In most cases, I'm only seeing models like sonnet being just barely sufficiently for the workloads I've done historically. Would love to know where others are finding use of smaller models (like gpt-oss-120B and below, esp smaller models like this).
Maybe some really lightweight borderline-NLP classification tasks?
q how to force kill particular WSL
it's also quite good at tool calling, if you give it shell access it'll happily do things like "find me files over 10mb modified in the last day" etc where remembering the flags and command structure if you're not doing that action regularly previously required a google or a peek at the manpage
i also use it to transcribe todo lists and notes and put them in my todo app as well as text manipulation, for example if i have a list of like, API keys and URLs or whatever that i need to populate into a template, I can just select the relevant part of the template in VSCode, put the relevant data in the context and say "fill this out" and it does it faster than i would be able to do the select - copy - select - paste loop, even with my hard won VIM knowledge
TL;DR
It's very fast (90tok/s) and very low latency and that means it can perform a lot of mildly complex tasks that have an obvious solution faster than you.
and fwiw i don't even think sonnet 4.5 is very useful, it's a decent model but it's very common for me to push it into a situation where it will be subtly wrong and waste a lot of my time (of course that's colored by it being slow and costs money)