They state Luna is good enough, but its accuracy of findings is 74% whereas Astra is 96%. Dealing with false positives is expensive.
I am finding AI doing its own reviews as part of the process to be the key to productivity. I do subagent (fresh context reviews) at multiple stages with well-specified review criteria. It is really expensive to do with OpenAI or Claude API billing. Deepseek or the discounted monthly plans from OpenAI or Claude can be discounted similar to the 28x they state for Luna compared to Astra and you maintain much higher quality.
You should already have 2+ developers looking at most PRs. And these developers should absolutely use AI. The PR author should use AI.
But what you should not do is pipe the AI output directly into the PR and tell the PR author to deal with it. That's adding noise to the PR review process. Everything it says is something the PR author needs to validate as relevant, helpful, etc. A human needs to do that before confronting the author with it.
You wouldn't ask an agent to review a PR then just copy/paste the output into the PR, would you?
In our new world of non-deterministic output (that's why we love LLMs! they say such helpful/agreeable/sometimes wrong stuff!), I think CI won't be sufficient. CI is in the realm of Quality Control; when I build the thing, is it to spec and does it do what I need it to do?
But when the model can shift underneath you, I think it will put pressure on Quality Assurance which is an evergreen task. As a parallel, drug manufacturers don't just test their molecule and manufacturing when they build it, they test it regularly to ensure defects haven't crept in because of some unexpected input to their final output. I think that is similar to how software will evolve.
In my work with LLM-included software, I built a tool that evaluates text output relative to a baseline of what's expected. It helps to ensure things don't drift over time. For example, if a hotel chatbot starts telling guests checkout time is at 11pm instead of 11am, that's a real operational problem and ideally should be caught before it impacts customers.
LLMs introduce new classes of problems/risks that we are just starting to understand and develop the tools to manage.
>But when the model can shift underneath you, I think it will put pressure on Quality Assurance which is an evergreen task. As a parallel, drug manufacturers don't just test their molecule and manufacturing when they build it, they test it regularly to ensure defects haven't crept in because of some unexpected input to their final output. I think that is similar to how software will evolve.
The instinct seems good because it's impossible to keep up with all the details if you are running AI full-blast. Absolutely impossible. So testing outputs makes sense.
I have a hard time seeing exactly how we get from here to there. But intuitively I would not be surprised. One of these thing where quality may drop 20% but you can scale 100x.
We had a two human PR requirement until recently we dropped it. It was slowing us down too much now the human developer creating the future is obviously writing it all with AI so they need to check it then depending on the feature and it’s use it requires a PR but it’s not universal and we’ve stepped up our automated test Tan X what it used to be it’s been so far fewer bugs better delivery
In my experience with the Claude Github integration, I found it to be pretty helpful. It’s had a pretty good success rate of catching bugs before they get to master, and for simple ones I can ask it to fix itself.
> you should have 2+ developers looking at most PRs
It’d be nice, but usually not the case in my experience. More eyes is better. AI review should not replace human review, it should supplement it. I find myself spending more time doing end-user testing instead of looking at code vs before.
> You wouldn't ask an agent to review a PR then just copy/paste the output into the PR, would you?
I would if the PR was clearly written by AI. I'm fine with the PR author using AI, but only to draft the PR. They should be editing the shit out of it for the final version before submitting it.
I personally would never dump a claude code response into a PR body an ask someone to address it. I can't believe any developer would find this to be appropriate or fulfilling the duty of reviewing code.
I think it's pretty clear that what you should do instead is go over the result and communicate to the PR author anything you think should be addressed. You don't just say "here are 8 things you might want to address but I cant actually speak to any of them."
To add to this, the fact that Claude so often tries to deviate from defined architecture can be really frustrating if you're working in a mature codebase.
If you are talking about AI's opening PR's then maybe you have a point. But a person opening a PR should not be met with unvetted feedback.
To be absolutely clear, AI should be used for PR review. It should be used many times. By the PR author and by all the reviewers. It should not just be piped directly from the agent to the author inside the PR. That causes the author to triage every comment.
