DeepSeek V4 Flash (Reasoning, High Effort)

DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts (MoE) language model released by DeepSeek in April 2026. Designed as a high-throughput, cost-effective alternative to the flagship V4-Pro, it balances rapid inference speeds with advanced logical reasoning and coding capabilities. The model is specifically engineered to handle long-context tasks, supporting a native context window of one million tokens.

Built on a 284B parameter architecture with 13B parameters activated per token, DeepSeek V4 Flash introduces a Hybrid Attention Architecture. This mechanism combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to reduce KV cache memory requirements and computational overhead in long-horizon tasks. Additionally, the model utilizes Manifold-Constrained Hyper-Connections (mHC) to improve the stability of signal propagation across its neural layers, and was trained using the Muon optimizer on a dataset of 32 trillion tokens.

When operating in its high-effort reasoning mode (often referred to as Thinking Mode or Flash-Max), the model allocates a significantly larger computation budget to internal chain-of-thought processing. In this configuration, DeepSeek V4 Flash can approximate the reasoning performance of larger frontier models on mathematics and logic benchmarks. This mode allows users to trade inference latency for improved accuracy on complex agentic workflows and multi-step problem-solving.

DeepSeek V4 Flash is primarily optimized for text-based tasks, including system-wide agentic coding and large-scale repository analysis. Its efficiency gains allow it to operate with a fraction of the inference FLOPs required by previous generation models like DeepSeek-V3, making it one of the most resource-efficient models in the 1M-token context class.