It wants things beyond what the mortals (us) know to reach for. It's not good at explaining itself, it doesn't show it's thinking. It's often not wrong. But the no compromises attitude can be unbearable to deal with. Especially given how little it cares about telling us.
The other explanation is just as part of ‘token efficiency’
that could help tackle half of the problems here. i do think the other 100% completionist part is something i'm more used to steering through with llm usage, have negotiated fora while, and that Astra is particularly an astronaut whose instincts are extremely strongly in the direction of foreseeing and outdesigning potential problems, that it is rarely going to pick a practical sensible clear path on it's own.
My results with 5.6 Sol were quite similar to 6, although I haven't tested it that much.
if GPT 6 Sol is just 5.6 at half the price it will be everything i really ever wanted.
They usually reduce usage consumption in line with cost reductions (But not always 1:1)
Astra was/is superior for planning type tasks. It was capable of doing seemingly magic things with rather vague/lazy instructions ("I need to be able to test this on Windows, maybe a qemu VM or something? Shrug." ... 1 hour later "yeah i built you a whole qemu + eval windows image + harness of powershell scripts + shell scripts to retrieve & verify harness.").
And for UI work -- which is not something I do a lot of but do here and there -- it was clearly superior to 5.6 Sol.
But it also feels sloppier? Somehow. And too expensive to use.
We'll see how Sol 6 is.
Terra had the "workhorse" quality where it could do these changes in bulk and follow directions without being too 'smart' (but sloppy) as you described. Luna was a bit too dumb and would make sloppy mistakes; I see that more as a "run these tests and format the results" sort of model. Maybe 6 Luna will be better.
I also just reread your comment and realized the naming convention is still extremely confusing with respect to ordering of [Family]x[Model]x[Number].
When people spend their days interacting with machines that pretend to be human, they may then start treating real humans like machines.
Sol 6 is a heaping pile of garbage. Just epic levels of slop. And r/codex etc is full of people noticing the same.
I've switched back to 5.6 Sol. What they're selling as Sol 6 is really what would have been Terra before, and it's awful.
Otherwise I'm using 5.6 Sol for actual plan execution and review..
(I have not done anything quantitative here. For one thing, OpenAI’s billing pages and the codex-rs frontend make it pathetically difficult to get any real data. Some day I should wire up a proxy to extract actual stats.)
If I leave Astra overnight, I'll wake up with three new different projects, each of them 20% done and having nothing to do with my original goal.
Highlight and lowlight of my week was successfully convincing the OpenAI support chat robot to give me a refund for the month for my issues with 6 chewing threw my usage with no output.
But I wonder if that's intentional because it can keep computing while you are answering, so long as your steer aligns well enough with the direction it wants to go. Better than letting a cache go cold and burning compute on bringing it all back up.
Like you said, it felt very natural to work with. Opus 5 is way too slow and verbose for me, I find I get distracted and annoyed with it.
Opus 5.5 seems a LOT closer so far to what I liked about 5.6 Sol but we'll see
How are people using 5.6 Sol? API pricing? Subscriptions?
I like because DeepSeek 4.1 Flash because I never experience quota issues, and it's still cheap and mostly good enough.
I'm happy to spend more for a better product, but mostly I just want to avoid quotas, since it turns me into an addict, feeling like I have to be ensuring the bots are active.
I like that with DeepSeek's API pricing that I can not sure it for 2w, and not feel like I've missed out. 2w is a long time, but I only use it for personal stuff, and I often go 1-2w without using it due to other commitments.
Except they were not for past few years as they misfired on the attempt to compete with vscode. That had a big impact on pycharm, which seemed starved for resources for so long. The company eventually declared a year of Django, but even that failed to really make an impact.
Arguably, Jetbrains had first insight into AI based code completion via rapid rise of the TabNine plugin but missed that opportunity also.