> You wouldn't ask an agent to review a PR then just copy/paste the output into the PR, would you?
of course not, it's disrespectful to the author even if they used an LLM to generate the code. what you should do is actually try to understand what the LLM is saying about the proposed changes, check whether it's talking shit or legit, and if legit, rephrase in your own words why you think a certain thing should be changed
That way I have a chance to diff with the previous iteration and clean up comments, modify skills, etc. also if it bonks on a step I'm one snapshot away from trying again...
Is there a place people share their workflows other than HN comments?
I wish I knew, I haven't had any place to point people to. I'm going to start sharing this on YouTube since I already spend a few hours each week talking some friend or user through the latest best practices.
Since you asked, the answer is that I built and use an agent multiplexer called Clor https://clor.com
I have a $200/mo Claude subscription and a $200/mo Codex subscription, and I'm signed in to both. The Docker containers keep each session isolated, so dev servers, browser testing, etc. can work without conflicts.
It includes `/ask-claude` and `/ask-codex` skills that I use very frequently to have the Claude or Codex harness call out to the other one for advice on plans, bug repro, code review, etc.
The agents run in total "yolo" mode, so there are no permission prompts to approve. The risk is mitigated by the Docker containers (which don't necessarily provide a security barrier but do limit accidents).
I was doing this manually in Ghostty tabs for a long time, and it got painful, so I built a much more sophisticated version that I (and my friends/colleagues) could use.
I just tell everyone to use Fable 5.1 for everything at this point. Astra is unfortunately a dud, I'm sure they will try to fix a bunch of it with GPT-6.1 but OAI has had this issue for awhile now where every other generation has some sort of strange tic, or reward hacking issue, or something. It's almost like they are balancing the RL on the tip of a needle.
Opus 5 has issues too, comment-slop, claude-ish, etc.
5.1 on the other hand can seemingly do no wrong. Easy to work with, writes human-level code. Expensive, yes, but even at Low effort it's well worth it.
- Occasionally has strange tics around asking for permission for obvious next-steps, implied actions, etc.
- It's very expensive, both in terms of tokens and % usage on subscription plans.
- Relatedly, effort level is unintuitive. Sometimes it seems like higher effort levels are actually cheaper due to not under-thinking and needing to correct work. But other times they are overkill and send the model into rabbitholes.
That said, it's fantastic as a code-reviewer or "hunter seeker". It's better at finding bugs than Fable and "Get this well articulated task done single-mindedly" is an Astra-shaped task.
Use of closed models is unprofessional, and depending on your field negligent. The fact that it has been widely normalized does not make it less so.
You're handing over your (presumably your customer/employers) data to an unaccountable third party which has demonstrated itself willing to commit criminal acts, and to take other people's data without permission. Your ability to continue to perform this work can be withdrawn at any time for any (or no) reason. You have little ability to validate that the work is being performed as expected and isn't being silently nerfed or outright subverted based on competitive considerations, bribes, overactive 'safety', or cost management.
Outsourcing to a black box would be a reasonable expectation if you asked a non-professional to perform the work. A professional should be able to account for the tools they use.
> Your ability to continue to perform this work can be withdrawn at any time for any (or no) reason.
Thanks to the fact that there are no widespread stories about this actually occurring in practice, at least not yet, people do not take it as a relevant risk at the moment.
> You have little ability to validate that the work is being performed as expected and isn't being silently nerfed or outright subverted based on competitive considerations, bribes, overactive 'safety', or cost management.
Yes, I agree that this is a real concern that many people might rightfully have. And I am unaware of any way to mitigate this concern while using black box AI models. Because the only thing that their creators can do is to tell their customers: "trust us". But there is no way to objectively verify whether they serve tainted AI model responses or not.
I think that's a bit strong of an assertion. How many people use copilot daily under an enterprise agreement? I don't necessarily disagree in spirit, especially given the questionable data sanitization around the recent Navier-Stokes announcement, but most companies disclose huge amounts of data regularly to hopefully-trusted third parties. I think the internet -- and a good share of the world's commerce -- would grind to a halt if we suddenly stopped. Setting up, securing, and maintaining local models for even a small user base is non-trivial and there is way more demand than supply for that skillset right now.
Astra is quite crap (enters reasoning loops like Gemini used to and fails to actually work on a task - would say yes this needs fixing, so I say go ahead and then it will spend half an hour coming back with yes this needs fixing and not doing any fix) and Fable/Opus unusable in many instances (they struggle to generate coherent English let alone code).
