DeepSeek V3.1 is a large hybrid reasoning model built on a Mixture-of-Experts design, carrying 671B total parameters with 37B active per forward pass. What sets it apart is its dual-mode operation: a single set of weights can switch between a thinking mode, where the model deliberates through chain-of-thought-style reasoning, and a non-thinking mode that behaves like a faster chat model, with the toggle handled through the chat template rather than separate deployments. The base version is trained for raw next-token prediction on 14.8T tokens using FP8 mixed precision, optimized for efficiency and stability at scale, with a 128K token context that supports long documents, multi-file code, and extended agent traces. Together, the architecture and mode-switching design make V3.1 a single model that can flex between quick answers and deeper reasoning depending on what a task requires.</parameter>

Post-training is where V3.1 differentiates from its base checkpoint. It is refined on top of DeepSeek-V3.1-Base, which itself extends the original V3 base through a two-phase long-context extension, then applies post-training optimizations that noticeably sharpen tool calling and agent task performance. A key quality claim is that the thinking-mode variant reaches answer quality comparable to the more computationally heavy DeepSeek-R1-0528 while responding more quickly, suggesting the team distilled the reasoning gains of R1 into a more efficient V3 lineage. In practice, V3.1 fits well for coding assistants, multi-step agent pipelines, retrieval-augmented workflows, and long-context tasks where a developer or product needs one model that can both plan carefully and respond conversationally without switching endpoints.