DeepSeek V4

Hybrid attention with CSA and HCA: DeepSeek V4 replaces a single attention stack with Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA). CSA compresses KV entries, then applies DeepSeek Sparse Attention with sliding-window KV for fine-grained local dependencies. HCA aggressively compresses KV for extreme-context layers, keeping the model feasible at 1M tokens.

Training stability at trillion-parameter scale: The team introduces two techniques that materially cut loss spikes. Anticipatory Routing decouples backbone and router updates, using current weights for features but historical weights for routing indices. SwiGLU Clamping bounds the linear and gate components of SwiGLU to stabilize activations throughout pretraining.

Domain-specialist post-training: Instead of one large mixed-RL stage, DeepSeek trains a separate specialist expert per domain. Each expert goes through supervised fine-tuning on domain data, then Group Relative Policy Optimization (GRPO) RL with a domain-specific reward model. The specialists are merged into the final model, recovering capability without destabilizing the generalist.

Frontier-adjacent performance at open-source cost: DeepSeek-V4-Pro-Max beats GPT-5.2 and Gemini 3.0-Pro on standard reasoning benchmarks and lands just behind GPT-5.4 and Gemini 3.1-Pro, effectively trailing the closed frontier by roughly 3 to 6 months. For open-weights teams that need long-context reasoning without closed API pricing, this is the most important release of the week.