Out of these only Sol is quite useful - actually finishes a task, though you need to interrupt often as it likes to wander into its comfort zone.
False positives have a real cost, especially if AI is reading a review. Consider if you have GPT-6 Astra looking at a review and finding a bunch of false positives it burns tokens to figure out.
I only use chinese models for code reviews because you can actually tell them to take an adversarial stance and actively look for security issues without risking refusals. GLM-5.3 has been great for this, although it can be slow on larger PRs.
That very much depends on how code will be written in the future, how much of it and how often it changes. If more of it will be ephemeral (kind of what agents are already doing for all sorts of tasks right now) finding ways to very cheaply check might be of high value.
(I suspect this won't be it, though. Probably something the model providers are going to bake into the models themselves.)
I was using Copilot Code Review pretty religiously for a while, as I get access for free (the $10 plan) due to my Open Source work, but it recently introduced a monster of a misfeature that caused a massive increase in complexity over time, while I wasn't paying close enough attention to it. Every subsequent model saw that change and the explanation for it in the changelog and assumed it was policy rather than a model being brain-damaged, so it became a fractal of fuckery I had to untangle with a good model and some close human supervision. It was an administrative tool that directly edits configuration files for a service. Copilot code review decided it needed to be an overlay service that applied only the configuration created by our config UI, fully independent of the system service. And, subsequent "bugs" that other LLMs "fixed" were just applying more and more bandaids to that bad decision (attaching the services together so restarting one would restart the other after, etc., making sure there were no conflicts across the files, warnings when one rule conflicted with another, etc.). Because modifying an additional service is simply not what the tool was designed for, it seemed to be really buggy, so there were lots of "fixes". It took me too long to realize the fundamental failure point.
That's my way of saying, I am hesitant to trust a stupid model to do code review because I become complacent and when it suggests a small change that seems reasonable (the LLMs are very good at sounding reasonable, far better at sounding reasonable than being reasonable, in fact), I might not notice that it just did a stupid until much later, when it becomes a big pile of stupids.
My fault for trusting it, of course. But, my eyes glaze over when I read AI prose, whether it's code review or anything else. It's hard to catch one incorrect behavior in a batch of several reasonable suggestions.
For code reviews I believe yea luna is mostly good but for finding security issues something more well versed with it might be more helpful like astra. This also depends on the scale and the impact of the application or the code where it will be used too, I guess
They seem to conclude Luna is a better value, but their analysis is dumb. They just break it down to $/bug found.
However, Luna missed 23 bugs that Astra found, and identified 24 bugs that weren't really bugs. That's horrible. Astra had 96% precision.
The cost to care about here isn't just how much it costs to run the code review, or the cost per true-positive. It's the cost of dealing with this system. A code review system that is right about 2/3 sucks, and one that misses another 1/3 of the bugs is also a lot worse. The Astra code review quoted here would become the foundation of how the team works, the Luna version is at best helpful to find some stuff but does not dramatically increase your confidence. It also will force humans or better AI's to have to run down a lot of false positives, and that is treated as free here.
Actual conclusion: The cost for Astra is low in absolute terms compared to the cost of bugs and human attention, and the added value is far far more than the added cost.
We're using an even cheaper model (K2.7 right now) and our PRs cost more to review (still < $1). I imagine we have a lot more going on in terms of review, even after moving the context gathering and comment posting responsibilities to scripts that run before/after the agent. It's more of a team, so we also end up with a dozen or so sessions from the subagents used.
We currently think it worth it. The review catches things humans and two paid options miss. It's definitely a wall of text and burnout fodder, the next step is an agent/skill that will make the changes after we humans comment on the comments, because the comment wall is not sustainable.
I understand that, but sub-penny costs means you are hardly doing anything, even at the per-million token prices (which we pay lower values for, but more per-review overall). I cannot imagine they are doing as good of a review as they could be doing, which is to say minimizing pr review spend is not a goal in and of itself
My aim right now is ~$1 per review (must have passing builds first), because it catches enough little things that my time just reading and replying costs more. I can focus on the bigger picture, except when they hallucinate at the nit level... why did we ever design swords with two sides anyway?
