I have really been trying to get local models to work. I have tried different harnesses, tooling, skills, prompts, etc. But when I compare claude code with anthropic models or codex with gpt 5.5, vs qwen, glm or gemma and the same harnesses, the frontier models come out massively ahead. I am at the point where I just don't see the point of the non-frontier models, they waste more time than they save.
For agentic coding I 100% agree with you, it's worse and slower and more expensive for LARGE coding with local models. Narrow coding (like writing a specific function) is slow but viable. Regular LLM chat usage on high-end consumer hardware is competitive except on cost though. 0
I would say that is highly unlikely if by SOTA models you are not just referring to coding benchmarks but more general purpose ability and domain-specific knowledge. For example Kimi 2.6, which is comparable to Opus 4.6, is roughly 500+GB large, and I don't see how that would run on consumer hardware anytime soon.
Besides, this is not just about the technical feasibility, but also economically not viable whatsoever. Why should consumer laptops be capable of running such models, when they would be massively underutilized most of the time, when inference providers can produce the same results faster, cheaper and a lot more viable economically?
It runs like shit though in terms of tokens/second and still has a reduced context window. Vs a single claude prompt can easily get into 300k tokens without breaking a sweat.
I want local AI to be a thing but the hardware isn’t here yet, because the only options are a Mac Studio or DGX machines strapped together. RAM prices needs to crash before local AI has a chance at actually competing.
The more recent Chinese models are no longer heavily limited by context size. It can easily fit in RAM on a prosumer laptop. (You can also use swap space to extemd that, since context is only written to once per inference, thus a relatively mild wear-and-tear concern.)
You’re right, and it feels like these people saying otherwise either don’t use these tools professionally (and therefore can’t tell a difference between local/cloud models) or literally just haven’t tried running local models
As soon as I can buy hardware for less than 5k that runs an opus 4.6+/5.5 model locally I will do it instantly
Anthropic illegally downloaded virtually all copyrighted material in the world to train their models. What makes you think they will have even a little consideration for your IP?
Same. It pains me to hear quibbling about spending or reimbursing $200/month for codex or Claude plans . These are virtually inexhaustible for me as a software engineer and seem like a steal given the gains in efficiency.
And, who wants to screw around with harnesses or define agent orchestration when Claude/codex are good at this and getting better every month.
Good example. AFAIK they are still focusing on doing this via larger models which is a bad call. They are also focusing to hard on fully-agentic coding which, while useful to a limited extent, is not the best way to use AI for most non-trivial coding tasks.
Yeah, fully agentic coding just takes too much knowledge out of the engineer. It’s a lazy man’s button to do work and yield average results. Fun for vibe coding and not reliable enough for enterprise where quality matters. Maybe their focus will pay off in reliability, maybe not.
The hosted frontier models are massively subsidized, right? I think the point of local non-frontier models is just learning at this point, so you’ll be skilled if/when the market starts comparing the actual price of the two different models.
