DeepSeek-V3.2 handles long-context retrieval tasks by pairing a 128K-token context window with DeepSeek Sparse Attention, which is designed specifically to make long inputs efficient. Starting from a V3.1-Terminus model that already supported 128K tokens, the V3.2-Exp work introduces DSA so that attention cost scales closer to linearly with sequence length instead of quadratically, while maintaining similar benchmark scores. This means that when you feed in large retrieved contexts—like many pages of documentation, logs, or research notes—the model can still process them without an extreme latency spike.
From a system-design point of view, the most effective pattern is still retrieval-augmented generation rather than brute-force stuffing everything into the context. The Milvus documentation on “Build RAG with Milvus and DeepSeek” describes a standard flow: embed documents, store them in a vector database such as Milvus or Zilliz Cloud, retrieve a small number of top-ranked chunks per query, and only then call DeepSeek with the user question plus those chunks. DeepSeek’s long context and DSA then operate over a carefully filtered set of evidence, which improves both answer quality (less noise) and performance (shorter effective sequences). If you need follow-up questions, you can keep previous retrieved chunks and model responses in the context up to the 128K limit, turning the conversation into a multi-turn, retrieval-augmented session.
DeepSeek-V3.2’s handling of long context also plays nicely with other pieces of the ecosystem. The vLLM team reports Day 0 support for V3.2-Exp and notes that DSA’s “lightning indexer + sparse attention” design required some special handling but ultimately delivered large cost savings for long documents. Tutorials around DeepSeek and RAG also highlight complementary tools like DeepSeek-OCR, which help turn scanned PDFs into better-structured text for embedding, improving downstream search quality before the language model ever sees the data. For developers, the key takeaway is that DeepSeek-V3.2 lets you be more ambitious with context size when necessary, but the best practice remains: use a vector store to narrow the universe of tokens, and let DSA plus the 128K window handle the remaining long-context reasoning cleanly.