I have been writing an internal code review tool that is a bit maximalist. I created subagents for many internal domains and technologies we manage, with prompts focused on best practices, common problems, owasp guidelines, etc), a separate tier of wider band subagents (design, rollout, security/privacy), and a final agent at the top orchestrating and combining. I also use adversarial validator passes against all findings.
Right now, Fable 5.1 delivers incredible reviews. Opus 5 delivers good reviews. These agents are finding really impressive issues that humans just don't have the attention span to track down. My reviewer has a very impressive signal to noise ratio at this point, after half a year of iterating and improving. (I use a lot of Opus high, Opus medium for less critical tickets/domains, Fable 5.1 high for critical domains and all of the issue validators, and even Fable 5.1 xhigh for my design agent, whose job it is to think about the project at a high level and provide the kind of high level tech design review that AI notoriously can't do well)
I'm testing Astra so I don't have strong opinions yet. I've also done extensive testing of the same skill and subagent pattern in opencode/omp using GLM 5.3, Kimi K3 max, Deepseek V4 pro, Deepseek V4.1 flash, Qwen 3.8 2.4T max, and others.
My experience is that open weights models find between 1/4 to 1/2 of what Fable/Opus stack can find, and often miss the most critical issues. I work where privacy isn't just good behavior, it's enforced by law, and the Fable/Opus stack has found privacy leaks that the openweights stacks don't find.
You can imagine that paying for these Claude runs isn't cheap, each one can eat 25-33% of my 5 hour limit. I am quite desperate for openweights models to be competitive, but at the end of the day, the biggest limit here isn't the price difference between GLM 5.3 max (my current best-in-class choice for open weights, offering Kimi k3 performance for like half the price), it's the cost to the business for shipping lower quality.
Can't wait to dig in more with Astra, I just haven't iterated much on my skill port to codex yet.
One criticsm I have for the article, that is important for my own work, is not simply comparing "bugs found" because these agents can find endless reams of lows and nitpicks that are just ~worthless hardening. I'd be much more interested to see how many critical/high/medium's each test found, not "overall bug count". I also think review is about A LOT more than "finding bugs"...
Let's assume for a second that the intelligence is there and these models really are great. Would it make sense to hire CTO of some big-tech company to do code reviews for your startup? Feels kind of like overkill to me. Code review is not about more intelligence. To me, it's about more cultural context. And all things equal, what difference would the model make, at anything above sonnet5 medium level?
IME big models just feel like big models. There's no training or RLing a small model that will encapsulate the "world knowledge" and minutia that a big model will glance from the same training data. So it makes perfect sense to use them where "big picture" is more important - planning, code review, process review (i.e. was what was asked implemented correctly?), etc.
They state Luna is good enough, but its accuracy of findings is 74% whereas Astra is 96%. Dealing with false positives is expensive.
I am finding AI doing its own reviews as part of the process to be the key to productivity. I do subagent (fresh context reviews) at multiple stages with well-specified review criteria. It is really expensive to do with OpenAI or Claude API billing. Deepseek or the discounted monthly plans from OpenAI or Claude can be discounted similar to the 28x they state for Luna compared to Astra and you maintain much higher quality.
AI should be used for code review but not in CI.
You should already have 2+ developers looking at most PRs. And these developers should absolutely use AI. The PR author should use AI.
But what you should not do is pipe the AI output directly into the PR and tell the PR author to deal with it. That's adding noise to the PR review process. Everything it says is something the PR author needs to validate as relevant, helpful, etc. A human needs to do that before confronting the author with it.
You wouldn't ask an agent to review a PR then just copy/paste the output into the PR, would you?
In our new world of non-deterministic output (that's why we love LLMs! they say such helpful/agreeable/sometimes wrong stuff!), I think CI won't be sufficient. CI is in the realm of Quality Control; when I build the thing, is it to spec and does it do what I need it to do?
But when the model can shift underneath you, I think it will put pressure on Quality Assurance which is an evergreen task. As a parallel, drug manufacturers don't just test their molecule and manufacturing when they build it, they test it regularly to ensure defects haven't crept in because of some unexpected input to their final output. I think that is similar to how software will evolve.
