The reasoning model of DeepSeek allows for deeper answers by thinking through the steps of a problem, which was not released with previous models like OpenAI's.

DeepSeek's reasoning model was released for free at scale, contrasting with other models that required paid subscriptions, which contributed to its rapid adoption.

The reasoning models being developed by DeepSeek are designed to utilize more compute, emphasizing the growing importance of inference in complex AI tasks.

The release of the OpenAI o3-mini reasoning model aligns with expectations, but DeepSeek-R1 offers similar performance at a lower cost and reveals its reasoning process, unlike o3-mini.

DeepSeek-R1 is a reasoning model that builds on the training of DeepSeek-V3, enhancing discussions around AI capabilities and accelerating industry conversations.

The rapid rise of DeepSeek has raised questions about how they achieved such compatibility and capabilities with existing models.

DeepSeek's approach to training emphasizes compounding small improvements over time in data, architecture, and integration, which can lead to significant advancements.

DeepSeek's training efficiency stems from two main techniques: a mixture of experts model and a new method called MLA latent attention, which reduces both training and inference costs.

DeepSeek's rise in the open-source community is due to its strong reasoning model and user experience innovations that allow users to see the model's thought process, which had not been achieved before, and the 'DeepSeek moment' disrupted the market by providing a transparent and open-source approach that resonated with the community.