DeepSeek V3.2
DeepSeek-V3.2 is a reasoning-first open large language model that combines efficient sparse attention with advanced agentic capabilities, delivering GPT-5-level performance.
DeepSeek 
DeepInfra Open-weight reasoning model with agentic tool use
DeepSeek-V3.2 is an open-weight large language model developed by DeepSeek and released on December 1, 2025. It uses a Mixture-of-Experts architecture combined with a novel sparse attention mechanism called DeepSeek Sparse Attention (DSA), which reduces computational complexity to near-linear scale (O(kL)) for long-context tasks. The model supports a 160,000-token context window and is available under the MIT License on Hugging Face.
DeepSeek-V3.2 introduces three notable technical advances: a scalable reinforcement learning training framework, a large-scale agentic task synthesis pipeline covering over 1,800 environments and 85,000+ complex instructions, and native support for Thinking in Tool-Use — the ability to reason while invoking external tools in both thinking and non-thinking modes. It is best suited for complex multi-step reasoning, agentic workflows involving search and code execution, long-context document processing, and developers building AI applications that require integrated reasoning and tool use.
What DeepSeek V3.2 supports
Long-Context Processing
Handles inputs up to 160,000 tokens, enabling analysis of lengthy documents, codebases, or multi-turn conversations in a single context window.
Advanced Reasoning
Trained with a scalable reinforcement learning framework that extends post-training compute, supporting multi-step logical and mathematical reasoning tasks.
Thinking in Tool Use
Supports integrated reasoning during tool invocation, allowing the model to think through problems while calling external tools in both thinking and non-thinking modes.
Agentic Task Execution
Trained on a synthesis pipeline covering 1,800+ environments and 85,000+ complex instructions, enabling reliable performance on search, code, and general agent workflows.
Code Generation
Generates, explains, and debugs code across multiple programming languages, with demonstrated performance at competitive programming benchmarks including IOI and ICPC.
Mathematical Problem Solving
Achieves gold-medal-level results on the 2025 IMO, CMO, and ICPC World Finals benchmarks, reflecting strong symbolic and numerical reasoning capabilities.
Sparse Attention Efficiency
Uses DeepSeek Sparse Attention (DSA) to reduce attention computation to near-linear complexity (O(kL)), lowering resource requirements for long-context inference.
Open Weights Access
Released under the MIT License with full model weights available on Hugging Face, allowing local deployment and fine-tuning without usage restrictions.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 83.7% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 75.1% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 59.3% |
| HLE | Questions that challenge frontier models across many domains | 10.5% |
| SciCode | Scientific research coding and numerical methods | 38.7% |
| AIME 2025 | American math olympiad problems (2025) | 96.0% |
| SWE-bench Verified | Real GitHub issues requiring multi-file code fixes | 77.2% |
Common questions about DeepSeek V3.2
What is the context window size for DeepSeek-V3.2?
DeepSeek-V3.2 supports a context window of 160,000 tokens, making it suitable for long-document processing, extended conversations, and large codebase analysis.
Is DeepSeek-V3.2 open source?
Yes. DeepSeek-V3.2 is released as an open-weight model under the MIT License. The model weights are publicly available on Hugging Face at huggingface.co/deepseek-ai/DeepSeek-V3.2.
What is the training data cutoff for DeepSeek-V3.2?
Based on the metadata provided, DeepSeek-V3.2 has a training date of December 2025. Specific knowledge cutoff details are documented in the official technical report.
What makes DeepSeek-V3.2 different from earlier DeepSeek models?
DeepSeek-V3.2 introduces three new capabilities not present in earlier versions: DeepSeek Sparse Attention (DSA) for near-linear attention complexity, a scalable reinforcement learning post-training framework, and a large-scale agentic task synthesis pipeline covering 1,800+ environments. It is also the first DeepSeek model to support Thinking in Tool-Use.
Can DeepSeek-V3.2 be run locally?
Yes. Because the model weights are openly available under the MIT License on Hugging Face, developers can download and run DeepSeek-V3.2 locally. Community users have demonstrated running it on hardware configurations such as 16x AMD MI50 32GB GPUs using vLLM.
What types of tasks is DeepSeek-V3.2 best suited for?
DeepSeek-V3.2 is designed for complex reasoning tasks, agentic workflows (including search and code agents), long-context retrieval, mathematical problem solving, and applications that require the model to reason while using external tools.
What people think about DeepSeek V3.2
Community reception on r/LocalLLaMA was largely positive at launch, with the Hugging Face release announcement thread receiving over 1,000 upvotes and 210 comments, reflecting strong interest in the open-weight release. Users highlighted the model's agentic capabilities and its MIT License as notable attributes.
Some users raised questions about real-world quality shortly after release, as reflected in a thread titled "is the new Deepseek v3.2 that bad?" with 68 comments discussing early impressions. A separate thread documented community experimentation with local hardware, including a 16x AMD MI50 setup achieving 10 tokens per second for text generation using vLLM.
is the new Deepseek v3.2 that bad?
deepseek-ai/DeepSeek-V3.2 · Hugging Face
16x AMD MI50 32GB at 10 t/s (tg) & 2k t/s (pp) with Deepseek v3.2 (vllm-gfx906)
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