonarQube 46%), named first 0 of 50 (SonarQube 0%), average position 5.87.

SonarQube is the compliance answer rather than the conversational one. The record describes rule-based static analysis with quality gates that block merges below defined thresholds, a free community edition, a cloud tier that includes SAST and compliance reporting, and enterprise terms priced away from the per-seat norm. Recent releases are recorded as leaning into the verification of AI-generated code. Its counts describe a specific audience. Named in 23 of 50 answers and never first, it holds the latest average position of any product in the edition at 5.87, which means answers list it last when they list it at all.

Pros

• 23 of 50 answers name it, tied on count with Cursor
• Quality gates are recorded as blocking merges that fail defined thresholds
• Deterministic analysis across a wide language set, plus a free community edition
• Four of five answers from each Perplexity model and all five from Claude name it

Cons

• No first places across the 50 answers
• Average position 5.87 is the latest in the edition
• Not named by GPT-5.6 Sol, ChatGPT or Gemini 3.5 Flash

Pricing: from about $34 per month on the cloud tier, with enterprise terms quoted per line of code or by annual agreement. Best for: regulated teams that need auditable quality gates more than comment threads.

8. Claude Code

Pick Claude Code Review if depth matters more than a predictable monthly bill.

Measured: named 18 of 50 (Claude Code 36%), named first 0 of 50 (Claude Code 0%), average position 3.83.

Claude Code Review is the depth-first option and the least predictable on cost. The record describes a multi-agent review inside Claude Code that dispatches specialised agents over one pull request, each covering a different dimension such as bug detection or test coverage, and it prices the work by usage rather than by seat. Named in 18 of 50 answers, it holds the best average position outside the leading four at 3.83, which suggests answers place it early when they reach for it at all. Who reaches for it is the point. All five Gemini answers name it, none of the OpenAI answers does, and neither Perplexity model names it in any of its answers.

Pros

• 18 of 50 answers name it, eighth in the edition
• Gemini names it in all five of its answers
• Average position 3.83 is the best outside the leading four

Cons

• Usage-based pricing per pull request means no flat seat cost to budget
• None of the OpenAI models names it, and neither does Perplexity

Pricing: usage-based, recorded as token-priced per pull request with no flat monthly rate. Best for: teams that value review depth over cost predictability.

9. Graphite

Pick Graphite if the team reviews stacked pull requests and wants the review inside that workflow.

Measured: named 15 of 50 (Graphite 30%), named first 1 of 50 (Graphite 2%), average position 4.67.

Graphite is a review workflow rather than a reviewer alone. The record bundles AI review with stacked pull requests, a pull-request inbox, a merge queue and team insights, which suits a team whose bottleneck is coordinating reviews rather than finding bugs in a diff. Named in 15 of 50 answers and first in 1, its average position of 4.67 sits mid-table. The model split is narrow for a workspace tool. Three of five answers from Claude Opus 5 and three of five from Perplexity name it, and neither Google model names it in any of its ten answers. Pricing is recorded per user on an annual Team plan with unlimited AI reviews.

Pros

• 15 of 50 answers name it, ninth in the edition
• Stacked pull requests, a merge queue and a review inbox are recorded as one workflow
• Claude Opus 5 and Perplexity each name it in three of five answers

Cons

• Neither Google model names it in any answer
• Average position 4.67 and a single first-place mention
• Recorded as GitHub-only, which narrows the fit further than the counts suggest

Pricing: $40 per user per month on the Team plan billed annually in one answer, with $20 to $40 recorded elsewhere. Best for: GitHub teams running stacked pull requests.

10. Snyk

Pick Snyk if the security scanner is the job and AI review is the addition.

Measured: named 14 of 50 (Snyk 28%), named first 0 of 50 (Snyk 0%), average position 6.5.

