alt Hacker NewsGP's point is about "sending tokens to someone else's computer" versus "keeping the tokens locally". I think model capabilities are secondary.
In May of this year, I was running qwen3.6:35b-a3b on my MacBook (bought in 2024). Obviously not as fast as, say, running a model on Cerebras, but a year ago it wasn't really feasible to have a local model running on my 2024 laptop with vision support. (Concretely, I was passing apartment diagram pictures to Qwen and making it compare different apartments for which ones would feel the most spacious while optimizing for initial moving costs and other factors.)
This was back in May and I wouldn't be surprised if there have been significant improvements since then.
Overall, I think it's fair to compare a workflow like "use llama.cpp locally to upload some pictures and ask questions" to "open the ChatGPT app, upload pictures from your phone, and ask questions". Sure, you can't run a model like GPT-5.4 locally, but the model is mostly an implementation detail here. What a user will care about is: "when I go with the llama.cpp option, am I getting useful information from my conversations?"
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Deepseek 4 flash can run locally , and qwen 3.8-next-flash , they are already gpt 5.6 tier.
Wouldn't the better comparison still be against an AI provider with better privacy controls, especially if that's what someone cares about (even if they don't care about whether they're comparing a 35 billion param model vs a x trillion param model)?