DeepSeek‑V3.2‑Exp thinking

DeepSeek V3.2 Exp Thinking is an advanced hybrid reasoning AI model built explicitly to amplify multi-step, complex reasoning and deep cognitive processing tasks. It extends the capabilities of the earlier V3.1 series by focusing on enhanced "thinking" mode performance, enabling superior contextual understanding and dynamic problem solving in domains like software development, research, and knowledge-intensive industries. Designed for enterprise-grade deployment and research-driven workflows, DeepSeek V3.2 Exp Thinking features optimized token handling, faster inference, and richer multimodal data interpretation that supports robust, stepwise thought processes.

Technical Specifications

• Architecture: Transformer-based model with DeepSeek Sparse Attention (DSA) for selective token attention
• Parameters: 671 billion total, with 37 billion active during inference
• Context Window: Up to 128K tokens
• Sparse Attention: Focused on selecting only the most relevant tokens, reducing computational load from quadratic to near-linear scaling with context length‍
• Thinking Mode: Chain-of-Thought generation prior to answers‍
• Training Efficiency: Similar training regime as V3.1-Terminus but with reduced computational cost due to DSA

Performance Benchmarks

Overall, DeepSeek-V3.2-Exp maintains performance on par with V3.1-Terminus in complex reasoning tasks. Slight variations occur across specific benchmarks, with strengths in mathematics contests like AIME 2025 and programming challenges (Codeforces).

Key Features

• Chain-of-Thought Reasoning: Generates explicit intermediate reasoning steps before final answers, enhancing transparency and complex problem-solving.
• Thinking Mode that activates multi-step, logical reasoning processes for complex problem-solving.‍
• DeepSeek Sparse Attention (DSA): Fine-grained token selection for long contexts reduces compute costs while maintaining output quality.‍
• Large Context Window: Supports up to 128K tokens, suitable for multi-document workflows and deep knowledge integration.‍
• Streaming Support: Enables streaming of reasoning content and final outputs for real-time interaction.

API Pricing

• 1M input tokens (CACHE HIT): $0.0294
• ‍1M input tokens (CACHE MISS): $0.294
• 1M output tokens: $0.441

Use Cases

• Complex reasoning tasks requiring stepwise deduction, such as mathematical problem solving and logical puzzles.
• Document analysis and summarization where large context windows and structured reasoning are crucial.
• Conversational agents needing explicit reasoning transparency for trust and explainability.
• Knowledge-heavy applications involving multiple linked documents or extensive logs.
• Tool-augmented AI agents where integrating chain-of-thought and function calls improves task control.

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Code Sample

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Comparison with Other Models

vs DeepSeek-V3.1-Terminus: V3.2-Exp uses sparse attention to reduce computation but has near-identical output quality. V3.2-Exp’s Thinking mode explicitly exposes chain-of-thought reasoning, which V3.1 lacks.

vs OpenAI GPT-4o: GPT-4o offers high-quality responses but with costly processing for very long contexts, while DeepSeek scales efficiently to 128K tokens. DeepSeek’s sparse attention enables faster long-context reasoning, whereas GPT-4o relies on dense attention. GPT-4o has broader multimodal support, but DeepSeek focuses on optimized textual reasoning transparency.

vs Qwen-3: Both models support large contexts, but DeepSeek’s sparse attention reduces computational costs on extended inputs. DeepSeek provides explicit chain-of-thought in Thinking mode; Qwen-3 focuses more on general multimodal capabilities.

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