DeepSeek V4 Pro is a large-scale Mixture-of-Experts language model built on 1.6 trillion total parameters with 49 billion activated per token, allowing it to combine broad knowledge capacity with comparatively light per-request compute. Its defining architectural choice is a hybrid attention design that pairs Compressed Sparse Attention with Heavily Compressed Attention, which the technical documentation reports reduces single-token inference FLOPs to roughly 27% of what DeepSeek V3.2 used at million-token context lengths. The same architecture underpins DeepSeek V4 Flash, positioning V4 Pro as the higher-capacity sibling tuned for more demanding workloads. This attention pairing, together with the 1M-token context window, is the core efficiency story for the model.
Post-training follows a deliberate two-stage pipeline in which separate domain experts are first cultivated through supervised fine-tuning and GRPO reinforcement learning, and then merged into a single model via on-policy distillation. The result is aimed squarely at advanced reasoning, coding, and long-horizon agent workflows such as full-codebase analysis, multi-step automation, and large-scale information synthesis, with configurable reasoning effort levels up to a maximum setting. Because the model is openly distributed on Hugging Face under an MIT license, teams can self-host for full-stack coding assistants, research pipelines, or enterprise agents that need both very long context and controllable reasoning depth, while still being able to call hosted endpoints for production traffic.
