GPT-5.6 Luna vs. GPT-6 Astra: Is a $1.20 Model Good Enough for Code Review?

We're using an even cheaper model (K2.7 right now) and our PRs cost more to review (still 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.

$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?

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.

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.

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"...