DeepSeek V4 Pro is a large-scale Mixture-of-Experts language model built to handle demanding computational tasks with high efficiency. It features a massive architecture comprising 1.6 trillion total parameters, with 49 billion parameters activated during inference. To manage extensive information processing, the model utilizes a hybrid attention mechanism that integrates Compressed Sparse Attention and Heavily Compressed Attention. This design significantly reduces the computational cost of processing long sequences, allowing the model to maintain stability and performance through the use of manifold-constrained hyper-connections that strengthen signal propagation.

The model undergoes a rigorous post-training process that begins with independent domain-expert cultivation using supervised fine-tuning and group relative policy optimization. This is followed by a unified consolidation phase through on-policy distillation to refine the model's output. These methods enable the model to excel in agentic coding, mathematics, and STEM-related reasoning, where it consistently outperforms other open-weight systems. By balancing these advanced training techniques with a focus on inference efficiency, the model serves as a versatile tool for developers seeking frontier-level reasoning capabilities in a highly optimized, accessible format.