이 모델의 강점은 무엇인가요?

MIT license allows commercial use Extremely low API costs Efficient MoE architecture Supports 128K context

이 모델의 약점은 무엇인가요?

Japanese processing inferior to specialized models Servers may be located in mainland China Slightly slow inference speed

어떤 용도에 가장 적합한가요?

Large-scale processing focused on cost savings On-premises model deployment Research on MoE architecture Embedding into commercial services

모델 목록으로

DeepSeek오픈소스

DeepSeek V3.2

Name: DeepSeek V3.2
Price: 0.27 USD
Author: DeepSeek

An open-source large-scale model developed by China's DeepSeek-AI. It adopts the Mixture-of-Experts (MoE) architecture, with an efficient design where only a portion of the 685B parameters are activated during inference. While available for commercial use under the MIT license, its API fees are also extremely low.

파라미터

685B (MoE)

컨텍스트

128K

라이선스

MIT

출시일

2026-03-28

일본어 처리 능력

✅High-Quality JP

Multilingual model with strong Japanese language processing capabilities.

API 가격

입력 가격 (1M 토큰당)

$0.27

출력 가격 (1M 토큰당)

$1.1

과금 모드: standard

강점

・MIT license allows commercial use
・Extremely low API costs
・Efficient MoE architecture
・Supports 128K context

약점

・Japanese processing inferior to specialized models
・Servers may be located in mainland China
・Slightly slow inference speed

활용 사례

・Large-scale processing focused on cost savings
・On-premises model deployment
・Research on MoE architecture
・Embedding into commercial services

심층 분석

Arena Elo

1485

Thinking variant, #3 overall on BenchLM

SWE-Bench Verified

80.8%

vs GPT-5.2: 80.0% (paper claim)

Input Price

$0.28/1M tokens

~20x cheaper than GPT-5.2

Output Price

$1.10/1M tokens

~50x cheaper than GPT-5.2

Context Window

128K tokens

Standard frontier length

Total Parameters

685B

37B active via MoE architecture

License

MIT

Fully open-source, commercial use allowed

강점

・Extraordinary cost-to-performance ratio with pricing ~20x lower than GPT-5.2
・MIT license enables unrestricted commercial use and self-hosting
・Gold-medal level performance in IMO and IOI competitions
・DeepSeek Sparse Attention dramatically improves long-context efficiency
・Exceptional agentic capabilities with integrated thinking and tool use
・Strong coding performance competitive with frontier models

약점

・Slower inference speed (43 t/s measured) compared to most API competitors
・Creative writing and conversational tone lag behind proprietary models
・128K context window is smallest among major frontier models
・Chinese jurisdiction raises data privacy concerns for some enterprises
・Weaker safety guardrails compared to Anthropic or OpenAI offerings
・Occasional inconsistencies in complex multi-step instruction following

경쟁사 비교

Model	Arena	SWE	GPQA	Price
GPT-5 High	1550+	80.0%	85.7%	$15/$60
Gemini 3.0 Pro	1520+	76.2%	91.9%	$10/$40
Kimi K2 Thinking	1480+	71.3%	84.5%	Est. $5/$20
Claude 4.5 Sonnet	1475	77.2%	83.4%	$3/$15

개요

DeepSeek V3.2 represents a paradigm shift in open-source AI, delivering near-frontier performance at a fraction of proprietary model costs. This 685B parameter Mixture-of-Experts model activates only 37B parameters during inference, achieving remarkable computational efficiency while maintaining competitive benchmark scores. The model's DeepSeek Sparse Attention mechanism reduces complexity from O(L²) to O(Lk) for long-context processing, addressing a critical efficiency bottleneck in transformer architectures. Released under the MIT license in December 2025, V3.2 bridges the gap between open-source and closed-source models, particularly excelling in mathematical reasoning (gold medals in IMO and IOI), coding tasks, and agentic capabilities. Its 'thinking with tools' innovation integrates chain-of-thought reasoning with tool execution, enabling more sophisticated problem-solving approaches. At approximately $0.28 per million input tokens, it offers approximately 20x cost savings over GPT-5.2 while delivering comparable performance on key benchmarks. The model's significance extends beyond raw performance metrics. It demonstrates that frontier-level AI capabilities need not require frontier-level pricing or proprietary restrictions. For organizations prioritizing cost efficiency, data sovereignty, and customization potential, DeepSeek V3.2 presents a compelling alternative to closed-source offerings, though with trade-offs in speed, creative capabilities, and certain safety features.