Snyk sits in a different job from the products above it. The record describes security-focused static analysis and dependency scanning, with one answer noting it is not primarily a conversational architectural reviewer and another pairing its free tier with a reviewer’s free tier to cover dependency scanning at no cost. It is named as an addition rather than a replacement. Named in 14 of 50 answers and never first, it averages a position of 6.5, so answers mention it late when they mention it. The model coverage is narrow in the same way, with GPT-5.6 Luna naming it in four of five answers and Sonar Reasoning Pro in three of five, while the other OpenAI answers skip it.

Pros

• 14 of 50 answers name it, tenth in the edition
• GPT-5.6 Luna names it in four of five answers
• A free tier is recorded for dependency scanning alongside a reviewer

Cons

• First-place mentions come to none in 50 answers
• Average position 6.5 is the joint latest in the edition
• Not named by GPT-5.6 Sol or ChatGPT
• Recorded as not a conversational architectural reviewer

Pricing: a free tier is recorded, and no paid price appears in the answers. Best for: security-first teams pairing a scanner with a dedicated reviewer.

11. DeepSource

Pick DeepSource if you want deterministic checks to run before any model writes a comment, and the team is small.

Measured: named 11 of 50 (DeepSource 22%), named first 0 of 50 (DeepSource 0%), average position 4.45.

DeepSource is for teams that distrust a purely model-written review. The record describes a deterministic static analysis engine running before the AI layer, so rule-based findings land before any generated comment, and it places the product with smaller teams and simpler repository structures rather than multi-repository governance. That ordering is the pitch, and its cost is less cross-repository reasoning than the leaders offer. Named in 11 of 50 answers and never first, it draws most of its total from one model, with four of five Claude Fable 5 answers naming it. Otherwise the spread is thin, one answer from each Google model, one from the OpenAI family, and none from either Perplexity model.

Pros

• 11 of 50 answers name it, eleventh in the edition
• Claude Fable 5 names it in four of five answers
• Deterministic static analysis runs before the AI layer, per the record

Cons

• No price appears anywhere in the record for it
• First places: none across the 50 answers
• One answer from each Google model and none from either Perplexity model
• Recorded as less deep on cross-repository reasoning than the leaders

Pricing: no price is recorded in this edition’s answers. Best for: small teams that want deterministic findings before model-written ones.

12. Codex

Pick Codex only if you are consolidating on an OpenAI coding agent rather than buying a review tool.

Measured: named 4 of 50 (Codex 8%), named first 0 of 50 (Codex 0%), average position 6.5.

Codex appears in these answers as an agent rather than as a review product. The record describes a coding agent that writes features, fixes bugs, runs tests and reviews codebases, integrating with GitHub to review pull requests, and one answer files it beside GitHub Copilot as the easiest starting point for a GitHub-native team rather than as a standalone reviewer. Named in 4 of 50 answers and never first, its placement averages 6.5. Two of those mentions come from Claude, one from Claude Fable 5 and one from Sonar Reasoning Pro, so its visibility rests on one family rather than on the panel.

Pros

• 4 of 50 answers name it, twelfth in the edition
• Recorded as an agent that writes, tests and reviews code rather than a dedicated reviewer

Cons

• Answer share of Codex 8%, a thin base for any conclusion
• Zero first-place mentions across 50 answers
• No price is recorded, and the review role is described only inside a broader agent
• Three of the four mentions come from one model family

Pricing: the record holds no price for it. Best for: teams already committed to an OpenAI agent stack.

13. Devin

Skip Devin as a reviewer. The models named it as one of the coding agents whose pull requests need review, not as a tool that does the reviewing.

Measured: named 1 of 50 (Devin 2%), named first 0 of 50 (Devin 0%), average position 6.0.

Devin is in this table for a reason worth stating plainly. The single answer that mentions it, from Claude, lists it among the coding agents whose output pushes a team towards a codebase-aware reviewer. It is a name in the question rather than an answer to it. Its measured presence is the smallest in the edition, one answer in 50, at an average position of 6.0, which is better than two products the models name far more often. No price, no review capability and no comparison position are recorded for it, so nothing here supports treating it as a review tool.

