Split View: 2025 오픈소스 AI 모델 완전 비교: DeepSeek R1 vs Llama 4 vs Qwen 3 vs Mistral — 누가 왕인가

2025 Open Source AI Models Showdown: DeepSeek R1 vs Llama 4 vs Qwen 3 vs Mistral

• 1. The 2025 Open Source AI Battlefield
  • From GPT-4 Dominance to Open Source Resurgence
  • Enterprise Adoption Surging
  • The Big Four
• 2. Model Profiles
  • DeepSeek R1 (671B / 37B MoE)
  • Llama 4 Scout (109B/17B) and Maverick (400B/17B)
  • Qwen 3 (0.6B ~ 235B)
  • Mistral 8x22B (176B / 39B MoE)
• 3. Benchmark Showdown
• 4. License Comparison
• 5. Local Deployment Practical Guide
  • 5.1 Getting Started with Ollama (Easiest)
  • 5.2 llama.cpp + GGUF Quantized Deployment
  • 5.3 Production Serving with vLLM
  • 5.4 Hardware Requirements
• 6. Cost Analysis: Cloud API vs Local vs Open Source API
  • 6.1 Cloud API Cost Comparison (Per 1M Tokens)
  • 6.2 Local Execution Cost Analysis
  • 6.3 Cost Optimization Strategies
• 7. Best Model by Use Case
  • 7.1 Comprehensive Recommendation Table
  • 7.2 Scenario-Based Detailed Guide
• 8. Five 2025 Open Source AI Trends
  • Trend 1: MoE Becomes the Default Architecture
  • Trend 2: The License Wars -- MIT vs Apache vs Meta Custom
  • Trend 3: The Small Model Rebellion
  • Trend 4: Distillation Technology Matures
  • Trend 5: The Rise of Chinese Models
• Practice Quiz
• 9. Practical Adoption Checklist
  • Pre-Adoption Evaluation
  • Phased Adoption Roadmap
  • Common Mistakes and Solutions
• References

1. The 2025 Open Source AI Battlefield

Until 2024, the AI market was dominated by OpenAI's GPT-4. But in 2025, the open-source camp launched a full-scale counteroffensive. The catalyst was DeepSeek from China.

From GPT-4 Dominance to Open Source Resurgence

In January 2025, DeepSeek R1 shattered the AI landscape. A 671B parameter MoE model released under the MIT license matched or exceeded GPT-4 on several benchmarks. The training cost was approximately 8.2 million USD, roughly 1/100th of GPT-4's estimated cost.

This shock triggered a chain reaction:

• Meta released Llama 4 Scout and Maverick, achieving an extraordinary 10M token context window
• Alibaba deployed the Qwen 3 series with a full lineup from 0.6B to 235B
• Mistral proved itself as Europe's champion with the 8x22B MoE model delivering best-in-class cost efficiency

Enterprise Adoption Surging

According to Red Hat's 2025 survey, enterprise adoption of open-source AI models increased by 82% year-over-year. Key drivers include:

• Data Sovereignty: Sensitive data never leaves internal infrastructure
• Cost Reduction: Local execution up to 50x cheaper than API costs
• Customization: Domain-specific models through fine-tuning
• Vendor Independence: Reduced dependency on specific providers

The Big Four

2. Model Profiles

DeepSeek R1 (671B / 37B MoE)

DeepSeek R1 was the biggest event in open-source AI in 2025. Published in Nature, the model's core innovation was training reasoning capabilities using pure reinforcement learning (RL) alone.

Architecture:

• Total Parameters: 671B
• Active Parameters: 37B (only ~5.5% active during inference)
• Number of Experts: 256 (8 activated per token)
• Context Length: 128K tokens
• Training Cost: ~8.2M USD (1/100th of GPT-4)

Benchmark Results:

• AIME 2024: 79.8% (math olympiad level)
• MATH-500: 97.3%
• HumanEval: 92.7%
• MMLU: 90.8%

Training Methodology Innovation:

DeepSeek R1's greatest innovation lies in its training approach. Instead of the traditional supervised fine-tuning (SFT) approach, reasoning capabilities were developed through pure reinforcement learning alone. During this process, the model naturally acquired Chain-of-Thought reasoning, self-verification, and error correction abilities.

DeepSeek R1 Training Pipeline:
1. Base model pretraining (large-scale text data)
2. Pure RL training (GRPO algorithm)
   - Reward: correctness only (no process rewards)
   - Result: self-discovers reasoning strategies
3. Distillation -> transfer to smaller models (1.5B ~ 70B)

License:

MIT License, the most permissive among all four models. Commercial use, modification, and redistribution are all unrestricted.

