A real-world benchmark for AI code review | Hacker News Reader

A real-world benchmark for AI code review

qodo.ai

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Company creates a benchmark. Same company is best in that benchmark.

Story as old as time.

A dishonest AI company? Well, I never!

>optimize for thing

>thing gets optimized

Nothing suggest they built the benchmark before the product. It was built after the product was already shipped, more of an afterthought.

Some feedback for the team, looked at pricing page and saw it more expensive ($30/dev/mo) and highly limiting (20prs per month per user). We have devs putting up that many prs in a single day. With this kind of plan pretty much no way we would even try this product

It's true, those are some pre-AI quotas.

Not even, there are some months where I make 30+ prs at work (no llms, just a lot of code responsibilities).

Try git-lrc, totally free since it uses gemini key. Triggers reviews automatically on git commit.

I'm not as cynical as the others here; if there are no popular code review benchmarks why should they not design one?

Apparently this is in support of their 2.0 release: https://www.qodo.ai/blog/introducing-qodo-2-0-agentic-code-r...

> We believe that code review is not a narrow task; it encompasses many distinct responsibilities that happen at once. [...]

> Qodo 2.0 addresses this with a multi-agent expert review architecture. Instead of treating code review as a single, broad task, Qodo breaks it into focused responsibilities handled by specialized agents. Each agent is optimized for a specific type of analysis and operates with its own dedicated context, rather than competing for attention in a single pass. This allows Qodo to go deeper in each area without slowing reviews down.

> To keep feedback focused, Qodo includes a judge agent that evaluates findings across agents. The judge agent resolves conflicts, removes duplicates, and filters out low-signal results. Only issues that meet a high confidence and relevance threshold make it into the final review.

> Qodo’s agentic PR review extends context beyond the codebase by incorporating pull request history as a first-class signal.

I'm building a benchmark for coding agent memory following your philosophy. There are so many memory tools out there but I have not been able to find a reliable benchmark for coding agent memory. So I'm just building it myself.

A lot of this stuff is really new, and we will need to find ways to standardize, but it will take time and consensus.

It took 4 years after the release of the automobile to coin the term milage to refer to miles driven per unit of gasoline. We will in due time create the same metrics for AI.

Curious what papers you are reading on this. Benchmarks are way more important than people realize, on every level.

Cmd+F - "Overfitting"...nothing.

Nope, no mention of how they do anything to alleviate overfitting. These benchmarks are getting tiresome.

Call for pricing.

The benchmark measures whether a tool finds known bugs. That's useful but it's the wrong question for most teams in 2026.

The question auditors actually ask isn't "did your tool catch this bug?" It's "can you prove this change was reviewed, by whom, and that the code didn't change between review and merge?"

None of the tools benchmarked here produce verifiable evidence. They produce comments. A green checkmark on a PR tells you someone clicked a button. It doesn't tell you what they saw, whether the diff changed after review, or what risk level the change carried.

We took a different approach: instead of building another AI reviewer, we built a governance layer that wraps whatever review process you already use. Every PR gets a cryptographically sealed evidence bundle -- the exact diff, risk tier (L0-L4), findings, and a SHA-256 hash chain. Verifiable offline with one command. Open source, Apache 2.0.

https://github.com/DNYoussef/codeguard-action

Not a replacement for code review tools. An audit trail for them.

I'd be interested, but they don't even list any anthropic model on their code review benchmark, so I feel like they haven't really tested their benchmark on SOTA models.

Whenever I see this, I make the (almost always correct) assumption that the SOTA models had an advantage, with the alternative explanation being a complete lack of awareness of the state of AI (which is very very rare for a tool like this).

With SOTA missing, it also is a strong indicator that someone like you is not the target audience.

I don't think LLMs are the right tool for pattern enforcement in general, better to get them to create custom lint rules.

Agents are pretty good at suggesting ways to improve a piece of code though, if you get a bunch of agents to wear different hats and debate improvements to a piece of software it can produce some very useful insights.

I'm trying to bring a slightly different take to the pricing of ShipItAI (https://shipitai.dev, brazen plug). I've got a $5/mo/active dev + Bring Your Own Key option for those that want better price controls.

Still early in development and has a much simpler goal, but I like simple things that work well.

No way you can afford unlimited pr's and unlimited projects for 20$/month using anthropic api.

I feel like pricing needs to be included here. I kind of don't care about 10 percentage points if the cost is dramatically higher. Cursor Bugbot is about the same cost but gives 10x the monthly quota of Qodo.

I know this is focused solely on performance, but cost is a major factor here.

Where's the code for this? I'd love to run our tool, https://tachyon.so/, against it.

coderabbit being the worst while (presumeably) advertising the most seems to be check out at least, wouldn't believe the recall % seems bogus.

> Qodo takes a different approach by starting with real, merged PRs

Merged PRs being considered good code?

What do you suggest they use for ground truth?

I thought about this quite a bit. There are some nuggets in the open source code:

- vX.X.1 releases. when software was considered perfect but author had to write a fast follow up fix. very real bugs with real fixes

- Reverts. I'm sure anyone doing AI code review pays attention to this already. This is a sign of bad changes, but as important.

- PRs that delete a lot of code. A good change is often deleting code and making things simpler

For the first, your signal would be weak, for those events are rare. I don't think deleting and reverting is a signal of quality. Rather, it demonstrates bad changes, as you said. This does not tell the model what good code is, just what it is not.

Your pricing page has a bug on it, the annual price is higher than the monthly price.

I'm seeing $30/m at annual and $38/m at monthly? (maybe already fixed, hard to tell)

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