GPT-5.6 Luna Finds 75% of GPT-6 Astra's Bugs for 3.6% of the Cost
GPT-5.6 Luna vs. GPT-6 Astra: Is a $1.20 Model Good Enough for Code Review?
A head-to-head test on 50 public pull requests found GPT-5.6 Luna caught 69 verified bugs for $0.20, while GPT-6 Astra caught 92 for $5.66. Luna was 28x cheaper per review and 20x cheaper per verified bug, but its precision was 74% versus Astra's 96%, and it missed 15 of 24 security bugs. Luna is good enough for everyday correctness bugs, but not for authentication or permission code on its own.
Luna found 75% as many verified bugs as Astra for 3.6% of the money. Per verified bug, Astra cost 20x more.
- nonethewiser
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?
- jacobgold
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.
- InsideOutSanta
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.
- StevenWaterman
$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?
- CharlieDigital
I found Luna and even 5.4-mini to be quite good at code review provided a few things:
1. Run it in multiple cycles, only on the diff, and only emit a few findings at a time.
2. Give it a memory so each cycle, it knows the previous finding to check if it's been fixed.
3. Give it access to canonical docs that encode your human reviewer heuristics. I exposed these as tool calls so they could be tracked via telemetry.
4. Run multiple reviewers, each with a tight focus. Security, performance, structural, database, etc. Each a separate prompt and persona. Additionally, we had file activation filters so the FE React reviewer didn't activate on BE only changes.
Luna and 5.4-mini with no reasoning were exceptionally fast and almost always found issues with code produced by Opus and Fable.
Default prompts for the curious (these are templates deployed by default, but customizable).
Performance: https://github.com/zeeq-ai/zeeq-app/blob/main/src/backend/Ze...
Structural: https://github.com/zeeq-ai/zeeq-app/blob/main/src/backend/Ze...
(Keep in mind each agent also has tools to access and reference external docs.)
- gregwebs
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.
- rektomatic
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.
- amluto
This article is missing an incredibly important detail: what is the harness doing?
I get remarkably good results using any recent OpenAI model using the codex-rs harness pointing at a built checkout of the PR. The models use the available tools (i.e. the shell) to understand the repo. I get some false positives and some false negatives, but I don’t believe for a second that I would get comparable performance using a dramatically less capable harness. (Also, the models read the short AGENTS.md for some context as to what’s going on. The prompt I use is about one sentence. I don’t bother with the built in review tool.)