벤치마크 및 성능

DeepSeek V3.2 achieves impressive benchmark performance across multiple domains. Based on the technical report and independent evaluations: | Benchmark | DeepSeek V3.2 | GPT-5 High | Gemini 3.0 Pro | Notes | |-----------|---------------|------------|----------------|-------| | GPQA Diamond | 82.4% | 85.7% | 91.9% | Scientific reasoning | | AIME 2025 | 93.1% | 94.6% | 95.0% | Mathematical reasoning | | MMLU-Pro | 85.0% | 87.5% | 90.1% | Broad knowledge | | HLE (text) | 25.1% | 26.3% | 37.7% | Challenging reasoning | | SWE-Bench Verified | 73.1% | 74.9% | 76.2% | Real-world coding | | LiveCodeBench | 83.3% | 84.5% | 90.7% | Coding benchmarks | | CodeForces Rating | 2386 | 2537 | 2708 | Competitive coding | | τ²-Bench | 80.3% | 80.2% | 85.4% | Tool-use benchmarks | | BrowseComp | 67.6%* | 54.9% | - | Search agent (with context mgmt) | *Score with context management strategy applied. The DeepSeek-V3.2-Speciale variant achieves even higher scores on reasoning tasks, with gold-medal performance in IMO 2025 (35/42) and IOI 2025 (492/600). However, it requires significantly more tokens (23-45k vs 16-21k for standard benchmarks) to achieve these results, impacting cost and latency. In agentic scenarios, DeepSeek V3.2 significantly narrows the gap between open and proprietary models. It scores 70.2% on SWE-Bench Multilingual (vs GPT-5's 55.3%) and 46.4% on Terminal-Bench 2.0 with Claude Code framework, demonstrating strong real-world coding agent capabilities. The model's 'thinking with tools' approach shows particular strength in complex, multi-step reasoning scenarios that require external tool integration.

상세 비교

**DeepSeek V3.2 vs GPT-5:** - **Pricing:** DeepSeek costs ~$0.28/$1.10 vs GPT-5's ~$15/$60 per 1M tokens (~20-50x cheaper) - **Performance:** GPT-5 leads on most benchmarks by 1-3 percentage points, but DeepSeek achieves gold-medal math competition performance - **Context:** Both 128K context windows - **Openness:** DeepSeek MIT license (fully open) vs GPT-5 proprietary - **Speed:** GPT-5 faster inference (est. 60-80 t/s vs DeepSeek's 43 t/s) - **Use Case:** GPT-5 for maximum accuracy; DeepSeek for cost-sensitive, high-volume, or self-hosted applications **DeepSeek V3.2 vs Gemini 3.0 Pro:** - **Pricing:** DeepSeek ~10-15x cheaper - **Performance:** Gemini leads on reasoning benchmarks (GPQA 91.9% vs 82.4%), but DeepSeek competitive on coding agents - **Context:** Gemini offers 1M tokens vs DeepSeek's 128K - **Multimodal:** Gemini natively multimodal; DeepSeek text-only - **Strengths:** Gemini for multimodal/long-context; DeepSeek for cost efficiency and coding **DeepSeek V3.2 vs Kimi K2 Thinking:** - **Pricing:** Both budget options, DeepSeek slightly cheaper - **Performance:** DeepSeek achieves comparable scores with fewer output tokens (16k vs 24k on AIME) - **Architecture:** Both use advanced reasoning approaches - **Openness:** DeepSeek MIT license; Kimi licensing less clear - **Regional:** Both Chinese models, but DeepSeek more internationally accessible

커뮤니티 평가

The AI development community has responded enthusiastically to DeepSeek V3.2, with particular focus on its value proposition. On GitHub, the DeepSeek-V3.2 repository has gained 1,565 stars and 158 forks, indicating significant developer interest. The model has been downloaded over 4.2 million times on HuggingFace within its first month. Developer sentiment centers on several key themes: 1. **Cost Revolution:** Many highlight the 20x cost advantage over GPT-5.2 as transformative for startups and high-volume applications. One developer noted: 'This changes the economics of AI - we can now afford to run sophisticated analysis on entire codebases.' 2. **Self-Hosting Appeal:** The MIT license resonates strongly with organizations concerned about data privacy, vendor lock-in, or regulatory compliance. Enterprise teams appreciate the ability to deploy on-premises without licensing restrictions. 3. **Agentic Potential:** The integrated 'thinking with tools' capability has excited the AI agent development community. Developers report improved results on complex multi-step tasks that require both reasoning and tool execution. 4. **Math/Code Focus:** The model's strong performance on mathematical reasoning and coding benchmarks has made it a favorite for technical applications. Researchers note its ability to construct proofs and solve competition-level problems. Some concerns have been raised about speed limitations and creative writing capabilities, but these are generally viewed as acceptable trade-offs given the pricing. The community has developed various optimization techniques and context management strategies to work around the 128K context limitation.

활용 사례

**1. High-Volume API Applications:** Ideal for applications processing large volumes of text where cost is primary concern. Example: E-commerce product description generation, customer support automation, or content moderation systems. With pricing 20x lower than premium models, organizations can process millions of queries daily without breaking budgets. **2. Self-Hosted Enterprise Solutions:** Perfect for regulated industries (finance, healthcare, government) requiring data sovereignty. Organizations can deploy DeepSeek V3.2 on-premises under MIT license, maintaining full control over data and models while avoiding vendor lock-in. Example: Internal knowledge base analysis, document processing, or sensitive data analysis where data cannot leave corporate networks. **3. Agentic Workflow Development:** The integrated thinking and tool-use capabilities make it excellent for developing complex AI agents. Example: Automated research agents that combine search, code execution, and reasoning to solve problems; coding assistants that can understand requirements, write code, test it, and debug issues in a single workflow. **4. Mathematical/Scientific Research:** Gold-medal performance in mathematics competitions makes it valuable for research applications. Example: Assisting mathematicians with proof verification, exploring mathematical conjectures, or generating and testing hypotheses in scientific domains. **When to choose over alternatives:** - **Over GPT-5:** When cost savings outweigh marginal performance gains - **Over Gemini:** When multimodality isn't needed and cost efficiency is priority - **Over Claude:** When open-source self-hosting is required - **Over other open-source models:** When you need frontier-level performance with proven benchmarks