Deepseek v3.2 vs Deepseek V4 Flash

DeepSeek-V3.2 is a large language model optimized for high computational efficiency and strong tool-use reasoning. It features DeepSeek Sparse Attention (DSA), a mechanism that lowers training and inference costs while maintaining quality in long-context tasks. A scalable reinforcement learning post-training framework further enhances reasoning, achieving performance comparable to GPT-5 and earning top results on the 2025 IMO and IOI. V3.2 also leverages large-scale agentic task synthesis to improve reasoning in practical tool-use scenarios, boosting its generalization and compliance in interactive environments.

DeepSeek V4 Flash is an efficiency-focused Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B active parameters, supporting a 1M-token context window. It is built for fast inference and high-throughput workloads while preserving strong reasoning and coding capabilities. The model features hybrid attention for efficient long-context processing and offers configurable reasoning modes. It is a strong fit for use cases such as coding assistants, chat applications, and agent workflows where responsiveness and cost efficiency matter.