DeepSeek

When DeepSeek released R1 in January 2025, it changed the conversation about what's possible outside the major US AI labs. The model matched or outperformed leading reasoning models at a fraction of the reported training cost, and the team released the weights openly under an MIT license. This triggered what observers started calling a 'DeepSeek moment': the realization that frontier-level performance wasn't a US lab monopoly.

R1 was particularly notable for its training approach. Unlike models that rely heavily on RLHF with human preferences, R1 used reinforcement learning with verifiable rewards (RLVR): training the model on math and code where correctness can be checked automatically, without needing human preference labels. It was one of the clearest demonstrations that capable reasoning models could be trained without massive human annotation pipelines.

The DeepSeek V3 series continued this trajectory, with V3.2 reportedly achieving gold medals at the International Math Olympiad in 2025. For builders, DeepSeek models are significant because they're open-weight, can be run locally or on cloud APIs at low cost, and often match closed frontier models on coding and reasoning tasks. They've become a go-to option when cost, privacy, or customizability matters.