If you play with these models long enough, you realize there is more to them than just "model X is smarter than model Y" or "model Y is cheaper than model Z". They are different tools and the prompting technique is different. It is very much like playing an instrument.

With Claude, you sometimes want to under-specify or phrase things more indirectly to give a color to the implementation or elicit something creative. Also (you might raise an eyebrow at this) being nice to Claude will be rewarded and being mean to Claude will be punished. Claude tends to mirror your tone more aggressively and you don't want to get into negative loops with it.

With GPT, you have to be precise and reduce ambiguity. GPT will often try to resolve ambiguity in a min-max style "I'm going to do X, but make sure it is not quite Y". It will tend to be more paranoid and overengineer to catch all edge cases if you don't tell it precisely what the scope is.

With Qwen, you have to give it a shape and let it fill it in. Qwen likes XML, JSON and lists. Qwen likes to be shown a bunch of examples of previous work.

This is not scientific at all, just vibes, YMMV.

That's a great write up.

The one thing I feel it seems to under estimate is the likelihood of improvement. Even the authors acknowledge it's not even worth comparing local models from a year ago to what we have now. In fact, people widely see Opus 4.5 in November last year - 8 months ago - as the first time agentic coding became viable broadly viable even with frontier hosted models.

So why would we lock in hard on any concept at this point of what a local model is and isn't good for? Whatever it is right now, it probably won't be that in a year. It might be naive optimism to think we'll ever get to long horizon tasks with models that run on consumer / pro grade hardware. But so far the naive optimists are winning.

This article is a good summary of local models. Unlike the way they are hyped sometimes, as fantastic tools for coding and agentic local work. The reality is that they are rather limited, would not do well on a long or complex task, and are prone to fall into loops, forget their tasks, etc. Not mentioned in the article is that they are also rather expensive - not just for the hardware cost, but also electricity. These 3090 and 5090 machines are pretty power hungry, and these models are pretty slow on these machines, making them consume more power per token.t

Where they shine is in your ability to control them, their privacy, their predictability (e.g. if you are doing a repetitive task, like classifying your photo/video library), and depending on your energy bill - their costs.

That was a lot of text for me still having no idea what the point of the author was (beside what I can infer from the headline that is).

I do however now know that they're a totally cool dude building stuff physically and as software + that other people give them money for it.

Does that have anything to do with the topic suggested by the headline? Not sure.

I've been running qwen3-5-9b-q4-k-m and qwen3-6-27b-q6-k simultaneously on an Intel Arc Pro B70 with a lot of success.

https://github.com/cptskippy/battlemage-llm-gateway

Opencode has been a huge productivity accelerator. I have two Hermes agents that I'm training to support my workflow with pretty good success. One is a personal assistant who manages my backlog and keeps me on task, follows up with me on items, and will put together research briefs. The other I use a general purpose coder and research and it's about 50:50 with the tasks I've given it. In fairness though, the task it failed at left me scratching my head to figure out as well.