I have become so "lazy" (in a good way), so far that I've started using the model for lots of daily mundane things on top of just coding:
* "commit this on a branch, push, create a PR and assign $nickname for review"
* "Use the Stripe CLI to download all open and overdue invoices and reconcile them with this CSV export from our bank account."
* "Use these Elasticsearch credentials to summarise what kind of operations are causing load at the moment."
* "Tell me if our codebase already supports X and where it's implemented." --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.00 --presence-penalty 0 --metrics --jinja --chat-template-file chat_template.jinja --chat-template-kwargs '{"preserve_thinking": true}' --spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.75 -ngl 99 -c 131072 -fa on -np 1 -hf unsloth/Qwen3.6-27B-MTP-GGUF:Q6_KThe trade-off of MoE is that it is worse but faster for the same total size.
It feels like anything less than Sonnet is just a waste of time, apart from use as a smarter search function.
It also strikes me as strange that you would mention Codex for UI polish, as it's notoriously bad at UI, and far behind Claude Opus. Altman specifically posted that they are working to improve this for the next model release.
All the drudgery.
I almost find it offensive when colleagues open a MR with an obvious slop description that's frequently inaccurate.
That said, I find AI useful for a lot of drudgery like resolving merge conflicts or splitting changes out into separate MRs.
Particularly with the latter I had issues with small models, they butchered the changes I wanted moved. Not even on the second attempt did GPT 5.4 mini manage to move 10-20 lines to another file without modifying them in the process.
Other Notes: I have had to set the compact target to 75% on a 256k context window as once the conversation length goes about 100k I start seeing a drop in the quality and speed. This becomes very problematic after about 150k. I tried Qwen 3.5 122b too but it actually seems much worse at coding than 3.6 27b even though its much larger. Maybe because I am using a 4bit quant or maybe I just don't have it configured correctly? I know 3.6 is newer but I didn't expect it to out perform a model that is much larger from the prior generation. Gemma 4 31b is a good model for other tasks but at least my personal experience is that Qwen outperforms in coding. Nemotron Super 120b is great at a lot of stuff but it also seems to be not as good at coding as Qwen. This was very surprising to me.