Then the incremental improvements did, in my experience, cross some kind of threshold in late 2025 where the things became useful. It is of course anecdotal and personal judgment. But I asked LLMs to implement a small feature in my codebase (my usual test) and finally it produced code I was happy with. They've also been able to locate and diagnose a problem based on logs. In my view it's now a markedly different level of capability than we had a year ago, though I would call the previous two years equally useless.
Why would you attempt to use GPT 5.3 to generate code today and form an opinion on that basis?
I do not think it is even still available in Codex, I believe it only has the smaller, distilled GPT 5.3 Codex Spark.
How does GPT-5.6 Sol or Claude Fable 5 or Claude Opus 5 do on that Cinema 4D code?
Even 3.7. I remember when it came out and people were claiming that that was now the model that was going to replace engineers. Cue Fable years later and people still claim that this one is the one.
Yes, as a product gradually improves there will always be many people for whom version X didn't work well for them and version X+1 does. It turns out that Opus 4.5 and GPT 5.1 were larger than average improvements that cross that threshold for a significant number of people.
My point is these things only get truly better in anecdotes. The ways in which they fail is yet to change.
If your claim is that there's no substantive difference between Sonnet 3.5 and Fable, then we live in very different worlds.
“But it’s different this time” - several people, several times over the last couple of years.
This is not at all a dig at you, I’m very sorry if it reads that way. My point is these things only get truly better in anecdotes. The ways in which they fail is yet to change. Just yesterday I had gpt 5.3 generate completely awful code for the Cinema 4D Python API. Also an anecdote. But for all of the people saying they are truly intelligent and truly reason, they still make obvious mistakes, write around problems, fail entirely at architectural decisions, fail at random, generate FAR too much code.
And no amount of harnesses, methodologies, loops make much of a difference. If you listen to people on the internet they say it’s all working. You listen to people on the job and they mostly say it’s creating tech debt and a review bottleneck. Also burnout, so much burnout.
I think LLMs are mediocre. I think it’s fine they’re mediocre. You can work with low expectations. But the hype cycles are so tiresome.