Llama 4 Scout (109B/17B) and Maverick (400B/17B)

Meta's Llama 4 ships in two variants, each targeting different use cases.

Scout Model (109B total / 17B active):

• Number of Experts: 16
• Context Length: 10M tokens (longest ever)
• Specialty: Optimized for efficient long-document processing
• Runs on a single H100 GPU

The Scout model's 10M token context is a breakthrough over existing models. This means processing thousands of pages of documents in a single pass.

Maverick Model (400B total / 17B active):

• Number of Experts: 128
• Shared Expert architecture for stable training
• Context Length: 1M tokens
• Natively multimodal (text + image)

Multimodal Capabilities:

Llama 4 was designed as multimodal from the ground up. The ability to process text and images simultaneously is built in, requiring no separate adapter.

Llama 4 Variant Comparison:
+--------------+-----------+-----------+
|              |  Scout    | Maverick  |
+--------------+-----------+-----------+
| Total Params |   109B    |   400B    |
| Active Params|   17B     |   17B     |
| Experts      |   16      |   128     |
| Context      |   10M     |   1M      |
| Multimodal   |   Yes     |   Yes     |
| GPU (FP16)   |   1xH100  |   8xH100  |
+--------------+-----------+-----------+

License:

Meta Custom License. Commercial use is permitted, but services exceeding 700 million monthly active users (MAU) require separate authorization from Meta.

Qwen 3 (0.6B ~ 235B)

Alibaba's Qwen 3 offers the broadest model lineup, ranging from 0.6B to 235B.

235B MoE Model (22B active):

• Apache 2.0 License
• 29 language support (strongest in CJK languages)
• 1M token context
• "Thinking Mode" support: switches between thinking and non-thinking modes in a single model

Full Lineup:

Qwen 3 Model Lineup:
+-- Dense Models
|   +-- Qwen3-0.6B  (mobile/IoT)
|   +-- Qwen3-1.7B  (edge devices)
|   +-- Qwen3-4B    (local chatbot)
|   +-- Qwen3-8B    (general local)
|   +-- Qwen3-14B   (coding/analysis)
|   +-- Qwen3-32B   (high-performance local)
|   +-- Qwen3-72B   (enterprise)
+-- MoE Model
    +-- Qwen3-235B  (22B active, best performance)

Thinking Mode Innovation:

Qwen 3 supports two modes in a single model:

• Thinking mode: Performs step-by-step reasoning for complex math, coding, and logic problems
• Non-thinking mode: Responds quickly to simple questions

Users can switch modes using /think and /no_think tags, allowing them to balance cost and latency according to the situation.

Multilingual Performance:

Supports 29 languages with dominant performance in CJK (Chinese, Japanese, Korean) languages. This is due to the inclusion of massive CJK corpora in the training data.

Mistral 8x22B (176B / 39B MoE)

Europe's representative Mistral is the value king.

Architecture:

• Total Parameters: 176B
• Active Parameters: 39B (2 of 8 experts active)
• Apache 2.0 License
• 65K token context

Strengths:

• Delivers GPT-4-adjacent performance at 1/10th the cost
• Optimized for European multilingual (English, French, German, Italian, Spanish)
• Strong in function calling and JSON output
• Excellent code generation

Mistral 8x22B Expert Routing:
Input Token -> Gate Network -> Top-2 Expert Selection
                                    |
                              Expert 1 (active)  <-- weighted sum --> Output
                              Expert 5 (active)  <--+
                              Expert 2 (inactive)
                              Expert 3 (inactive)
                              Expert 4 (inactive)
                              Expert 6 (inactive)
                              Expert 7 (inactive)
                              Expert 8 (inactive)

Cornerstone of the European AI Ecosystem:

Mistral plays a pivotal role in European enterprise AI adoption through proactive EU AI Act compliance and data sovereignty guarantees. It also provides its own AI service through the Le Chat platform.

3. Benchmark Showdown

The table below compares key benchmark results for each model. All figures are based on official announcements and may not reflect identical testing conditions.

Key Analysis:

• Math/Reasoning: Overwhelming dominance by DeepSeek R1. Leads competitors significantly on AIME and MATH-500
• Coding: DeepSeek R1 takes first place, Qwen 3 follows closely in second
• General Purpose: Llama 4 Maverick records high Arena ELO with well-balanced performance
• Cost Efficiency: Mistral 8x22B is the most efficient (best performance/cost ratio)

4. License Comparison

Licensing is one of the most critical factors when deploying open-source AI models in production.

