CodePulse Reviews vs CodeRabbit
Both put an AI reviewer on your pull requests. The differences worth your time are narrower than either marketing site suggests: where the review lands, what you pay per developer, and how much of what the bot says is worth reading.
We measured the last one ourselves. The numbers are below, with the parts that do not flatter us left in.
The short version: on that run CodePulse posted 82 comments to CodeRabbit's 125, at 95.4% precision against their 41.8%, and still found more of the real bugs - 56.0% of the 125 known defects to their 29.6%.
This is our own comparison of our own product. It reflects our positioning, not a neutral verdict.
They are not the same shape of tool
If one of these lists is obviously you, take it and skip the rest of the page.
Pick CodePulse Reviews if
- Your team lives in Slack. You paste a PR link in a channel, the review comes back in that thread, and the fix goes out from the same message. There is no dashboard to open and no second tab to keep current.
- You want fewer comments, each worth reading. Across the same benchmark we posted 82 comments to CodeRabbit's 125, and found more of the real defects doing it.
- You already pay a model provider. The BYOK plan runs reviews through your own Anthropic, OpenAI, Bedrock or Vertex key at $6 per developer per month.
- You want the fix, not just the note. One button turns the review into a commit on the branch.
Pick CodeRabbit if
- You want to start for nothing. They have a free tier. We do not, at any team size. If evaluating without a purchase order matters, that is a real reason to start with them.
- You want breadth over brevity. They posted 125 comments where we posted 82, including 41 our judges called nitpicks. Some teams genuinely want that wider pass, and a nitpick is only noise if you did not want it.
- You review outside the browser. Their plans include review in the IDE and from a CLI, which we do not offer.
- You need self hosting, SSO or audit logging now. Their Enterprise tier lists all three (coderabbit.ai/pricing, 7 August 2026).
How much of the review is worth reading
Greptile's published benchmark asks whether a tool found one nominated defect per case. That pays a reviewer for volume, because more comments buy more chances at the nominated defect and nothing is charged for the noise that comes with them, and it credits one defect per case in a corpus that actually holds 125. So we rebuilt the ground truth: every comment every tool posted, shown to two independent judges with the diff, clustered so one defect counts once, and only judge agreement creating a golden entry.
Three columns, because no single one is honest on its own. Defect recall is the share of the 125 known real defects a tool found. Case recall is how many pull requests it said anything useful about at all. Precision is how much of what it said was real, counting nitpicks against it.
| Reviewer | Defect recall | Case recall | Precision | Comments | Noise per case |
|---|---|---|---|---|---|
| CodePulse Reviews OURS | 56.0% | 77.5% | 95.4% | 82 | 0.1 |
| Greptile | 46.4% | 82.5% | 36.8% | 273 | 3.3 |
| BugBot (Cursor) | 35.2% | 70.0% | 81.0% | 72 | 0.3 |
| CodeRabbit | 29.6% | 52.5% | 41.8% | 125 | 1.3 |
| GitHub Copilot | 23.2% | 50.0% | 35.4% | 154 | 2.1 |
| Graphite | 6.4% | 15.0% | 84.2% | 31 | 0.1 |
What this does and does not show
Against CodeRabbit specifically: we found 56.0% of the 125 real defects, they found 29.6%. Our precision was 95.4% against theirs at 41.8%, and we said it in 82 comments against their 125.
The number that needs a caveat before you use it. Our 56.0% is the shipping build, which carries a checklist written from misses on a development split of this same benchmark, so that figure is partly in-sample. The clean out-of-sample number from the same run is 48.0%. Against CodeRabbit's 29.6% either one is a wide gap, but the honest one to argue from is 48.0%.
And the one we lose. Greptile lands a real finding in more pull requests than we do, 82.5% of cases against our 77.5%. It finds fewer of the total defects and posts far more noise getting there, but on the plain question of "will it say something useful about this PR", it beats us.
Everything that weakens the result, in full:
- This is our own benchmark. We built the harness, chose the metrics and ran our own arm. It is not independent and not a third party's verdict.
- One run per arm, with no repeats. The noise floor is 6.7 percentage points, so treat anything closer than that as a tie.
- About 22% of all comments are still disputed between the two judges and are excluded from precision rather than guessed at.
- Precision and defect recall come from different adjudication states. Recall is scored against the fully resolved list; the per-comment tallies behind precision are the first-round classification and were never recomputed as disputes settled. Every tool's precision would move slightly up if they were, without changing the order.
- The golden list can only contain defects that some tool commented on, so it is a floor on what is in the corpus, not a census.
- Adjudication has scored this corpus at 95, then 108, then 125 defects as it improved. Earlier published versions of these figures are retired.
- Greptile beats us on case recall, 82.5% to our 77.5%, meaning it lands a real finding in more pull requests than we do.
About the fixture
The public evaluation forks leak the answers three ways: rival bots' reviews sit on the pull request, branch names describe the planted defect, and repository history contains the upstream fix. We closed all three for our runs and audited a transcript to confirm. We are not suggesting anyone read them, and one of the leaks worked against us, because our reviewer stayed quiet about a defect a rival had already posted. The narrow, checkable statement is that of the six columns, one was produced under conditions someone verified, and it is ours.
803 comments across 42
cases, judged by Claude Opus 5 and GPT-5.6-sol over
4 rounds of disagreement resolution. Raw
per-comment classifications live in the benchmarks/
tree of our repository. The same run is written up at
codepulsehq.com/research/ai-code-review-precision.
Where the money actually differs
Their list price, as they publish it, next to ours. Neither column is the whole cost of ownership, but it is the number finance will ask about.
CodePulse Reviews
| BYOK your own Anthropic, OpenAI, Bedrock or Vertex key |
$6 per dev / month |
| Team frontier model on every review |
$29 per dev / month |
| Business adds layered SAST pre-review and compliance |
$49 per dev / month |
No free tier. Slack workflow is included in every plan, not metered.
CodeRabbit
| Free 14 day trial of Pro Plus, unlimited repositories |
$0 per user / month |
| Pro billed annually |
$24 per user / month |
| Pro Plus billed annually |
$48 per user / month |
| Enterprise self hosting, SSO, audit logs |
Custom quote |
| Slack agent metered, on top of a seat |
$0.50 per agent minute |
Source: coderabbit.ai/pricing, 7 August 2026.
Read that honestly: their Pro tier undercuts our Team tier, and they have a free tier where we have none. Our BYOK plan is the cheapest row on either list, and it is cheap because you bring the model bill yourself. The line to watch on their side is the Slack agent, which is metered per agent minute on top of a seat; Slack is where our product runs by default and it is not a separate meter.
We review our own pull requests with it
Figures from our production account, measured 7 August 2026. One engineering organisation, which is the honest scope: this is heavy internal use, not a customer roster.
Reviews run includes re-reviews (13,354 of 21,296): pushing a fix and asking for another pass is ordinary use, and counting only first passes would understate the work. Repository, author and turnaround figures cover a trailing 30 days, because individual review records expire after 30 days. Reproduce with scripts/dogfood_stats.py.
The questions this page invites
Is this an independent comparison?
Your numbers disagree with the published benchmark. Why?
How confident should I be in a gap of a few points?
Do you train on our code?
Can we run both for a while?
Try it on the next PR someone is waiting on.
Install the Slack app, pick a repo, paste a PR link. That is the whole setup.
Weighing more than one option? See our guide to the best CodeRabbit alternatives.