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Model ID
DeepSeek-V4 series incorporate several key upgrades in architecture and optimization: Hybrid Attention Architecture: We design a hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to dramatically improve long-context efficiency. In the 1M-token context setting, DeepSeek-V4-Pro requires only 27% of single-token inference FLOPs and 10% of KV cache compared with DeepSeek-V3.2. Manifold-Constrained Hyper-Connections (mHC): We incorporate mHC to strengthen conventional residual connections, enhancing stability of signal propagation across layers while preserving model expressivity. Muon Optimizer: We employ the Muon optimizer for faster convergence and greater training stability.

API Usage

You can interact with the DeepSeek-V4-Pro model through various programming languages and methods. Below are examples showing how to use the model’s API.

API Examples

Generate a model response using the chat endpoint of DeepSeek-V4-Pro.

Shell

Python