⌘KCtrl+K
State-of-the-art 685B reasoning LLM with sparse attention, long context, and integrated agentic tools.
State-of-the-art 685B reasoning LLM with sparse attention, long context, and integrated agentic tools.
DeepSeek-V3.2 is a state-of-the-art large language model that harmonizes high computational efficiency with superior reasoning and agentic AI performance through DeepSeek Sparse Attention (DSA) and scalable reinforcement learning. The model achieves gold-medal performance in the 2025 International Mathematical Olympiad (IMO) and International Olympiad in Informatics (IOI), performing comparably to GPT-5.
This model is ready for commercial/non-commercial use.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA DeepSeek-V3.2 Model Card
GOVERNING TERMS: This trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Community Model License. Additional Information: MIT License.
Global
Use Case: DeepSeek-V3.2 is designed for advanced reasoning tasks, agentic AI applications, tool use scenarios, and complex problem-solving in domains requiring high computational reasoning such as mathematics, programming competitions, and enterprise AI assistants. The model integrates reasoning into tool-use scenarios through a large-scale agentic task synthesis pipeline.
Build.NVIDIA.com: 12/16/2025 via link
Huggingface: 12/01/2025 via link
References:
The DeepSeek-V3.2 family includes multiple specialized variants:
| Variant | Description | Primary Use |
|---|---|---|
| DeepSeek-V3.2 | Standard version optimized for general reasoning and agentic tasks | Balanced reasoning and tool use |
| DeepSeek-V3.2-Speciale | High-compute variant with enhanced reasoning capabilities, surpassing GPT-5 | Deep reasoning tasks only (no tool calling) |
| DeepSeek-V3.2-Exp | Experimental version | Research and development |
Architecture Type: Transformer
Network Architecture: DeepSeek Sparse Attention MoE
Total Parameters: 685B
Base Model: DeepSeek-V3.2-Exp-Base
Input Types: Text
Input Formats: String
Input Parameters: One Dimensional (1D)
Other Input Properties: Supports multi-turn conversations with system prompts, user messages, and assistant responses. Includes a new "developer" role exclusively for search agent scenarios. Utilizes an updated chat template with "thinking with tools" capability.
Output Types: Text
Output Format: String
Output Parameters: One Dimensional (1D)
Other Output Properties: Supports structured JSON output, function/tool calling, and reasoning content. Output can include explicit "thinking" traces when enabled via reasoning_content parameter.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engines:
Supported Hardware:
Operating Systems: Linux
Additional Testing Statement: The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
DeepSeek-V3.2 (2025)
Data Modality: Text
Training Data Collection: Undisclosed
Training Labeling: Undisclosed
Training Properties: The model was trained using a scalable reinforcement learning framework with robust RL protocol and post-training compute scaling.
Testing Data Collection: Undisclosed
Testing Labeling: Undisclosed
Testing Properties: Undisclosed
Evaluation Benchmark Score: Undisclosed
Evaluation Data Collection: Automated
Evaluation Labeling: Human
Evaluation Properties: Evaluated on competitive programming (IOI 2025, ICPC World Finals), mathematical reasoning (IMO 2025, CMO 2025), and general reasoning benchmarks comparing against frontier models like GPT-5 and Gemini-3.0-Pro.
Acceleration Engine: Transformers, vLLM with DeepSeek Sparse Attention optimization Test Hardware: The model is deployable on NVIDIA H100 and H200 GPUs. Precision formats available include FP8, BF16, F32, and F8_E4M3 for optimized inference.
For local deployment, recommended sampling parameters:
1. DeepSeek Sparse Attention (DSA) An efficient attention mechanism that substantially reduces computational complexity while preserving model performance, specifically optimized for long-context scenarios.
2. Scalable Reinforcement Learning Framework Robust RL protocol with scaled post-training compute enables performance comparable to GPT-5. The high-compute variant (DeepSeek-V3.2-Speciale) surpasses GPT-5.
3. Large-Scale Agentic Task Synthesis Pipeline Novel synthesis pipeline that systematically generates training data at scale to integrate reasoning into tool-use scenarios, improving compliance and generalization in complex interactive environments.
DeepSeek-V3.2 introduces significant updates to its chat template compared to prior versions:
developer role exclusively for search agent scenarios (not accepted in official API)Important Note: This release does not include a Jinja-format chat template. Refer to the Python encoding scripts in the encoding/ folder of the model repository for message encoding and parsing.
DeepSeek-V3.2-Speciale A high-compute variant designed exclusively for deep reasoning tasks. Important limitations:
The model includes verified final submissions for:
Submission files are available in the repository's assets/olympiad_cases folder for community verification.
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here