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
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.
> 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.
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.
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.
Company creates a benchmark. Same company is best in that benchmark.
Story as old as time.
>optimize for thing
>thing gets optimized
A dishonest AI company? Well, I never!
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.
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'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.
Cmd+F - "Overfitting"...nothing.
Nope, no mention of how they do anything to alleviate overfitting. These benchmarks are getting tiresome.
Call for pricing.
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.
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.
> 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?
coderabbit being the worst while (presumeably) advertising the most seems to be check out at least, wouldn't believe the recall % seems bogus.
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)