技术规格

DeepSeek-V3.2 Thinking

DeepSeek-V3.2 Thinking is an advanced reasoning-enhanced language model that integrates large-scale reinforcement learning with a massive mixture-of-experts (MoE) architecture. As the reasoning-specialized variant of the V3.2 series, it is engineered to prioritize logical consistency and systematic problem-solving through an explicit chain-of-thought (CoT) process. The model is specifically optimized for complex domains such as mathematics, algorithmic programming, and multi-step agentic workflows, where it generates detailed reasoning traces prior to producing a final response. This transparency into the model's internal logic allows for more reliable verification of complex outputs and supports sophisticated tool-integration scenarios.

Technically, the model utilizes a sparse Mixture-of-Experts (MoE) framework comprising 671 billion total parameters, with 37 billion parameters activated per token to maintain high computational efficiency. A significant architectural advancement in this version is the introduction of DeepSeek Sparse Attention (DSA), which reduces the computational complexity of the attention mechanism from quadratic to nearly linear. This innovation, instantiated under Multi-Head Latent Attention (MLA), enables the model to process long-context sequences with substantially lower memory and compute overhead. The model also employs a Group Relative Policy Optimization (GRPO) framework for reinforcement learning, which stabilizes training by utilizing group-based baselines instead of a separate critic network.

DeepSeek-V3.2 Thinking is designed for high-stakes reasoning applications, including scientific research, debugging intricate software logic, and executing autonomous agentic tasks. It supports a 128k context window and introduces a 'thinking with tools' capability, allowing the model to perform interleaved reasoning and API calls. The integration of Multi-Token Prediction (MTP) during training further enhances its internal representations, leading to faster convergence and more robust performance on reasoning-heavy benchmarks. Released under the MIT license, this model provides an open-weight foundation for researchers and developers seeking to deploy frontier-class reasoning capabilities in local or enterprise environments.