DeepSeek

Introduced two variants: V4-Pro (~1.6T params) and V4-Flash (~284B params)  Major upgrade to Mixture-of-Experts (MoE) architecture, improving efficiency and scalability  Added ~1 million token context window, enabling much longer conversations and code understanding  Significant boost in coding, reasoning, and agent capabilities compared to previous versions  Focus on higher performance at lower cost, competing with top closed-source models

Much longer code understanding, with claims of over 1M token context enabled by a new sparse attention approach. Better repo-level work, aiming for multi-file reasoning like tracking imports, types across modules, and cross-file refactors. Stronger “memory-like” behavior for projects, via Engram conditional memory meant to retain and recall repo conventions and patterns. More stable scaling and training efficiency, tied to the mHC (Manifold-Constrained Hyper-Connections) method DeepSeek publishe

Introduced DeepSeek Sparse Attention (DSA), a new sparse-attention system that greatly reduces compute and memory usage. Long-context performance improved, enabling faster processing of long documents, large prompts, and extended conversations. Inference cost significantly reduced — API pricing cut by more than half, making large-scale usage much more affordable. Model efficiency improved while maintaining performance similar to DeepSeek V3.1 on most tasks. Strong performance on reasoning, coding, and QA tasks, with some areas showing slight improvements. Continued support for chat mode and reasoner mode, now optimized with V3.2 architecture. Released with open-source weights, code, and tooling for easy deployment and experimentation.

Introduced DeepSeek Sparse Attention (DSA) for more efficient long-context handling. API cost reduced by ~50% compared to V3.1. Improved benchmarks in math/reasoning (e.g., AIME up), with small trade-offs in some knowledge tasks. Both deepseek-chat and deepseek-reasoner models upgraded to V3.2 weights. Added native support/optimizations for Chinese accelerators (Huawei Ascend, Cambricon, Hygon) alongside Nvidia GPUs.