DeepSeek V4 Pro (Reasoning, Max Effort) is a large-scale Mixture-of-Experts (MoE) language model released in preview by DeepSeek on April 24, 2026. This specific configuration of the V4 series is optimized for deep logical reasoning, advanced mathematics, and complex agentic tasks. The model architecture consists of 1.6 trillion total parameters, with 49 billion activated parameters per token, providing a balance between extensive knowledge capacity and inference efficiency.
Architecture and Long-Context Efficiency
The V4 series introduces a Hybrid Attention Architecture that integrates Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA). This design facilitates a 1 million token context window while dramatically reducing resource overhead; at maximum context, the model requires only 10% of the KV cache and 27% of the inference FLOPs compared to previous generations. Signal propagation is stabilized through Manifold-Constrained Hyper-Connections (mHC), a proprietary wiring method that enhances logical consistency in deep networks. Training was conducted using the Muon optimizer on a dataset exceeding 32 trillion tokens.
Reasoning and Optimization
The "Max Effort" (or Pro-Max) reasoning mode is a result of a specialized post-training pipeline. This pipeline uses a two-stage paradigm: independent cultivation of domain-specific experts followed by unified consolidation via on-policy distillation. The model employs Group Relative Policy Optimization (GRPO) for reinforcement learning, enabling it to outperform predecessors in coding and STEM benchmarks. Evaluations suggest the model achieves state-of-the-art results for open-weights models in repository-level reasoning and complex cross-file debugging.
To achieve optimal performance with the reasoning chains in Max Effort mode, it is recommended to use a temperature of 1.0 and top_p of 1.0. Additionally, the context window should be set to at least 384,000 tokens to accommodate the internal "thinking" process required for the most difficult logic and agentic workflows.