CodeRabbit finds GPT-6 Astra catches 20% more cross-file bugs than GPT-5.6 Sol

I see both OpenAI and Anthropic shipping models that are only a little better while costing about 2x the prior generation. That pricing strategy is interesting.

I agree Astra costs more than Sol for the same intelligence tasks, but I would not say that about Fable.

I expect this from scaling laws. Bigger models are smarter and cost more, though it is frustrating that vendors do not publish model sizes.

Once I factor in token efficiency, Astra and Sol come out at about the same price.

These models seem to have new capabilities, judging by the recent "Fable/Astra built GTA 6" videos. People will need different ways to work with them to get value from them.

This comparison is limited to CodeRabbit's code-review product, which makes it nearly irrelevant to me. I have used it as an engineer and former security researcher, and it created too much noise and friction to be worth it.

I do not get AI code-review tools when AI is already writing the code. They produce lots of noise and still miss the issues that matter most.

I have seen AI catch important issues, but only with enough context. Code review should be part of the pipeline and consume the task, specs, and test results, not just put a model on a diff.

The noise becomes bad enough that people start ignoring findings. Fable- and Sol-generated code is often close to correct, but its remaining failures are usually logical errors.

Astra feels much slower than GPT-5.6 Sol on the same tasks in my use. Maybe it reads more context.

I found Astra faster than Sol with both on medium reasoning for simple agentic coding. For open-ended work, Sol may miss something Astra finds, so a cheaper run can still have a worse outcome.

If Fable is a Sol-class model, I think Astra should be compared with Mythos instead.

My team has a product owner shipping vibe-coded frontend without review and now a stack of backend PRs totaling about 15k LOC. Developers use AI more slowly and carefully, but we do not know how to review this volume safely.

I would reject that 15k LOC dump and require a design document first. Review changes in 200 to 300 LOC pieces, because nobody will understand a huge AI-generated change well enough to maintain it.

I use AI to check whether logic works as expected, but I review system design myself. I also require AI-generated changes to be split into 1k to 2k LOC chunks or meaningful units so we can validate them incrementally.

I would invest in a test suite that validates the new behavior. AI can do adversarial review and apply fixes, but it should keep prior tests green.

We avoid reviewing AI-generated code by running code and infrastructure in isolated, forkable environments with realistic end-to-end tests. The review target becomes the tests, and our platform diffs them and replays browser interactions against a real backend.

The right answer depends on the codebase. Mature systems such as LLVM or WebKit still need humans reviewing LLM output closely, while disposable vibe-coded apps can let robots review robots.

I ran toy benchmarks and got Astra about 30% faster than Sol, at similar cost for the same outcome. Its token efficiency mattered even though the sticker price was 2.5x Sol's.