License Selection Guide:

• Maximum freedom: DeepSeek R1 (MIT) - absolutely no restrictions
• Patent protection needed: Qwen 3 or Mistral (Apache 2.0) - includes patent retaliation clause
• Large-scale services: Avoid Llama 4 (beware of 700M MAU limit)
• Distillation purposes: DeepSeek R1 most clearly permits this

5. Local Deployment Practical Guide

5.1 Getting Started with Ollama (Easiest)

Ollama is the simplest way to run LLMs locally.

Installation:

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows
# Download installer from the official website

Running Models:

# DeepSeek R1 (various sizes)
ollama run deepseek-r1:1.5b    # minimum specs, 2GB RAM
ollama run deepseek-r1:7b      # general use, 8GB RAM
ollama run deepseek-r1:14b     # recommended, 16GB RAM
ollama run deepseek-r1:32b     # high performance, 32GB RAM
ollama run deepseek-r1:70b     # maximum performance, 64GB RAM

# Llama 4 Scout
ollama run llama4-scout:17b

# Qwen 3
ollama run qwen3:8b
ollama run qwen3:14b
ollama run qwen3:32b
ollama run qwen3:72b

# Mistral
ollama run mistral:8x22b

API Server Mode:

# Start default server (port 11434)
ollama serve

# Call API from another process
curl http://localhost:11434/api/generate -d '{
  "model": "deepseek-r1:14b",
  "prompt": "Implement quicksort in Python"
}'

5.2 llama.cpp + GGUF Quantized Deployment

When you need finer control, use llama.cpp directly.

Build:

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON   # For NVIDIA GPU
cmake --build build --config Release

Quantization Options:

Running Example:

# Download GGUF model (Hugging Face)
# e.g., DeepSeek-R1-Distill-Qwen-14B-Q4_K_M.gguf

# Run
./build/bin/llama-cli \
  -m DeepSeek-R1-Distill-Qwen-14B-Q4_K_M.gguf \
  -c 4096 \
  -ngl 99 \
  --temp 0.6 \
  -p "Explain how to set up a Redis cluster with Docker Compose"

5.3 Production Serving with vLLM

For production environments, vLLM is optimal.

# Install vLLM
pip install vllm

# Start server
python -m vllm.entrypoints.openai.api_server \
  --model deepseek-ai/DeepSeek-R1-Distill-Qwen-14B \
  --tensor-parallel-size 2 \
  --max-model-len 8192 \
  --port 8000

vLLM advantages:

• PagedAttention for maximum memory efficiency
• Continuous Batching for throughput optimization
• OpenAI-compatible API
• Automatic tensor parallel processing support

# Call via OpenAI-compatible API
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
    "messages": [
      {"role": "user", "content": "Write a Kubernetes CronJob manifest"}
    ],
    "temperature": 0.7,
    "max_tokens": 2048
  }'

5.4 Hardware Requirements

For individual users, 7B-14B quantized models are the practical choice. An RTX 4060 Ti 16GB or M-series Mac is sufficient.

6. Cost Analysis: Cloud API vs Local vs Open Source API

6.1 Cloud API Cost Comparison (Per 1M Tokens)

DeepSeek's API is approximately 18x cheaper on input and 36x cheaper on output compared to GPT-4o.

6.2 Local Execution Cost Analysis

Initial Investment:

Break-Even Calculation:

Scenario: 1M tokens per day usage:

GPT-4o API monthly cost: (2.50 + 10.00) x 30 = 375 USD/month
DeepSeek API monthly cost: (0.14 + 0.28) x 30 = 12.6 USD/month
Local RTX 4090 (electricity only): ~15 USD/month

RTX 4090 break-even vs GPT-4o: ~6 months
RTX 4090 break-even vs DeepSeek API: ~74 months (not recommended)

Conclusion: DeepSeek's API is already so affordable that individual users choosing local deployment do so primarily for privacy and offline access, not cost savings.

6.3 Cost Optimization Strategies

1. Hybrid Approach: Local for sensitive data, API for general tasks
2. Model Size Optimization: Not every task needs the largest model
3. Quantization: Q4_K_M delivers sufficient performance for most tasks
4. Caching: Cache results for frequently used prompts
5. Batch Processing: Non-real-time tasks can be batched for cost reduction

7. Best Model by Use Case

7.1 Comprehensive Recommendation Table

7.2 Scenario-Based Detailed Guide

Startup (Budget Constrained):

Recommended Stack:
- Development: DeepSeek R1 API (under 50 USD/month)
- Production: Qwen 3 14B on RTX 4090 (local)
- Rationale: Maximum performance at minimum cost

Enterprise (Regulatory Compliance Required):

Recommended Stack:
- Internal Documents: Qwen 3 72B on private cloud
- Customer Service: Llama 4 Maverick via API
- Analytics: DeepSeek R1 (MIT license = minimal legal risk)

Individual Developer:

Recommended Stack:
- Coding Assistant: DeepSeek R1 14B (Ollama, local)
- General Questions: DeepSeek API (cheapest)
- Learning: Qwen 3 8B (free, local, multilingual)

8. Five 2025 Open Source AI Trends

Trend 1: MoE Becomes the Default Architecture

Among major models released in 2025, 3 out of 4 adopted MoE architecture. This is no coincidence.