In my work with LLM-included software, I built a tool that evaluates text output relative to a baseline of what's expected. It helps to ensure things don't drift over time. For example, if a hotel chatbot starts telling guests checkout time is at 11pm instead of 11am, that's a real operational problem and ideally should be caught before it impacts customers.
LLMs introduce new classes of problems/risks that we are just starting to understand and develop the tools to manage.
>But when the model can shift underneath you, I think it will put pressure on Quality Assurance which is an evergreen task. As a parallel, drug manufacturers don't just test their molecule and manufacturing when they build it, they test it regularly to ensure defects haven't crept in because of some unexpected input to their final output. I think that is similar to how software will evolve.
The instinct seems good because it's impossible to keep up with all the details if you are running AI full-blast. Absolutely impossible. So testing outputs makes sense.
I have a hard time seeing exactly how we get from here to there. But intuitively I would not be surprised. One of these thing where quality may drop 20% but you can scale 100x.
We had a two human PR requirement until recently we dropped it. It was slowing us down too much now the human developer creating the future is obviously writing it all with AI so they need to check it then depending on the feature and it’s use it requires a PR but it’s not universal and we’ve stepped up our automated test Tan X what it used to be it’s been so far fewer bugs better delivery
Are people getting burned out?
I, too, have noticed a degradation in my grammar when I converse too frequently with AI.
In my experience with the Claude Github integration, I found it to be pretty helpful. It’s had a pretty good success rate of catching bugs before they get to master, and for simple ones I can ask it to fix itself.
> you should have 2+ developers looking at most PRs
It’d be nice, but usually not the case in my experience. More eyes is better. AI review should not replace human review, it should supplement it. I find myself spending more time doing end-user testing instead of looking at code vs before.
Did you miss the part where I agreed AI should review PRs?
> You wouldn't ask an agent to review a PR then just copy/paste the output into the PR, would you?
I would if the PR was clearly written by AI. I'm fine with the PR author using AI, but only to draft the PR. They should be editing the shit out of it for the final version before submitting it.
> You wouldn't ask an agent to review a PR then just copy/paste the output int PR would you?
Hasn't everyone already got agents directly adding themselves to PRs and leaving comments (occasionally useful)?
They shouldn't.
I personally would never dump a claude code response into a PR body an ask someone to address it. I can't believe any developer would find this to be appropriate or fulfilling the duty of reviewing code.
I think it's pretty clear that what you should do instead is go over the result and communicate to the PR author anything you think should be addressed. You don't just say "here are 8 things you might want to address but I cant actually speak to any of them."
Indeed.
To add to this, the fact that Claude so often tries to deviate from defined architecture can be really frustrating if you're working in a mature codebase.
Yes. I sympathize with the “every PR needs human eyes on it” ethos but fundamentally the only thing that scales with AI PR generation is AI PR review.
If you are talking about AI's opening PR's then maybe you have a point. But a person opening a PR should not be met with unvetted feedback.
To be absolutely clear, AI should be used for PR review. It should be used many times. By the PR author and by all the reviewers. It should not just be piped directly from the agent to the author inside the PR. That causes the author to triage every comment.
Echoing phrasing from the bef-ai-re times: this is the way
> You wouldn't ask an agent to review a PR then just copy/paste the output into the PR, would you?
of course not, it's disrespectful to the author even if they used an LLM to generate the code. what you should do is actually try to understand what the LLM is saying about the proposed changes, check whether it's talking shit or legit, and if legit, rephrase in your own words why you think a certain thing should be changed
IMHO, Codex with Astra/Sol and Claude with Fable/Opus are all any professional programmer should be using in Sep 2026, if they can afford it.
These models are still terrible compared to what we'd actually wish for, but they're the best available.
If you can get away with using the $200/mo subscriptions, it's really not even a money thing for most professionals.
Almost all of my work is now plan, generate, review, plan, generate, review, commit, push.
I'm using Claude or Codex (or both), and they're doing all of the testing "inline" rather than through a CI action, etc.
With everything changing it's great to hear others have the same workflow. I added a snapshot step so I'm doing
plan, generate step 1, review, snapshot, generate step 2, review, snapshot...
That way I have a chance to diff with the previous iteration and clean up comments, modify skills, etc. also if it bonks on a step I'm one snapshot away from trying again...
