DeepSeek's V3 model, released in December, is a general-purpose model that performs comparably to other leading models like OpenAI's GPT-4 and Anthropic's Claude 3.5.

DeepSeek's V3 model utilizes a mixture of experts architecture, activating only 37 billion out of 671 billion parameters for each token prediction, significantly reducing computation compared to models like Llama 3, which activates all 405 billion parameters.

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

DeepSeek introduced novel techniques to efficiently train models with a mixture of experts architecture, stabilizing performance and increasing GPU utilization.

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 R1 model builds on V3 by applying various algorithmic improvements to enhance its reasoning capabilities, achieving performance similar to OpenAI's models on complex reasoning benchmarks.

DeepSeek's R1 model is one of the first large models to achieve top-tier results purely through reinforcement learning, marking a significant milestone in AI development. Additionally, DeepSeek introduced a cold start phase for fine-tuning on structured reasoning examples before reinforcement learning, which eliminated language mixing issues and made outputs far more comprehensible.

DeepSeek represents a significant step towards achieving artificial general intelligence (AGI) in a more accessible form.

DeepSeek's recent achievements in AI are notable, but their models are limited to text, while Llama 4 models are competitive, offering lower cost-per-intelligence for text tasks and leading in multimodal capabilities, indicating a potential gap in DeepSeek's capabilities.