DeepSeek V4 Flash (Reasoning, Max Effort)

DeepSeek V4 Flash is an efficiency-oriented Mixture-of-Experts (MoE) language model designed to balance high-throughput inference with advanced reasoning capabilities. Released as an upgrade to the V4 series in July 2026, the model features 284 billion total parameters, with only 13 billion activated per token. It is specifically optimized for agentic workflows, long-context comprehension, and complex coding tasks, often outperforming larger models in terminal-based and multi-step tool-use benchmarks.

The model utilizes a unique Hybrid Attention architecture that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA). This design significantly reduces the KV cache memory footprint—utilizing approximately 7% of the memory required by traditional architectures—allowing for a massive 1-million-token context window. Additionally, it incorporates Manifold-Constrained Hyper-Connections (mHC) to stabilize signal propagation and the DSpark speculative decoding module to accelerate generation speeds.

DeepSeek V4 Flash introduces a graded Reasoning Effort system (Low, High, and Max) that allows users to control the depth of the model's internal deliberation. In the Max Effort mode, the model engages in its most extensive chain-of-thought process, designed for solving high-difficulty logical, mathematical, and architectural problems. For this mode, it is recommended to set a maximum output limit of up to 384,000 tokens to accommodate the potentially lengthy reasoning traces.

For optimal performance in agentic scenarios, the model is typically paired with a temperature of 1.0 and a Top-P setting of 0.95. Its post-training utilizes a two-stage paradigm involving independent cultivation of domain-specific experts via Group Relative Policy Optimization (GRPO) followed by unified model consolidation through on-policy distillation.