I'm not using my models locally, but the majority (80% or more) of my coding agent sessions run on open source models, i.e. DeepSeek v4 Pro and Kimi K2.6 with thinking.
A point that I haven't seen come up a lot, but is very valuable to me is that for open source models, I can select the inference provider myself (even if it's not a local GPU), which means that I can enjoy superb speed (i.e. 300 tok/s) while still spending much less than the big providers.
My experience is that if you were fine with the coding models of yesterday (i.e. Claude Opus from Jan/Feb of 2026), you will be fine with either Kimi K2.6 or DeepSeek v4 Pro. Kimi is a bit more smart but has only 256K context and the performance deteriorates (and sometimes just gets stuck) when it fills up the context window. DeepSeek v4 has a 1M context and performs just as well with much less issues. And they both generate very idiomatic code, gives the same vibe of Opus a few months ago.
Since it's also fast (and does not fixate on trying to fix impossible problems, unlike the recent Opus/GPT 5.5 models), a big benefit is that you still control and steer the coding agent and you won't be losing focus like the major models. They are smart, but they don't fixate as much on trying to do stupid things, and since it's fast, you can just interject. It's a much more pleasant experience than the latest models.
I still use the latest models time to time when I expect the agent to fixate all of the problems and figure out everything themselves, but for me open source models are like 80~90% of all of my sessions.