Pros

• Named once in 50 answers, by Claude, alongside the agents it describes sending pull requests from
• Average position 6.0, ahead of two products the models name far more often

Cons

• The mentioning answer lists it as a source of pull requests rather than a reviewer
• Pricing is absent from the record
• No first-place mentions and a single model that names it

Pricing: no price appears anywhere in the record. Best for: no review use case is recorded, so treat the name as a coding agent in the review queue.

How the tools compare

CodeRabbit leads the table on all four measures, and no other product comes close on first place. The pattern below it is a cliff rather than a slope. Four products sit between 26 and 43 mentions, and between them they open 6 of the 50 answers.

That is the shape a reader should take from this table. The models agree that these products belong in an answer. They largely agree that CodeRabbit belongs at the start of it.

The named-versus-first gap makes the same point in one figure per vendor. CodeRabbit holds the smallest gap in the edition at 26 points, because it converts mentions into first places. Qodo holds the widest at 84 points, Greptile at 72 and GitHub Copilot at 70. A narrow gap means the models treat a name as the recommendation. A wide gap means they treat it as an alternative worth listing.

This ranking is this publication’s own count for one edition, not an independent award.

Where the models disagree

All ten models agreed on the leader. CodeRabbit was the leading name in every model’s answers, and the four families agreed with each other. The shortlist below is where they part company, and the splits follow vendor ecosystems more than product categories.

CodeAnt AI is the sharpest case. It is named in 26 of 50 answers, in none of the 15 answers from the three OpenAI models, in all five from Claude and in all five from Perplexity. Perplexity describes exactly what it is for: “CodeAnt AI is positioned as the best all-in-one option when you also want SAST, secrets detection, IaC scanning, and developer metrics in the same product.” A team asking ChatGPT and a team asking Perplexity get different shortlists from the same question.

Claude Code shows the mirror image. Gemini names it in all five answers, Claude and Claude Fable 5 in four of five each, and the three OpenAI models in none. The two Perplexity models also leave it out, with Sonar Reasoning Pro naming it once. GPT-5.6 Sol comes closest to a uniform answer, naming CodeRabbit, Qodo, GitHub Copilot and Greptile without touching Cursor, SonarQube, Graphite, Snyk, DeepSource, Codex or Devin at all. Its five answers open the same way: “For most engineering teams, I’d start with CodeRabbit.”

Greptile’s split runs the other way. Gemini, Gemini 3.5 Flash and Perplexity each name it in all five answers, and ChatGPT names it once in five. Cursor is named in five of five Gemini answers and none of the Perplexity answers. Graphite is named three times in five by Claude Opus 5 and Perplexity, and never by either Google model. The disagreement is not noise around a consensus. It is a different shortlist depending on which assistant a buyer asks.

Two positions came out of the panel unevenly. Claude Fable 5 was the only model to name DeepSource four times in five, and that single model accounts for most of a product named in 11 of 50 answers. Codex takes four mentions, two from Claude and one each from Claude Fable 5 and Sonar Reasoning Pro, and none from the other seven models.

Why the ranking looks the way it does

The measured table shows which names appear. The run data also records where the answers got their information, and that is the finding a buyer cannot read off the table.

The most-cited host in this category is youtube.com with 210 citations. The second is codeant.ai with 151. The rest of the top group is sourcegraph.com at 76, m.youtube.com at 76, techsy.io at 67, deepsource.com at 51, dev.to at 45 and greptile.com at 43. Three of those eight are owned by vendors in the category, and two more vendor domains sit just outside the group, at docs.coderabbit.ai with 22 citations and qodo.ai with 19.

Recorded answers say the same thing in their own words. Claude Opus 5 opened one answer with this caution: “nearly every "best AI code review tools 2026" article is published by a vendor that ranks itself at or near the top”. Claude went further, quoting a comparison site that makes the point about CodeRabbit and Qodo blogging their own first places. Claude Fable 5 drew the operational conclusion that “accuracy varies enormously across this category” and advised piloting on your own repositories before committing.

