DeepSeek R1: Chinese LLM that changed the artificial intelligence economy

DeepSeek R1 is not just another chatbot.

It is a signal that the advantage in AI increasingly depends on efficiency, open weights, and the ability to cheaply scale reasoning.

DeepSeek R1 has become one of the most important reference points in the discussion about Chinese language models because it combined three things that rarely occur together: very strong results in reasoning tasks, publicly available weights, and a narrative of radical cost efficiency.

In practice, it is not about whether every DeepSeek benchmark should be treated as direct proof of superiority over closed models from the USA.

What is more important is that DeepSeek has shifted the boundary of expectations for open models: if a laboratory outside the American market center can publish a reasoning model whose results are compared to OpenAI-class models, then companies, universities, and administration are beginning to calculate AI implementation costs differently.

The most concrete part of the story begins with DeepSeek-V3.

In a technical report from December 2024, the authors described a mixture-of-experts model with 671 billion total parameters and about 37 billion active parameters per token.

This is an important difference: the model has very large capacity, but during a single computational step, it uses only a fraction of the experts.