Pretty simple. People keep going on about how useful these local models are for coding. So what I wanted to do was to create a standardized test for myself to see if that was true before committing to anything.

( I think the various benchmarks out there are a bit fluffy, so I wanted to try it against a real workload.)

What I did was throw a bunch of money up at OpenRouter and then used Roo to call in diff models, one at a time.

I gave each the same task - that is, here is a piece of code, here is my ticket, here is my repo. Investigate what you want and then do what my ticket says.

I already knew what was wrong with the code, but I wanted to see how obedient the models are at sticking to a scoped ticket and what they would find.

By far the best bang for buck was GPT 5.4 mini. It is exceptionally obedient at doing exactly what you tell it as long as you tell it exactly what to do.

It won't go off piste if properly constrained.

I think for light - med workloads, $20 on ChatGPT is a crimal steal. Chat and Codex have a separate usage pool.

I'm also aware that this is open AI's lock in phase where they provide the samples of crack for free to get you hooked. And, yes, they are crack dealers in every sense of the word.

Anyway, it's good to know that with a little bit of elbow grease and some smarts, the smaller models, which could reasonably be self-hosted, could do a decent enough job if they are narrowly scoped.

You're probably not going to be able to yeet an entire code base at them and go "figure out what's wrong and fix it" while you snooze tho, but I think that's probably a good thing from a human in the middle perspective.

Yeah, a higher quant would be nice, I actually try not to go below Q5, but you can domino’s so much with 16GB of VRAM and the ddr4 system RAM.

But I must say I‘m pretty impressed by Qwen3.6-35b, not only from its capabilities but also from hardware requirements. MoE for the win I guess.

RWKV sounds interesting, have to look into it, thanks!