There is minimal downside to switching to open models

Andrew Marble
marble.onl
andrew@willows.ai
June 21, 2026

There was a time not too long ago when using Linux entailed some professional risk¹. First there was compatibility: you may not have been able to render a Word document or PowerPoint correctly, and you might have had to trust Open Office’s export capability to render docs the way you wanted. There might have been specialty file formats you couldn’t easily view and so couldn’t collaborate. And second, the software ecosystem was just worse generally. There were lots of half-build open-source projects trying to achieve the functionality of mainstream software, but they always had rough edges. I, embarrassingly, stayed on Windows until I left academia over Matlab.

Nowadays I think this issue has largely disappeared. Most productivity software has a web-app, Linux is more mature, open-source software is better. I’m sure that there are all sorts of application specific software (CAD?) that still require a Windows machine, but the gap is much narrower and Linux + open source generally aren’t the “sacrifice” they once were generally.

There remains a clear penalty for being an open² LLM user. Every leaderboard consistently gets topped by proprietary models served over API. Today on June 21, 2026, Claude and GPT are at the top of the Artificial Analysis intelligence leaderboard. That’s from the performance side. The compatibility side is worse too. Claude code just works, and more generally, the big two provide nice APIs that make them easy to use, and, even if it’s a low bar, are “trustworthy” in the sense that we’ve largely all agreed we don’t mind sending them our LLM queries and trust them to handle them appropriately.

Open models are served via various means, some by the companies that released them and some by third parties like OpenRouter. Unfortunately, both of these routes are dodgier in terms of privacy and data sharing, and I would not feel the same comfort sending API calls containing client or confidential data to them³.

The other option or course is to run them yourself. This solves the privacy issue but is at least two of expensive, complicated, and comparatively slow.

Up until recently, open models had mostly been a hobby for me. I’ve tinkered with them since the original Llama leak, and occasionally used them when I has a niche use case, but for most professional work, I stuck with the Big 2. This appears to be changing, with Claude’s ID verification rollout⁴. It was inevitable that things would get worse for users, and the writing was on the wall anyway recently with all the new “safeguards” on recent models and the whole Mythos thing. I’m not going to spend time talking about why I’m not going to indulge ID verification (or the LARPing that surrounds it) but what is immediately concerning is what kind of professional penalty it will incur to stop using the top models.

I’m hoping it’s going to be minimal. I’m already set up to run a range of open models either locally or in the cloud, there are good coding harnesses for open models, and most importantly the open models are now very close to the leaders and typically trail only by a few months. This doesn’t feel like 2008 Linux vs Windows, it’s much closer. I expect productivity will take a short-term hit, but don’t think it’s a deal breaker the way switching from Matlab to GNU Octave would have been when I was doing research.