u/nbr_engineer

For the last couple of months, I’ve been testing a bunch of models inside opencode. And like probably many others, I kept falling into the “newer model = better model” trap. Every time something new dropped, I tried it.

GLM 5.1 got hype. Kimi K2.6 got hype. MiMo M2.5 Pro looked really interesting on paper. DeepSeek v4 Pro also had its moment (and it's month of "Deepseek V4 next week!" hype). And don’t get me wrong, some of them are genuinely good. But for my actual daily coding workflow, I keep coming back to Qwen3.6 Plus.

It is just insanely solid, and it punches way above its weight class.

The thing that impresses me most is not even one single benchmark or a flashy demo. It's the combination of things that actually matter when you are living inside a real codebase:

• It follows instruction protocols tightly without drifting.
• It does not randomly lose the plot or start hallucinating arguments after a few tool calls.
• It actually understands existing architecture instead of trying to casually rewrite half the project.
• The native 1M context window is a massive deal—and unlike some models, it actually reasons across that context stably instead of collapsing on repository-level files.

Is it as strong as GPT-5.5 or Claude Opus 4.7? No, obviously not. I would still hand those the keys for absolute hardest architectural decisions or deeply complex abstract logic.

But for daily coding agent loops? Man, Qwen3.6 Plus just delivers.

I think the model disappeared a bit from the spotlight because newer open models came out and the hype cycle moved on to the next shiny object. But if you are using opencode or building automated dev pipelines, it deserves way more attention than it gets.

Sometimes the best model isn't the newest one or the loudest one on a Twitter benchmark screenshot. Sometimes it’s just the one that remains reliable 20 turns into a messy debugging session.

What do you guys think? Anyone else daily-driving it for dev work over the newer releases?