DeepSeek Unveils V3.2-Exp: Slashing Long-Context Costs by 6x
Hangzhou-based AI company DeepSeek has unveiled DeepSeek-V3.2-Exp, an update that introduces DeepSeek Sparse Attention (DSA) for improved long-context efficiency. The company, owned by High-Flyer, has also slashed API prices by 50% following the release.
DeepSeek-V3.2-Exp retains the MoE and MLA stack from previous versions, but introduces a two-stage attention path. This path consists of a lightweight 'indexer' and sparse attention over a selected subset, significantly reducing decode costs. As a result, decoding at 128k is now approximately six times cheaper.
The indexer is trained to mimic the dense model's attention distribution using KL-divergence in a two-stage process. This innovation allows DeepSeek V3.2-Exp to maintain benchmark parity while improving long-context economics. The update is a drop-in replacement for RAG and long-document pipelines where quadratic attention dominates costs.
DeepSeek has also made DeepSeek-V3.2-Exp truly open source, licensing it under MIT. The company has reduced API prices by 50% consistent with the efficiency gains of DSA.
DeepSeek's latest release, DeepSeek-V3.2-Exp, brings significant improvements in long-context efficiency and cost reduction. With its open-source licensing and substantial API price cuts, the update is poised to have a notable impact on the AI landscape.
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