Delisting Closed-Weight Models

And maybe taking a step further

For much of its operational history, BayLeaf has highlighted open-weight LLMs. The Basic model in the BayLeaf Chat service has always been powered by an open-weight model (even if it wasn’t told in the system prompt exactly which one), and most of the per-course agents have been based on open-weight models. But, with the introduction of the BayLeaf API in March 2026, users could directly access a huge variety of models, including many closed-weight models.

I like the way that open-weight models are available for unauthenticated download from the web in the same way that much of the presumed-public data used to train them was. That’s a great expression of reciprocity. Am I giving that up or diluting my focus by allowing people to access closed-weight models on my platform? I’m still not sure.

This morning, I pushed a change to the BayLeaf API that effectively delists closed-weight models. If you craft a request with the correct model slug for a closed-weight model you know of (perhaps you found it listed on OpenRouter), the request will still go through (assuming there are ZDR providers for it available), but I’m not making it too easy.

What do I gain by taking this step, or the next step beyond delisting to actually block access to closed-weight models? I gain some ideological purity points, but who is counting? I gain a talking point about how I operate an open-weight-only LLM service, and that feels good personally. I’d hope, a few months after delisting, that I can tell stories of how people didn’t knock on my door asking for access to those unlisted models. It’d show sufficiency and that access to plenty-good-enough doesn’t require dancing on the frontier.

What do I lose by making those brand-name models harder or impossible to access? Writing this in July 2026, just a few days after the Open Weights and American AI Leadership open letter and the launch and full binary release of Kimi K3, I’m not clear I’m giving up much at all either in the present or in the long term. There’s an increasing thin sliver of capability that is only accessible via specific closed-weight models, and it would be easy for me to justify that accepting (excepting?) this sliver is a fair price to pay for being able to declare ourselves satisfied within the open-weight sphere.

To be fair, it is reasonable to want access to the full variety of models to run comparative experiments. This is something I’m a little interested in myself as an AI nerd, but the vast majority of BayLeaf’s audience, the UC Santa Cruz campus community, doesn’t need this, and it is available at reasonable costs outside the walls of the BayLeaf playground. Maybe I’ll take that next step in the near future.

If my choice is between needing to tell someone that, sorry, we don’t have ClaudeGPT-9000 or getting to tell someone that there’s no way using BayLeaf can contribute data or money to providers who don’t play the Web reciprocity game… uh, the choice is getting clearer by the second.

You should know that the vast majority of BayLeaf’s development and maintenance was done and continues to be done with agents based on closed-weight models, but that fraction is steadily ticking down. And that’s not just because I’m trying out monstrously-large open-weight models like Kimi K3. The fraction done with laptop-scale (and even phone-scale) open-weight models is ticking up from below. It’s just computers, man. It doesn’t have to be empires.