DeepSeek-V3.2-Exp is an open-weight, sparse Mixture-of-Experts language model built as an evolution of DeepSeek-V3.1 Terminus, with the key change being a new attention mechanism called DeepSeek Sparse Attention (DSA). Architecturally it stays very close to V3.1: around 685B total parameters with ~37B active per token, FP8-centric design, and the same MoE/MLA family as V3. What V3.2 adds is a fine-grained sparse attention layer that trims the amount of work done per token, especially in long-context scenarios, while aiming to keep output quality statistically on par with the dense attention in V3.1. In other words, it’s a “same brain, cheaper long-context inference” iteration, not a completely new family.
Compared with earlier DeepSeek versions (V3 base and V3.1), the biggest practical difference is in long-context performance and cost. V3.1 already introduced a strong 671B-parameter MoE with 37B active, FP8 microscaling, and up to 128k context via multi-phase long-context training. V3.2 keeps that training recipe aligned but swaps the dense attention blocks for DSA via continued training, letting DeepSeek measure apples-to-apples cost reductions: external reports and the V3.2 writeups point to up to ~50% lower cost on long-context workloads versus V3.1 at similar quality. It also ships earlier and more completely on non-CUDA accelerators—Huawei Ascend, Cambricon, Hygon DCUs—alongside Nvidia, which matters if you care about cross-vendor deployment.
For application developers, this means you can often treat V3.2 as a drop-in upgrade where long prompts are the bottleneck: chat transcripts with lots of history, RAG workloads that need large retrieved bundles, or multi-step tool-calling traces. If you’re doing retrieval-augmented generation against a vector database such as Milvus or Zilliz Cloud, V3.2’s main value is that it gives you more headroom before long-context costs explode, and it handles large concatenated contexts more gracefully when you really need them. But the core pattern—keep most knowledge in the vector store, keep prompts lean, rely on retrieval instead of dumping entire corpora into context—still applies, and is usually more important than the specific version bump from V3.1 to V3.2.