Four vendors currently advertise themselves as the number one AI code reviewer, and all four are telling the truth. They cite the same independent benchmark on four different dates, in two different modes, and one of them is quoting a research preview you cannot buy. If you are adding automated review to a large monorepo, the leaderboard is not the artifact you need. The number that predicts whether your engineers keep reading the bot's comments in month three is precision on your pull requests — and no published benchmark measures those.
Here is the short version. Buy Greptile if you can live with its training terms. Buy Qodo if procurement needs contractual no-training and on-premises. Buy CodeRabbit if you need Azure DevOps and Bitbucket on day one. Do not buy Graphite for the review.
| Tool | List price (checked 2026-09-03) | Cost at 200 engineers, 4,000 PRs/mo | Self-host | Trains on your code? |
|---|---|---|---|---|
| Greptile Pro | $30/seat/mo, 50 credits/seat, $1 per extra credit | ~$6,000/mo (10,000 credits included) | Enterprise tier | Aggregated + anonymized, opt-out |
| CodeRabbit Essentials | $24/dev/mo annual ($48 Team, $72 Advanced) | ~$4,800/mo | Enterprise, 500+ seats | No |
| Qodo Merge | $30/mo base + $0.012/credit, Pro caps at 30 users | Enterprise quote required | On-prem, any size | No |
| Cursor Bugbot | Usage-based, $1.00–$1.50 per run | ~$4,000–$6,000/mo, uncapped | No | No, with Privacy Mode — default on for Enterprise |
| GitHub Copilot Business | $19/seat/mo + review credits | $3,800/mo + $1,000–$20,000 credits | No | No |
| Graphite Team | $40/user/mo annual, unlimited AI reviews | ~$8,000/mo | No (GHES on Enterprise) | Vendor says no |
Four Vendors Each Claim #1 on the Same Benchmark
Martian's Code Review Bench is the closest thing this category has to an independent referee. It runs two tracks: an offline benchmark scoring every tool against 50 curated pull requests with human-verified bugs, and an online benchmark that watches which review comments developers in real open-source repositories actually act on. The second is the honest one, because the PRs are fresh enough that no tool can have memorised them.
Then look at what the vendors did with it.
CodeRabbit announced on 3 March 2026 that it topped the online benchmark with an F1 of 51.2% — precision 49.2%, recall 53.5% — measured across nearly 300,000 pull requests over January and February. Qodo announced on 15 March that it was #1 with 64.3% F1. Read two paragraphs further and the tool scoring 64.3% is "Qodo Extended," a research preview; the production version, Qodo Standard, ranks fourth at 47.9%. cubic claimed the top slot on 25 March with 61.8% F1 — in offline mode, a different track. Greptile claimed it on 30 July at 60.8% F1, with CodeRabbit by then at 57.5%.
None of these is a lie. A live leaderboard that re-scores continuously will hand every serious vendor a week on top, and each of them screenshotted their week. The useful signal in that pile is not the ranking. It is Greptile's precision of 76.2%, the highest figure any vendor has published on this benchmark. Compare it only against the same snapshot, and compare it against the whole table rather than the two rows a vendor would pick: on Greptile's July board Devin sits at 73.6%, the ChatGPT Codex connector at 73.3%, cubic at 72.6% and CodeRabbit at 64.9%. The top four are inside 3.6 points of each other. That is a far tighter spread than the duelling #1 claims suggest — close enough that the lead is a reason to shortlist Greptile, not a reason to skip the bake-off — and it is the reason to insist on the same-snapshot rule — CodeRabbit's 49.2% comes from a different track in March, cubic's own March post puts CodeRabbit at 24.7%, and none of those numbers are comparable to each other. Precision is the share of the bot's comments that matched a change the developer actually made. It is the number your engineers experience as trust.
Why Precision Collapses on Real Pull Requests
Here is the finding that should reset your expectations. A comparative evaluation of five LLMs on automated code review tested 150 samples — 100 synthetic mutation-injected bugs and 50 real bug-fix pull requests mined from eight open-source repositories. On the synthetic set, the best model reached an F1 of 0.847. On the real pull requests, the best model reached 0.066. That is a 92% degradation between the benchmark and the job.
The same paper reports F1 falling from 0.657 on diffs under 10 lines to 0.043 on diffs over 150 lines, and near-zero recall on performance-related bugs across all five models. Treat the diff-size number carefully: the synthetic samples are tiny and the real ones are large, so that curve is partly the same synthetic-versus-real split relabelled rather than an independent effect. The direction still holds, and it points straight at monorepo pull requests, which are large by construction.
