DeepSeek V4 Flash (Reasoning, High Effort)

DeepSeek V4 Flash is an efficiency-oriented Mixture-of-Experts (MoE) language model designed for high-throughput and cost-effective reasoning. Released as the leaner counterpart to the V4 Pro, it balances a large total parameter count of 284 billion with a sparse activation of only 13 billion parameters per token. The model is specifically optimized for agentic workflows, complex coding tasks, and long-context processing, supporting a standard context window of 1 million tokens.

The "Reasoning, High Effort" designation refers to a specific configuration of the model's Thinking Mode. In this state, the model utilizes a chain-of-thought (CoT) deliberation process before generating a final response. The "High Effort" setting allows the model to spend a significant token budget on internal reasoning, which has been shown to substantially improve performance in STEM, logic, and multi-step agentic benchmarks compared to the non-thinking or low-effort modes.

Architecture and Innovation

DeepSeek V4 Flash incorporates several architectural advancements to maintain efficiency at scale. It utilizes a Hybrid Attention mechanism that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to reduce the KV cache footprint and compute costs associated with its 1M-token context. For training stability and faster convergence, the model employs the Muon optimizer and Manifold-Constrained Hyper-Connections (mHC). The official 0731 release also includes an attached speculative decoding module to improve inference speed.

Performance and Capabilities

Despite its lower activated parameter count, DeepSeek V4 Flash is competitive with significantly larger proprietary models in agentic benchmarks such as Terminal Bench and DeepSWE. Its post-training pipeline focuses on domain-specific expert cultivation through Reinforcement Learning with Group Relative Policy Optimization (GRPO). When operating at high reasoning effort levels, the model is capable of handling long-horizon coding loops and complex tool-calling scenarios that require deep deliberation.