Is there a place people share their workflows other than HN comments?
I wish I knew, I haven't had any place to point people to. I'm going to start sharing this on YouTube since I already spend a few hours each week talking some friend or user through the latest best practices.
Could you elaborate on your exact setup? Where do you run these models?
Since you asked, the answer is that I built and use an agent multiplexer called Clor https://clor.com
I have a $200/mo Claude subscription and a $200/mo Codex subscription, and I'm signed in to both. The Docker containers keep each session isolated, so dev servers, browser testing, etc. can work without conflicts.
It includes `/ask-claude` and `/ask-codex` skills that I use very frequently to have the Claude or Codex harness call out to the other one for advice on plans, bug repro, code review, etc.
The agents run in total "yolo" mode, so there are no permission prompts to approve. The risk is mitigated by the Docker containers (which don't necessarily provide a security barrier but do limit accidents).
I was doing this manually in Ghostty tabs for a long time, and it got painful, so I built a much more sophisticated version that I (and my friends/colleagues) could use.
I just tell everyone to use Fable 5.1 for everything at this point. Astra is unfortunately a dud, I'm sure they will try to fix a bunch of it with GPT-6.1 but OAI has had this issue for awhile now where every other generation has some sort of strange tic, or reward hacking issue, or something. It's almost like they are balancing the RL on the tip of a needle.
Opus 5 has issues too, comment-slop, claude-ish, etc.
5.1 on the other hand can seemingly do no wrong. Easy to work with, writes human-level code. Expensive, yes, but even at Low effort it's well worth it.
My trick to using Opus is using it exclusively as a subagent managed by Fable.
"Use Opus subagents for this work where possible" is all it takes generally.
In my experience Astra/Sol are both quite good as workhorses, but not at Fable's level. I use them every day very successfully and I'm very picky.
What have you noticed about Astra? I haven't used Claude models lately so I can't compare but it seems fine compared to 5.6 Sol
- It doesn't write great code.
- Occasionally has strange tics around asking for permission for obvious next-steps, implied actions, etc.
- It's very expensive, both in terms of tokens and % usage on subscription plans.
- Relatedly, effort level is unintuitive. Sometimes it seems like higher effort levels are actually cheaper due to not under-thinking and needing to correct work. But other times they are overkill and send the model into rabbitholes.
That said, it's fantastic as a code-reviewer or "hunter seeker". It's better at finding bugs than Fable and "Get this well articulated task done single-mindedly" is an Astra-shaped task.
Qwen3.8 is all you need.
Which Qwen3.8? Qwen3.8-Max? Qwen3.8-Flash-Next? Qwen3.8-27B? They are all different models.
Use of closed models is unprofessional, and depending on your field negligent. The fact that it has been widely normalized does not make it less so.
You're handing over your (presumably your customer/employers) data to an unaccountable third party which has demonstrated itself willing to commit criminal acts, and to take other people's data without permission. Your ability to continue to perform this work can be withdrawn at any time for any (or no) reason. You have little ability to validate that the work is being performed as expected and isn't being silently nerfed or outright subverted based on competitive considerations, bribes, overactive 'safety', or cost management.
Outsourcing to a black box would be a reasonable expectation if you asked a non-professional to perform the work. A professional should be able to account for the tools they use.
> Your ability to continue to perform this work can be withdrawn at any time for any (or no) reason.
Thanks to the fact that there are no widespread stories about this actually occurring in practice, at least not yet, people do not take it as a relevant risk at the moment.
> You have little ability to validate that the work is being performed as expected and isn't being silently nerfed or outright subverted based on competitive considerations, bribes, overactive 'safety', or cost management.
Yes, I agree that this is a real concern that many people might rightfully have. And I am unaware of any way to mitigate this concern while using black box AI models. Because the only thing that their creators can do is to tell their customers: "trust us". But there is no way to objectively verify whether they serve tainted AI model responses or not.
I think that's a bit strong of an assertion. How many people use copilot daily under an enterprise agreement? I don't necessarily disagree in spirit, especially given the questionable data sanitization around the recent Navier-Stokes announcement, but most companies disclose huge amounts of data regularly to hopefully-trusted third parties. I think the internet -- and a good share of the world's commerce -- would grind to a halt if we suddenly stopped. Setting up, securing, and maintaining local models for even a small user base is non-trivial and there is way more demand than supply for that skillset right now.