The inference this supports is narrow and worth stating as an inference. A vendor that publishes comparison content is more likely to sit inside the retrieval set the models draw on, so part of what looks like market consensus is content work. The counts here cannot show causation, and no citation count says anything about whether a product reviews code well. What they do show is that a high mention count is partly a measurement of who else is writing about the category.

How the sample was built

This edition is 10 models x 5 fixed prompts = 50 recorded answers. One answer was recorded per model per question, in September 2026, with web search enabled, through the models’ own APIs.

The five questions, verbatim:

1. What is the best AI code review tool for an engineering team? Name specific products.
2. Which AI code review tool would you recommend to an engineering team in 2026?
3. Compare the top AI code review tool options right now.
4. I’m an engineering team and I need an AI code review tool. What should I use and why?
5. Best AI code review tool for a fast-moving startup?

The ten models are GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity and Sonar Reasoning Pro. Four labels are short forms: ChatGPT is GPT-5.6 Terra, Claude is Claude Sonnet 5, Gemini is Gemini 3.6 Flash and Perplexity is Sonar Pro. They come from four families, with OpenAI and Anthropic supplying three models and 15 answers each, and Google and Perplexity supplying two models and 10 answers each.

Seventeen vendors were tracked for this category and 13 were named at least once. A product no model named is not listed. The method behind the panel, including how names are matched and what a share means, is set out on the method page, and every answer in this category is published in the category record.

How this sits against the AI code review guides

The five pages captured for this question alongside the run are a fair picture of how the category is written about, and three of them are published by a company that also sells a product in or near it.

The first is a subscription-funded review site’s listicle on open-source AI code review tools, built on a six-week evaluation of ten tools against a 450,000-line monorepo. Its top three are PR-Agent, which it describes as Qodo’s open-source pull-request reviewer, then SonarQube Community Edition and Semgrep Community Edition, and its comparison table carries GitHub stars, free-tier limits, monthly prices and language counts. It is written for teams that will self-host: “Verdict: The most complete open-source AI code reviewer in 2026.”

The second is vendor content. Cubic publishes a four-tool roundup on its own blog and ranks itself first, with a benchmark claim attached: “Cubic ranks first in this roundup for GitHub-centered teams that need context-aware review alongside PR explanations, custom agents, and follow-up workflows. Cubic is also the #1 AI code reviewer on independent benchmarks, scoring 61.8% F1 on the Martian benchmark.” CodeRabbit, Qodo Merge and GitHub Copilot code review are the alternatives it allows.

The third is a mismatch. A twelve-tool “best tools AI testing” listicle from the makers of one of the twelve covers UX testing, visual regression and model evaluation, and states its own position in the intro: “Throughout this listicle, we will analyse each tool, including our own platform, Uxia, offering a transparent look at its strengths and ideal use cases.” It ranks its own product first and covers no code review tool at all. It ranks for this question because it shares its words.

The fourth is another vendor blog, from the makers of a context-retrieval product, scoring five tools against six requirements and placing its own product first: “Unblocked leads this list for teams that need context spanning code and organizational sources, because it resolves conflicts between authoritative and outdated sources and feeds that context to any AI coding agent through one MCP server.” It is the most careful of the five about evidence, labelling vendor-sourced figures as vendor-reported and citing engineering writing on retrieval limits. Its subject is the context a coding agent retrieves, not the review of a diff.

The fifth is an enterprise services guide aimed at engineering leaders rather than buyers of one tool. It carries the category’s headline statistics, a four-layer review architecture, and a five-tool accuracy comparison it sources to Greptile’s own benchmark, where “Greptile led with an 82% catch rate on 50 real production bugs” and Graphite Agent finished last.

Those pages rank products for purchase, using evaluations, benchmarks and vendor claims, and one of them lists Sourcery as a primary editor-level review tool. No model in this run named Sourcery at all. None of the five carries an affiliate disclosure in the text captured, and two of them are written by a company that ranks its own product first without saying so in a disclosure line. This page does the opposite job with the same subject. It counts the names ten models produced across 50 recorded answers to five fixed questions, publishes the split so a reader can see where they part company, and states what the counts do not measure.

