DeepSeek-V3.2 Released! Here's the Analysis

Honghao Wang

02 Dec 2025 — 4 min read

DeepSeek V3.2 — Technical Report Overview

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> Source: DeepSeek, QbitAI

> Technical Report: Download PDF

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New Releases

DeepSeek has announced two powerful models:

DeepSeek-V3.2 — Balanced and practical, ideal for everyday Q&A, general Agent tasks, and tool usage.
Reasoning level: Comparable to GPT‑5, slightly below Gemini‑3.0‑Pro.
DeepSeek-V3.2-Speciale — Optimized for extreme reasoning.
Achievements: Surpassed Gemini‑3.0‑Pro in math and programming competitions; gold medals in IMO 2025 & IOI 2025.

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Evolution of the V3 Series

Past Milestones

DeepSeek V3 – Introduced MoE and Mixed Layer Attention (MLA).
DeepSeek V3.1 – Added Hybrid Reasoning Mode; improved Agent capabilities.
DeepSeek V3.1-Terminus – Enhanced language style and Agent stability.
DeepSeek V3.2-Exp – Trial run for DSA architecture.
DeepSeek V3.2 – First stable integration of DSA with large-scale RL and Agentic task synthesis.

Image source: Founder Park

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DeepSeek-V3.2-Speciale Highlights

Strengths:

Advanced instruction following
Mathematical proof
Logical verification

Recommended for: Complex mathematical reasoning, competitive programming, academic research.

Note:

Not optimized for casual conversation or writing.
Research-only.
No tool-usage support.

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Three Core Technical Upgrades

Core 1: DeepSeek Sparse Attention (DSA) — Efficient Long-Text Processing

DSA is the key architectural innovation in V3.2, reducing traditional attention complexity from O(L²) to O(L·k), significantly lowering inference costs for long-context tasks.

Key Features:

FP8 precision support
Compatible with MLA architecture
Training-friendly integration

Components:

Lightning Indexer — Selects top‑k relevant tokens quickly
Fine-Grained Token Selection — Improves relevance filtering
ReLU Activation — Enhances throughput

Two-Stage Training Strategy:

Dense Warm-up: Train indexer with dense attention
1,000 steps / 2.1B tokens
Sparse Attention: Use top‑2,048 key-value selections
15,000 steps / 943.7B tokens

Performance Gains (128k sequence length, H800 cluster):

Pre-fill per million tokens: $0.70 → ~$0.20
Decode per million tokens: $2.40 → ~$0.80

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Core 2: Scalable Reinforcement Learning

DeepSeek invested >10% of pre-training compute into RL — rare in open-source development — enabling significantly better performance on hard tasks.

GRPO Algorithm Enhancements:

Unbiased KL Estimation — Removes systemic bias.
Offline Sequence Masking — Filters deviating off-policy samples.
MoE Routing Consistency — Saves routing paths between inference/training.

Expert Distillation:

Trained in 6 domains: Math, Programming, Logical Reasoning, General Agent, Agent Programming, Agent Search.
Each supports thinking and non-thinking modes.
Specialist models generate domain datasets for final training.

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Core 3: Agent Capability Breakthrough

Context Management:

Retain reasoning content unless a new user message is added.
Tool-message additions keep reasoning history.
Tool results persist even if reasoning content is removed.

Cold Start Prompt Design:

System prompts guide natural tool calls in reasoning.
Tagged reasoning paths enforced for complex problem solving.

Automated Environment Synthesis:

1,827 task-oriented environments
85,000 complex prompts
Example: Travel Planning across multiple constraints

Specialized Agents:

Code Agent: Harvests millions of filtered GitHub issue–PR pairs, builds environments for Python, Java, JS, etc.
Search Agent: Generates training data from long-tail entity sampling, Q&A construction, and verification.

Evaluation Results:

SWE‑Verified solve rate: 73.1%
Terminal Bench 2.0 accuracy: 46.4%
Strong tool-use performance on MCP‑Universe & Tool‑Decathlon — close to closed-source standards.

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Deployment & Monetization Opportunities

Advanced models like DeepSeek‑V3.2 can be integrated into creative and commercial workflows via platforms such as AiToEarn官网 — an open-source global AI content monetization ecosystem.

Features:

Multi-platform publishing: Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X (Twitter).
AI content generation + analytics + ranking (AI模型排名).
Enables efficient monetization of AI-driven creations.

This infrastructure supports deploying agents in technical, creative, and multi-platform scenarios seamlessly.

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In Summary:

DeepSeek-V3.2 pushes the frontier in efficient long-text processing (DSA), massive-scale RL, and multi-capability Agents — while the Speciale version offers unmatched reasoning power for advanced computational challenges.

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Would you like me to also prepare a side-by-side performance table comparing V3.2 and Speciale against other major AI models? That could make this report even clearer.