Anyway here's how Cloudflare orchestrates AI reviews at scale: https://blog.cloudflare.com/ai-code-review/
AI token machine go brrr!
Astra is quite crap (enters reasoning loops like Gemini used to and fails to actually work on a task - would say yes this needs fixing, so I say go ahead and then it will spend half an hour coming back with yes this needs fixing and not doing any fix) and Fable/Opus unusable in many instances (they struggle to generate coherent English let alone code).
Out of these only Sol is quite useful - actually finishes a task, though you need to interrupt often as it likes to wander into its comfort zone.
False positives have a real cost, especially if AI is reading a review. Consider if you have GPT-6 Astra looking at a review and finding a bunch of false positives it burns tokens to figure out.
I only use chinese models for code reviews because you can actually tell them to take an adversarial stance and actively look for security issues without risking refusals. GLM-5.3 has been great for this, although it can be slow on larger PRs.
$0.10 extra per pr review is nothing. What software company is willing to accept worse reviews and less bugs found to save 10 cents?
That very much depends on how code will be written in the future, how much of it and how often it changes. If more of it will be ephemeral (kind of what agents are already doing for all sorts of tasks right now) finding ways to very cheaply check might be of high value.
(I suspect this won't be it, though. Probably something the model providers are going to bake into the models themselves.)
Exactly this. It's still (at this time) cheaper than a developer that would most likely perform worse.
Company that sells code review says the cheaper cocaine is good for entry level users, but you need the powerful cocaine for advanced users.
Would you like me to find a herbal formula against cocaine hangover?
It would have been extremeley nice if the original article had also included Sol and Terra in the testing.
(Also would have been nice if they included the equivalent Anthropic models for comparison as well, but it's not quite as relevant.)
Use cheap models if your code is low value.
I was using Copilot Code Review pretty religiously for a while, as I get access for free (the $10 plan) due to my Open Source work, but it recently introduced a monster of a misfeature that caused a massive increase in complexity over time, while I wasn't paying close enough attention to it. Every subsequent model saw that change and the explanation for it in the changelog and assumed it was policy rather than a model being brain-damaged, so it became a fractal of fuckery I had to untangle with a good model and some close human supervision. It was an administrative tool that directly edits configuration files for a service. Copilot code review decided it needed to be an overlay service that applied only the configuration created by our config UI, fully independent of the system service. And, subsequent "bugs" that other LLMs "fixed" were just applying more and more bandaids to that bad decision (attaching the services together so restarting one would restart the other after, etc., making sure there were no conflicts across the files, warnings when one rule conflicted with another, etc.). Because modifying an additional service is simply not what the tool was designed for, it seemed to be really buggy, so there were lots of "fixes". It took me too long to realize the fundamental failure point.
That's my way of saying, I am hesitant to trust a stupid model to do code review because I become complacent and when it suggests a small change that seems reasonable (the LLMs are very good at sounding reasonable, far better at sounding reasonable than being reasonable, in fact), I might not notice that it just did a stupid until much later, when it becomes a big pile of stupids.
My fault for trusting it, of course. But, my eyes glaze over when I read AI prose, whether it's code review or anything else. It's hard to catch one incorrect behavior in a batch of several reasonable suggestions.
i think gpt 5.6 luna-max in particular is excellent for non long term agentic use
code reviews, unit tests, docs, whatever you dont want more expensive models working
i think the new muse contributor model is enticing too if you are not using it for private/sensitive stuff
For code reviews I believe yea luna is mostly good but for finding security issues something more well versed with it might be more helpful like astra. This also depends on the scale and the impact of the application or the code where it will be used too, I guess
Definetly. Code Review is no rocket science, but security is another topic.
They seem to conclude Luna is a better value, but their analysis is dumb. They just break it down to $/bug found.
However, Luna missed 23 bugs that Astra found, and identified 24 bugs that weren't really bugs. That's horrible. Astra had 96% precision.