What should a buyer do with these counts?

Use them to build a two-tool shortlist, then trial both on your own pull requests. Nothing here settles which product is better, and none of it replaces a test on real code.

Four moves follow from the table. Match the team’s binding constraint to an entry rather than to the top of the list, because the products below CodeRabbit were built for different jobs. Check the model split against the assistant your team actually asks, since a name that never appears in one model’s answers may never reach the engineer writing the ticket. Treat every pricing line on this page as a claim from the record to verify rather than a current quote, because the answers disagree with each other on the same product. Then run the trial the answers themselves recommend: two to four weeks on real pull requests, counting accepted findings, dismissed comments, review latency and cost per reviewed pull request.

One caution belongs with the shortlist. A model can name a product in order to warn against it. The counts record presence, and the entries above are where presence is separated from the model’s actual advice.

What these counts cannot tell you

• Answer share counts presence in recorded answers. It is not a measure of quality, satisfaction, adoption or revenue.
• One answer was recorded per model-and-question pair, so a single changed answer moves a count by two points.
• The run is one dated snapshot from September 2026. Prices, features and product scope change faster than editions do.
• Names are matched as strings against known aliases. A bare “Copilot” in an answer counts for GitHub Copilot and a bare “Cursor” counts for Cursor.
• The panel queried model APIs with web search on. A person typing the same question into a consumer chat app may see a different answer.
• Every prompt was written in English and answered for a general engineering team, not for a specific stack, budget or region.

The single most important limit is the first one. This page is a visibility measure. It says which products the models put in front of a buyer, and it says nothing about whether any of them will find the bug in your pull request.

Frequently asked questions

Is there any AI tool for code review?

Yes, and the models named 13 of them in this category. CodeRabbit was named in all 50 recorded answers, followed by Qodo in 43, GitHub Copilot in 37 and Greptile in 36. The set covers four kinds of product: dedicated pull-request reviewers, reviewers built into a Git platform, coding tools that added review, and static-analysis platforms that added an AI layer. A further four tracked products were never named in any answer.

Which AI tool is best for engineers?

The counts answer a narrower version of that question. CodeRabbit is the leading name in all ten models’ answers, which makes it the safest name to put on a shortlist. Every other product is a shortlist entry rather than a recommendation: Qodo is named in 43 answers and placed first in 1, and GitHub Copilot is named in 37 and placed first in 2. Which one fits depends on the job, since a compliance gate, a diff reviewer and a coding agent solve different problems.

Can ChatGPT do a code review?

This page cannot answer that, and it is worth being exact about why. ChatGPT is one of the ten models in the panel, not one of the products tracked, and what it did here was name products rather than review code. Its answers named CodeRabbit in all five, GitHub Copilot in all five and Qodo in all five, and named Greptile once. The run holds no measurement of review quality for any tool, including a chat assistant asked to read a diff.

What are the top 5 code review tools?

On this edition’s counts, the five most-named products are CodeRabbit, Qodo, GitHub Copilot, Greptile and CodeAnt AI. Their counts are 50, 43, 37, 36 and 26 out of 50 answers. CodeRabbit is the only one of the five that opens answers regularly, at 37 first places, while CodeAnt AI opens 3, GitHub Copilot 2, Qodo 1 and Greptile none.

Should an AI reviewer replace human review?

The recorded answers say no, and the reasoning in them is worth keeping. AI review is described as a consistent first pass that catches mechanical defects and applies the same standard to every pull request, while human reviewers keep the decisions that need intent, architecture and risk acceptance. One answer put the division plainly, with AI in the first-pass role and humans in the deciding role. Nothing in this run measures whether any product achieves that, so treat it as the panel’s stated position rather than a finding.

A product I use is not listed. Why?

Because no model named it. Seventeen vendors were tracked for this category and four of them were never mentioned in any of the 50 answers, so they carry no count and no row. Absence from this page means the models did not produce that name when asked these questions. It does not mean the product is weak or unused.