We've shipped some code generated by Qwen3.6 27B to production (under OpenCode). It lacks the breadth of knowledge of models like Opus, but if a change is fully inferable from the prompt and the surrounding code, it works very well. It won't be able to write something from scratch that requires niche knowledge (say, a performant inference engine tailored to Blackwell GPUs), but if it's just a PR adding a new use case to an existing project (which is usually just "load from the DB, do some invariant checks, modify the entities, store them back"), it works as well as Sonnet (provided you have the correct configuration, like recommended temperature and top-p settings, the model isn't over-quantized, you have at least 150k tokens of context available, etc.).

I don't use them as coding agents, but they can be very useful for things like text transformation, summarizing, or text extraction.

That said, if you have a subscription to a paid model already, you're not necessarily winning out on anything except perhaps privacy, which isn't nothing.

there is plenty of grunt work these smaller models can do. update dependencies, fix merge conflicts, write --help, markdown, or readme files for existing code. etc.

sometimes they fail but undo is just a "git restore" or if automated, rejecting a PR and having a better model take a crack at it.

Qwen35ba3b can do a huge amount of data cleaning work on pretty modest hardware. Already have run about 100 billion tokens on it using 2x3090 gpus.

They're great at helping me look up web dev stuff when I don't have internet access.

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