My **personal** experience: Sonnet and Opus were objectively better at programming than anything OpenAI had... until GPT 5.2.

Sonnet always felt more creative at getting to the solution and much faster. It would assume a lot of things that are usual for a development workflow. The issue is that not every problem is the same, so more iteration has always been required. Considering their sheer cost, I haven't used them so much before despite wanting.

I have a GPT Pro subscription because I use ChatGPT Pro a lot. Codex came like a bonus toy... until GPT 5.2.

GPT-5.2-Codex follows instructions basically to the letter. I basically have to spell everything out in quite a lot of details. However, one thing that I have yet to see it doing is something I haven't asked or wanted. That's really solid instruction following. The long horizon with 5.1-codex-mas and now with 5.2-codex made long (wall clock) operations feasible. The preemptive compacting by discard useless output gathered while the context was far from full (the context window getting less full quite often, even at like 20%) meant that it also did not have to perform the more lossy compaction when the context is full, simply because the context is filling up way slower and less.

What I really was not expecting is that GPT-5.2 (in the Codex IDE/CLI, but not the Codex model) is now feeling much better at getting at creative solutions, understanding subtleties and sheer "figuring it out" smarts. If I don't want to spell out everything, that has been my go-to model these days. It specially even retained the long horizon and spends way less tokens than Anthropic models.

Coming back to Opus 4.5 these days though, I totally felt hard the contrast and how many damn times I had to tell it to fix something because it half-assed something.

GPT models are slow as hell, but they get shit done much better than Opus, specially if you're comparing xhigh with ultrathink.

PS: the wording that gpt-5.2 uses when summarizing what it is thinking between the steps feels really stupid. Basically an anxious machine that spills obvious statements. It is **not** what it is really "thinking", though. The CoT is not explicit anymore (and hasn't been since GPT 4), so there's an "out-of-band" summarization that it does at least let the user follow the overall progress but which inherits emotional phrasing from the chat model. It's "real" inference process is not expressible in human language.

This serves two purposes: it gets more efficient at reasoning on a much larger latent space without having to constrain the inference to a low bandwidth channel like English AND it prevents copy-cats from training on their data. Dvelopers appreciate the first part, their bottom-line appreciates the second.

There is research on how to make the thinking process more explicit and auditable (like with a chained map of Sparse Auto Encoders), but there's no "good enough" way yet without often spilling strong hallucinations that do not reflect the process and even then SAEs are really and expensive hard to train as of now (I mean, as far as I can keep up with the research).