DeepSeek-V3.2 Thinking
Active Parameters
671B
Context Length
128K
Modality
Text
Architecture
Mixture of Experts (MoE)
License
MIT
Release Date
10 Jan 2026
Knowledge Cutoff
-
Technical Specifications
Total Expert Parameters
37.0B
Number of Experts
-
Active Experts
-
Attention Structure
Multi-Head Attention
Hidden Dimension Size
-
Number of Layers
-
Attention Heads
-
Key-Value Heads
-
Activation Function
-
Normalization
-
Position Embedding
Absolute Position Embedding
System Requirements
VRAM requirements for different quantization methods and context sizes
DeepSeek-V3.2 Thinking
DeepSeek-V3.2 Thinking is the reasoning-enhanced variant of DeepSeek-V3.2, specifically optimized for complex problem-solving through chain-of-thought reasoning. Based on the same 671B parameter MoE architecture with 37B activated parameters, this model is fine-tuned to produce detailed reasoning traces before generating final answers. Excels at multi-step logical reasoning, mathematical proofs, algorithmic problem-solving, and tasks requiring explicit step-by-step thinking. Achieves enhanced performance on reasoning benchmarks: 94.8% on MATH-500 (with reasoning), 85.2% on Codeforces, and 73.4% on AIME. The thinking mode provides transparency into the model's reasoning process, making it ideal for educational applications, research, debugging complex logic, and scenarios where interpretability is crucial. Supports 128k context window with strong multilingual reasoning capabilities. MIT licensed.
About DeepSeek-V3
DeepSeek-V3 is a Mixture-of-Experts (MoE) language model comprising 671B parameters with 37B activated per token. Its architecture incorporates Multi-head Latent Attention and DeepSeekMoE for efficient inference and training. Innovations include an auxiliary-loss-free load balancing strategy and a multi-token prediction objective, trained on 14.8T tokens.
Other DeepSeek-V3 Models
Evaluation Benchmarks
Rank
#21
| Benchmark | Score | Rank |
|---|---|---|
Data Analysis LiveBench Data Analysis | 0.73 | ⭐ 6 |
Mathematics LiveBench Mathematics | 0.85 | ⭐ 8 |
Reasoning LiveBench Reasoning | 0.77 | 14 |
Agentic Coding LiveBench Agentic | 0.40 | 18 |
Coding LiveBench Coding | 0.70 | 31 |
Graduate-Level QA GPQA | 0.82 | 42 |
Rankings
Overall Rank
#21
Coding Rank
#35
GPU Requirements
Full Calculator
Choose the quantization method for model weights
Context Size: 1,024 tokens