Two caveats matter before you carry this into a vendor meeting. This study evaluated base models — Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.4 mini, Minimax M2.7 and GLM-5 Turbo — not the commercial products, which wrap those models in repository indexing, retrieval and rule systems that exist precisely to close this gap. And its most quotable result cuts against the industry's spending habits: Haiku 4.5 beat Sonnet 4.6 (F1 0.365 versus 0.343) at 3.2x lower cost per review. Bigger is not the axis.
What this does establish is that a vendor benchmark score is measured on a population that is not your monorepo, and the gap between the two is roughly an order of magnitude.
The Bottleneck Is Pickup Time, Not Review Time
The strongest business case for these tools is not bug-catching. It is queue time — and the largest dataset on it points one way, with a caveat worth stating up front.
LinearB's 2026 benchmarks, published 4 May 2026 across 8.1 million pull requests from 4,800 teams in 42 countries, found AI-generated pull requests wait more than 16 hours on average for a reviewer to pick them up, against roughly 200 minutes for unassisted work. But once a human starts reviewing, AI-generated PRs finish faster — about 194 minutes versus 252. On this dataset the delay sits in the queue rather than in the review itself. AI-assisted PRs are also about two and a half times larger at the 75th percentile (over 400 lines versus 157), and only 32.7% of AI-generated PRs merge within 30 days against roughly 84.5% for manual ones — a gap that wide is not a pickup-time story, so treat the queue as the leading problem rather than the only one.
Two things about that dataset before you quote it in a business case. The 4,800 teams are LinearB's own customers — organisations that had already bought an engineering-metrics platform, which is not a random sample of engineering organisations — and the report does not disclose how it attributes a pull request to AI in the first place. The direction is consistent with everything else in this category. Treat the specific multiples as this population's, not as constants.
A longitudinal study of an enterprise "2x" mandate — 802 developers, 196,212 pull requests, January 2024 to April 2026 — found per-capita throughput reached 2.09x its pre-mandate baseline while per-reviewer load roughly doubled and automated review overtook human review, with merge and revert rates holding steady. The authors are explicit that adoption was not randomly assigned, so this is an association, not a proven cause.
Read those together and the purchase makes sense: an automated reviewer's real job is to put a competent first pass on a pull request in the 16 hours before a human opens it. Buy it for latency, and measure it that way.
What These Tools Cost at 200 Engineers
Vendors quote incompatible units — per seat, per credit, per run, per dollar of "AI credits" — so normalise everything to one workload: 200 engineers, 4,000 pull requests a month, one monorepo.
Greptile is $30 per seat per month on Pro with 50 credits per seat included and $1 per additional credit. At 200 seats that is $6,000 a month and 10,000 pooled credits against 4,000 reviews — comfortable, until you use TREX reviews, which cost three credits each. CodeRabbit runs $24, $48 and $72 per developer per month annually across Essentials, Team and Advanced; the entry tier is the cheapest seat-priced option here at $4,800 a month, and the security and blast-radius analysis most monorepo teams actually want sits on Advanced at $14,400.
Qodo prices at $0.012 per credit with roughly 139 credits per review, but its Pro Team plan caps at 30 users — at 200 engineers you are in a custom Enterprise contract from the first conversation. Cursor Bugbot moved off its $40-per-seat subscription to usage-based billing on 8 June 2026, at a stated average of $1.00 to $1.50 per run. That is $4,000 to $6,000 a month at this volume with no published ceiling, and large multi-file refactors — the expensive ones — cost more.
GitHub Copilot is $19 per seat on Business and $39 on Enterprise, and its code review consumes "$0.05 USD to $1 USD worth of AI credits with 'Lite' effort, and $0.25 USD to $5 USD with 'Balanced' effort." At 4,000 Balanced reviews that band spans $1,000 to $20,000 a month. A twentyfold documented range is not a budget line, it is a risk you have to cap. Graphite is $40 per user per month annually on Team with unlimited AI reviews — $8,000 a month, the most expensive row here.
Who Should Not Buy Each of These
This is the section vendors never write.
Do not buy Greptile if your legal team reads subprocessor terms. Its security documentation states that Greptile "may aggregate and anonymize Customer Data" and "may use such data to train and improve artificial intelligence algorithms and models." There is an account-level opt-out and self-hosted deployments collect nothing unless you configure it, but the default is permissive and it is the only tool here where that is true. Inference runs through OpenAI and Anthropic APIs.
Do not buy CodeRabbit expecting to self-host at mid-size. Its self-hosted option is "available for CodeRabbit Enterprise customers with 500 or more user seats." At 200 engineers that door is closed. Its data posture is otherwise the cleanest of the SaaS options — the FAQ states CodeRabbit "never uses customer code for training," true whether retention is on or off — but note it also says your code "is shared with OpenAI and/or Anthropic for reviewing purposes only," and does not document a zero-retention agreement with either.
