Every time I use opus these days I go shut up... you are not fable.. Hard to imagine how just three days with it changed how I saw LLM use.

Yeah, Opus/GPT need multiple rounds of reviews from each other to get to clean auto review. Fable was like, it is done and indeed… crickets in bot comments. ‘No issues’ galore.

Ditto on GLM 5.2 + DeepSeek V4 Flash combo.

For most important work (complex, cross-domain inquiries etc.), I still rely on Codex GPT 5.5 though.

GPT-5.5 has been really hard to beat imho. I've spent $$$ on Opus, Deepseek v4 Pro and recently started to dogfood GLM-5.2 (which is not bad) but I cannot really trust any of them (almost blind) like I can trust GPT-5.5. It gives me tremendous confidence. I cannot say the same for any of the others I mentioned.

how much does your setup cost you? just curious

>> I am on the opposite camp. Open models are starting to perform better. GPT 5.5 keeps on messing things up.

I'm working in a 600k+ LoC codebase that has complex domain-specific logic and lots of moving parts. I find that Codex 5.5 is pretty good at surgical fixes, but does not go out of its way to explore and figure out what those surgical fixes might break. So I only use it to work on parts of the system that are pretty isolated from everything else so that risk of regression is small.

Tracking model performance on Artificial Analysis makes me think these models are constantly optimized/tuned in some way or another. GPT 5.5 was scoring in the mid 60's when it was first released, now it's almost 10 points higher.

Maybe I'll know once I try it? Honestly, for small functions or methods, I don't think there's a huge difference between models. But the larger the code gets, the more noticeable the difference seems to be.

Personally, I think this kind of coding experience varies from person to person

sadly with all the labs benchmaxxing I feel like you just have to try the model for a while to really evaluate how good it is, especially for each individual use case

>zero coding benchmarks

"What gets measured gets managed"

They claim extreme performance on ExploitBench, which Mythos was touted as being incredible at. https://x.com/OpenAI/status/2070555278576439306

> ... why was GPT's solution superior and faster?

Not saying that's the case with OP, but I've found folks sometimes just rationalize it so [0] as they're paying top dollar for it (especially, when compared to may be less capable but affordable models).

[0] https://en.wikipedia.org/wiki/Choice-supportive_bias

When I searched for papers on using LLMs, I found that typically, you can have an LLM generate code and then ask it to find GitHub projects similar to that code. Then you can learn by looking at the pull requests and seeing how they structure things In the old days, if I wanted to understand why memory offsets, padding techniques, or data layout structures were written a certain way, I had to stare at a senior programmer's code all day or wait for them to reply. But LLMs, while they do flatter me, explain things at a level I can actually understand. And LLMs don't get annoyed.

There's a lot of tacit knowledge in programming.

-Why do you cut API boundaries this way? -Why do you change the order of struct fields? -Why do you deliberately insert padding?

Most of it depends on the background and context. Sometimes you add it, sometimes you don't. To understand this tacit knowledge, you need access to senior developers. But their attitude often depends on how promising the student is and what background they come from. On top of that, you don't have to rely on the respondent's mood, authority, or availability.

Programming is fundamentally a field that requires seniors. In my case, I had no such seniors at all. I learned to code by buying codebases from failed companies and studying them. My first job didn't hire me as an employee—they hired me as the CEO of a subcontracting company (because that was structurally more advantageous for the contract). So I wasn't given the patience to learn programming fundamentals gradually. I had to pay penalties if I failed. Most of the projects I worked on were the kind where failure meant bankruptcy for me. Naturally, there was no one to teach me.

Most of my knowledge comes from reverse-engineering the code I purchased.

People say LLM code contains falsehoods, but commercially sold code has always had falsehoods too. Honestly, if we're just talking ratios, LLM code has fewer falsehoods.

In that sense, I still think it's a matter of context. If LLM code is false, was human code ever really true? LLMs do lie. They generate plenty of incorrect code. But humans do the same thing. If a problem comes up, you just look it up then and there. For me, LLMs and humans aren't all that different.

No offense but have you considered the strong possibility that you're just holding it wrong? You're entitled to your opinion, but OP is hardly the first person to say something like this and is surrounded by tons of folks saying the exact same thing. Just because it sounds weird to you, doesn't mean it's not true.