Both can be true at the same time. I currently wouldn't waste my time with open models for almost all use cases, but they're crucial from a data privacy and competitive perspective, and I can't wait for them to catch up enough to be as useful as the current frontier models.

Local isn't viable yet on an economic basis, API costs are so low that you're better off taking advantage of the bonanza. As local models become more performant, so too will the ability of providers via Openrouter be able to offer them cheaper than your likely payoff period for a $4K Mac Studio 128GB. e.g Gemma 4 31B is impressive, but it costs practically nothing via Openrouter. Given that there are a ton of providers for open models, I doubt there's any subsidy going on because the providers are faceless and interchangeable.

At least, that's my theory.

The big advantages of local on a business level are:

- Freezing your model's exact settings once you've locked in some kind of workflow that works just fine. - Guarding against insane token usage from LLMs who have been told to never stop until they figure out the solution OR setting up an LLM run incorrectly. (The last one happened to me with Gemini 3.1 Pro) - PII or some need for on-premise only LLMs.

I'm just waiting till I can afford a GPU again

> Interesting to see your observation where I have observed the opposite: posts that share big news about open-weight local models have many upvoted comments arguing local models shouldn’t be taken seriously and promoting the SOTA commercial models as the only viable options for serious developers.

When I argue this, my point is that FOSS shouldn't target the desktop with open weights - it should target H200s. Really big parameter models with big VRAM requirements.

Those can always be distilled down, but you can't really go the other way.

I've invested significant time into getting open models to work, and investigating what works well.

The TL;DR is that unless you are doing it as a hobby or working in an environment where none of the data privacy options supported by Anthropic/OpenAI (including running on Azure/Bedrock with ZDR) work for you then it's not worth it.

The best open models are around the Sonnet 4.6 level. That's excellent, but the level of tasks you can give to GPT 5.4 or Opus 4.6 is just so much higher it doesn't compare (and Opus 4.7 seems noticeably better in my few hours of testing too).

I have my own benchmarks, but I like this much under-publicized OpenHands page: https://index.openhands.dev/home

It shows for every task they test closed models do the best. The closest and open model gets is Minmax 2.7 on issue resolution where it's ~1% worse than the leaders.

That matches my experience - fine for small problems, but well behind has the task gets bigger.