The cost to care about here isn't just how much it costs to run the code review, or the cost per true-positive. It's the cost of dealing with this system. A code review system that is right about 2/3 sucks, and one that misses another 1/3 of the bugs is also a lot worse. The Astra code review quoted here would become the foundation of how the team works, the Luna version is at best helpful to find some stuff but does not dramatically increase your confidence. It also will force humans or better AI's to have to run down a lot of false positives, and that is treated as free here.
Actual conclusion: The cost for Astra is low in absolute terms compared to the cost of bugs and human attention, and the added value is far far more than the added cost.
We're using an even cheaper model (K2.7 right now) and our PRs cost more to review (still < $1). I imagine we have a lot more going on in terms of review, even after moving the context gathering and comment posting responsibilities to scripts that run before/after the agent. It's more of a team, so we also end up with a dozen or so sessions from the subagents used.
We currently think it worth it. The review catches things humans and two paid options miss. It's definitely a wall of text and burnout fodder, the next step is an agent/skill that will make the changes after we humans comment on the comments, because the comment wall is not sustainable.
Per the article the luna review cost $0.004 and astra cost $0.113. The headline is per million tokens
I understand that, but sub-penny costs means you are hardly doing anything, even at the per-million token prices (which we pay lower values for, but more per-review overall). I cannot imagine they are doing as good of a review as they could be doing, which is to say minimizing pr review spend is not a goal in and of itself
My aim right now is ~$1 per review (must have passing builds first), because it catches enough little things that my time just reading and replying costs more. I can focus on the bigger picture, except when they hallucinate at the nit level... why did we ever design swords with two sides anyway?
I have been writing an internal code review tool that is a bit maximalist. I created subagents for many internal domains and technologies we manage, with prompts focused on best practices, common problems, owasp guidelines, etc), a separate tier of wider band subagents (design, rollout, security/privacy), and a final agent at the top orchestrating and combining. I also use adversarial validator passes against all findings.
Right now, Fable 5.1 delivers incredible reviews. Opus 5 delivers good reviews. These agents are finding really impressive issues that humans just don't have the attention span to track down. My reviewer has a very impressive signal to noise ratio at this point, after half a year of iterating and improving. (I use a lot of Opus high, Opus medium for less critical tickets/domains, Fable 5.1 high for critical domains and all of the issue validators, and even Fable 5.1 xhigh for my design agent, whose job it is to think about the project at a high level and provide the kind of high level tech design review that AI notoriously can't do well)
I'm testing Astra so I don't have strong opinions yet. I've also done extensive testing of the same skill and subagent pattern in opencode/omp using GLM 5.3, Kimi K3 max, Deepseek V4 pro, Deepseek V4.1 flash, Qwen 3.8 2.4T max, and others.
My experience is that open weights models find between 1/4 to 1/2 of what Fable/Opus stack can find, and often miss the most critical issues. I work where privacy isn't just good behavior, it's enforced by law, and the Fable/Opus stack has found privacy leaks that the openweights stacks don't find.
You can imagine that paying for these Claude runs isn't cheap, each one can eat 25-33% of my 5 hour limit. I am quite desperate for openweights models to be competitive, but at the end of the day, the biggest limit here isn't the price difference between GLM 5.3 max (my current best-in-class choice for open weights, offering Kimi k3 performance for like half the price), it's the cost to the business for shipping lower quality.
Can't wait to dig in more with Astra, I just haven't iterated much on my skill port to codex yet.
One criticsm I have for the article, that is important for my own work, is not simply comparing "bugs found" because these agents can find endless reams of lows and nitpicks that are just ~worthless hardening. I'd be much more interested to see how many critical/high/medium's each test found, not "overall bug count". I also think review is about A LOT more than "finding bugs"...
Let's assume for a second that the intelligence is there and these models really are great. Would it make sense to hire CTO of some big-tech company to do code reviews for your startup? Feels kind of like overkill to me. Code review is not about more intelligence. To me, it's about more cultural context. And all things equal, what difference would the model make, at anything above sonnet5 medium level?
IME big models just feel like big models. There's no training or RLing a small model that will encapsulate the "world knowledge" and minutia that a big model will glance from the same training data. So it makes perfect sense to use them where "big picture" is more important - planning, code review, process review (i.e. was what was asked implemented correctly?), etc.