Devin Review 2.0 cuts code review costs by 82%: a PR now costs $0.25–0.75

Picture this: a developer uses an agent to put together a dozen pull requests in a day (a PR is a set of changes waiting for review before it is merged into the main codebase), and a human reviewer has time to read two or three carefully. The rest either sit in the queue or get skimmed. The creators of Devin think the first line of review should go to another agent, and they have released Devin Review 2.

0. The main selling point of the release is price: since August, reviewing one PR has become 82% cheaper (roughly five times), and it now costs $0.25–0.

75. At that price, you can run the bot on every PR, like automated tests in CI, instead of picking the "important" ones by hand.

What it costs and what you get

According to the company, the savings came from two sources: a better mix of models for the different stages of review, and an optimized harness around them. Quality did not drop as a result: the average number of bugs found rose from 1.22 to 1.32 per PR.

A few more figures from the blog. The median review time fell from 139 to 94 seconds, roughly 1.5 times faster. About 80% of PRs cost less than a dollar when you count all pushes to the branch. And the average cost of one bug that customers actually fixed is about $2.25. It is not a replacement for a human, but next to an hour of an engineer's time, the bot comes out ahead.

How to avoid paying for the same thing twice

The main technical change is incremental review. If another commit is added to a PR, Devin Review examines only the changed parts rather than the whole PR again. This is enabled by default. There are two benefits: the bill is smaller, because each push is smaller than the whole PR, and there is less noise, because new comments address fresh changes instead of repeating old ones.

There are several ways to trigger a review:

• with a /devin review comment in GitHub or GitLab;
• from the Devin Review page in Devin Cloud or Devin Desktop;
• automatically, when a PR is ready for review, or on every push.

Automatic runs are configured with rules: you can include drafts and filter by file paths, branches and labels. Customers use this to avoid spending money on low-value PRs. There is also a per-PR spending limit, so a PR that keeps growing will not inflate the bill. The bot also builds a "tour" of the PR: it briefly explains what changed and where to look, which makes it easier for a person to get up to speed. The tour can also be updated on every push.

Pairing with Devin and fine-tuning

Devin Review can review PRs from any source, but according to its creators, it works best together with Devin itself. In that setup, the agent goes through each comment on its own and decides whether to fix it, dismiss it or pass it to a human. By the time the developer returns to the session, only real bugs and contested decisions remain.

Two modes have been added. The first is effort levels. By default, review is tuned to save money, but in billing or security code a missed bug costs far more than a review. For such places there is Devin Review Ultra: a more thorough and more expensive review. The second is Custom Reviewers, still in beta. These are your own reviewers with your own instructions. Inside the company, they are used to check backend security practices, safe database migrations, performant SQL queries and accessible UI components. Each reviewer can have its own trigger rules, its findings go into the shared report and into Devin's auto-fix, and its spending is capped by the same limits.

What to do with this

The practical takeaway: if a review costs less than a dollar, it makes more sense to enable it on all PRs than to pick PRs by hand. A sensible start is to auto-run on PRs that are ready for review, cut out the small stuff with path and label filters, and keep Ultra for payments and authorization. Custom Reviewers are worth trying on the comments your team keeps repeating in reviews.

There are caveats, too. All the figures come from the company's own blog, and "1.32 bugs per PR" is an average across customers, with no comparison against independent tools. The "bugs found" metric does not say how many of them are actually important. So before making the bot a mandatory step in CI, it is better to spend a couple of weeks on your own repository watching the share of useful comments and the actual bill.