DeepSeek V4 Pro is built around a Mixture-of-Experts architecture that divides computation across specialized sub-networks, allowing it to handle a wide range of tasks without activating all 1.6 trillion parameters at once. Its defining structural choice is a hybrid attention mechanism that combines Compressed Sparse Attention with Heavily Compressed Attention, which dramatically reduces the computational burden of long contexts while keeping signal quality high. The system also incorporates manifold-constrained hyper-connections that reinforce signal propagation through the network, improving training stability and inference consistency across depth. The result is a model designed for complex, multi-step reasoning and coding tasks that can maintain coherence over extremely long inputs.

The model undergoes a two-stage post-training pipeline that first cultivates domain-specific expertise through supervised fine-tuning and GRPO reinforcement learning, then unifies those specialized capabilities through on-policy distillation into a single cohesive model. This approach produces a model that leads all open-weight models on math, STEM, and coding benchmarks, while rivaling top closed-source systems on the same evaluations. DeepSeek V4 Pro is released under the MIT License, making it available for both commercial and non-commercial deployment without licensing restrictions, giving development teams full access to integrate, fine-tune, or build upon the model for agentic applications, tool-use workflows, and complex problem-solving scenarios.