Do not buy Qodo for a 25-person team; the Pro tier's economics stop at 30 users and everything above it is a sales cycle. Do buy it if the requirement is contractual: its pricing page states plainly that "Qodo does not train models on your code," and Qodo Merge offers single-tenant and on-premises deployment across GitHub, GitLab, Bitbucket and Azure DevOps without a seat-count gate.
Do not buy Cursor Bugbot unless your team already lives in Cursor. Per-run billing with no published cap, on a monorepo where PR size is the cost driver and PR size is rising, is a meter pointed at your worst month. It is the best-integrated option for shops already on Cursor Teams at $40 per user, and Azure DevOps support is still limited availability.
Do not buy Graphite for the review. Graphite's real product is stacked pull requests and the merge queue, and it is good at that. But it does not appear on any independent leaderboard, publishes no precision figure, offers no self-hosting, and asks $40 per user — double CodeRabbit's entry tier — for a bundled reviewer nobody outside the company has measured. Its own guide asserts that the agent "does not train on your private codebase," which is a vendor claim on a vendor page, not a contract. If you want Graphite's workflow, buy Graphite and run a different reviewer inside it.
Do not buy GitHub Copilot code review as your only control, but do turn it on. It is included in Business and Enterprise seats you have probably already bought, configured through branch rulesets, and it excludes dependency manifests, log files and SVGs from review. It is a reasonable floor. It is not a monorepo reviewer, and it is GitHub-only.
Your Rules Are the Product, Not the Model
Every tool here converges on the same architecture — repository index, retrieval, custom rules — so the differentiator is how much policy you can encode and how the tool learns to shut up.
Bugbot is the most explicit: three rule types (team-wide, a .cursor/BUGBOT.md per project, and learned repository rules), with each rule truncated at 30,000 characters and the combined set capped at 100,000. Those caps are a real constraint in a monorepo with a dozen service-specific standards, and they are documented, which is more than most. Qodo configures through .pr_agent.toml and runs a Rule Miner that turns recurring patterns from your own PR history into enforced rules. Greptile learns from thumbs-up and thumbs-down reactions and tells you the payback period in its own docs: after two to three weeks it stops commenting on things you do not care about.
That two-to-three week figure is the one to hold vendors to, because the failure mode in this category is not a missed bug. It is comment volume training your engineers to scroll past the bot — and once that habit forms, the tool is worse than nothing, because it looks like coverage.
One structural warning. A study of AI-to-AI code review on GitHub found 248,641 AI-attributed pull requests that received at least one AI-attributed review, of which 208,145 were reviewed by the same product that wrote them. If your coding agent and your reviewer are the same vendor, you have automated the appearance of a second opinion, not the substance.
Run This Evaluation in the Next 30 Days
This week. Pull your own numbers first: median reviewer pickup time, PR size distribution, and what share of PRs merge within 30 days. Without that baseline you cannot tell a good tool from a placebo. Enable Copilot code review on one repository — it costs you seats you already own — and set it to Balanced.
Weeks two and three. Run Greptile, CodeRabbit and Qodo in parallel on the same 100 recent merged pull requests, in advisory mode, commenting only. Have two senior engineers label every comment as acted on, ignored, or wrong. That labelled set is your private precision benchmark and it is worth more than every number in this article. Track cost per review while you do it — the credit and per-run models diverge fast at monorepo diff sizes.
Before you sign. Get three things in writing: the training and retention terms including subprocessors, the self-hosting seat threshold, and a hard monthly spend cap. Ask each vendor for their precision on your labelled set. The one that engages with the question is usually the one to buy.
The Bottom Line
Buy Greptile as the default at monorepo scale — it holds the highest published precision on the only independent benchmark in the category, and precision is the variable that decides whether the tool survives contact with your engineers. Take the training opt-out on day one and put it in the contract. If your legal team will not accept a default-permissive training clause at any price, buy Qodo instead; it is the only option here offering on-premises deployment without a 500-seat gate and a plain no-training statement. If you need Bitbucket or Azure DevOps immediately, CodeRabbit's entry tier is the cheapest credible seat on the board.
Then ignore every leaderboard, including the one this article is built on. Four vendors won it this year, on four dates, and the one study that has measured the drop-off — a preprint, on 50 real pull requests — puts the gap between a benchmark score and real-PR performance at about 92%. Your 100 labelled pull requests are a smaller sample and a better instrument.
The tool that catches the most bugs is not the one you want. The tool your engineers still read in month six is.
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