The Reddit thread that caught my eye this morning was not about a new benchmark or a flashy demo. It was a simple observation: "I'm glad we have deepseek." The post listed how other companies have slowly pulled back from open weights, while DeepSeek continues to publish research and release base models.

This is not just fanboy praise. It is a real shift in the AI landscape, and it matters for anyone who cares about open-source AI.

What Is Actually Happening

Look at the pattern over the past year:

  • Kimi stopped releasing base models for K2.5
  • GLM withheld base models for GLM 5 and 5.1
  • Minimax delayed open weights and added problematic licenses to M2.7
  • Qwen 3.5 397B was open weight, but 3.6 is not

These are not isolated decisions. They are part of a broader retreat from open weights. The companies that used to publish detailed papers with training details now release blog posts and model cards instead.

DeepSeek is the exception. They still publish launch papers with architecture details, release base models alongside instruct versions, and open-source kernels and infrastructure. They even built a custom file system to squeeze more training efficiency.

Why This Matters

Open weights are not just about free access. They are about reproducibility, research, and the ability to build on top of frontier models without being locked into a vendor's API.

When a lab releases base models, researchers can study how the model behaves, fine-tune it for specific tasks, and understand what actually makes it work. When they withhold those weights, the community loses that visibility.

The Reddit comments highlight another angle: DeepSeek's contributions go beyond models. They open-source CUDA kernels, PTX libraries, and training infrastructure that other teams can use. This is the kind of gritty engineering work that moves the field forward.

The Hardware Reality Check

Some commenters pointed out that DeepSeek does not release small models. Their V4 Pro is a 1.6T parameter beast that requires serious hardware to run. That is a fair criticism.

But the counterargument is that large open-weight models serve as teachers for smaller community models. You need good 300B+ class models to distill into 27B or 7B models that can run on consumer hardware. If the frontier labs stop releasing those large models, the community loses the raw material for distillation.

What Comes Next

The trend is clear: frontier labs are treating open weights as a competitive disadvantage rather than a public good. They want to lock users into their APIs and monetize inference rather than let anyone run the models themselves.

DeepSeek is the last major lab pushing in the opposite direction. Whether that lasts is an open question. For now, they are the ones keeping the open-weight pipeline alive.

If you care about open-source AI, pay attention to what DeepSeek releases. They are currently the only ones giving the community the tools it needs to keep up with the frontier.

Sources

  • Reddit thread: "I'm glad we have deepseek" (r/LocalLLaMA, April 25, 2026)
  • Hugging Face model pages for Qwen, GLM, Kimi, and Minimax releases
  • DeepSeek research papers and GitHub repositories