Threads

Open weights versus closed

Whether frontier model weights should be published or kept behind an API — a line that ran from Stable Diffusion and leaked LLaMA to DeepSeek, and that by 2026 the incumbents and challengers had partly swapped sides on.

The open-versus-closed question is whether the weights of a capable model should be published for anyone to run and modify, or kept behind an API the developer controls. OpenAI set the closed template by renting GPT-3 through an API rather than releasing it. The counter-current arrived with Stable Diffusion, whose permissive public release put an image model on consumer hardware overnight, and hardened when Meta’s LLaMA leaked within a week of its research-only release.

An open ecosystem formed around the leak. llama.cpp made the models run on laptops; Llama 2 licensed them for commercial use; Mistral 7B and Llama 3.1 405B closed much of the gap to the closed frontier. Then DeepSeek’s R1 matched a leading reasoning model as open weights, and even OpenAI published open-weight models for the first time since GPT-2.

By 2026 the sides had partly swapped. Meta, long the standard-bearer for open weights, launched a closed frontier model, while Chinese labs — DeepSeek, Zhipu and Moonshot — became the most prolific publishers of open weights. The argument underneath the reversal endures: whether openness is a safeguard against concentrated power or a proliferation risk that hands capability to anyone.