MoE advantages:

• Efficiency: Only 5-20% of total parameters are active, reducing inference cost
• Scalability: Performance improves by adding more experts
• Specialization: Each expert specializes in specific domains

Dense models (all parameters always active) are increasingly reserved for smaller models only.

Trend 2: The License Wars -- MIT vs Apache vs Meta Custom

DeepSeek's adoption of the MIT license sent shockwaves through the industry. It reignited debates about the true definition of "open source" and raised questions about whether Meta's license can legitimately be called "open source."

Trend 3: The Small Model Rebellion

One of 2025's most surprising findings was that a well-trained 8B model surpasses 2023's GPT-4V on some benchmarks.

This is thanks to:

• Data Quality Improvements: Quality over quantity in training data
• Distillation Technology: Efficient knowledge transfer from large models
• Architecture Improvements: Efficient techniques like GQA and SWA
• Shared Training Recipes: Community-driven optimization know-how

Trend 4: Distillation Technology Matures

DeepSeek R1's distillation model series (1.5B-70B) demonstrates the maturity of distillation technology.

Distillation Pipeline Example:
DeepSeek R1 671B (Teacher Model)
    | distill
DeepSeek R1 Distill 70B (retains 85% performance)
    | distill
DeepSeek R1 Distill 14B (retains 75% performance)
    | distill
DeepSeek R1 Distill 1.5B (retains 60% performance)

The key to distillation is transferring the teacher model's "thought process" to the student model. In DeepSeek R1's case, reasoning abilities acquired through pure RL are passed to smaller models via distillation.

Trend 5: The Rise of Chinese Models

Among the 2025 open-source AI Big Four, 2 are Chinese models (DeepSeek, Qwen). This carries several important implications:

• Technological Self-Reliance: Competitive models developed despite US chip export restrictions
• Cost Innovation: DeepSeek's 8.2M USD training cost shocked the industry
• Open Source Strategy: MIT/Apache licensing to capture the global developer ecosystem
• Geopolitical Implications: New discussions about AI technology polarization and cooperation

Practice Quiz

Test your knowledge with these questions.

9. Practical Adoption Checklist

Here is the essential checklist for deploying open-source AI models in production environments.

Pre-Adoption Evaluation

Technical Requirements:

• Does the model's VRAM requirement match your available hardware?
• Does it support the context length you need?
• Can it meet your latency requirements?
• Does it adequately support the languages you need?

Business Requirements:

• Is the license compatible with your commercial use case?
• Does it meet your data privacy requirements?
• Can you guarantee the necessary SLA (Service Level Agreement)?
• Do you have a long-term maintenance plan?

Operational Requirements:

• Is a monitoring system in place?
• Do you have a fallback strategy for outages?
• Is a model update pipeline designed?
• Is there a security audit process?

Phased Adoption Roadmap

Phase 1: PoC (2-4 weeks)
+-- Define use cases
+-- Select candidate models (2-3)
+-- Run benchmark tests
+-- Cost analysis

Phase 2: Pilot (4-8 weeks)
+-- Deploy to small team
+-- Monitor performance
+-- Collect feedback
+-- Evaluate fine-tuning needs

Phase 3: Production (8-12 weeks)
+-- Build infrastructure
+-- CI/CD pipeline
+-- Monitoring dashboard
+-- Documentation

Phase 4: Optimization (Ongoing)
+-- Cost optimization
+-- Performance tuning
+-- Model upgrades
+-- Team capability building

Common Mistakes and Solutions

Mistake 1: Choosing the largest model from the start

Solution: Start small and scale up incrementally. In many cases, a 14B model is sufficient.

Mistake 2: Underestimating quantization quality

Solution: Q4_K_M delivers nearly identical results to FP16 for most use cases. Always validate with benchmarks.

Mistake 3: Insisting on either API or local deployment exclusively

Solution: Adopt a hybrid approach. Local for sensitive data, API for bulk processing is optimal.

Mistake 4: Insufficient license review

Solution: Always review the license with your legal team before adoption. Llama 4's MAU limit in particular can become a constraint for growing services.

Mistake 5: Production deployment without monitoring

Solution: Build a system to monitor response quality, latency, and error rates in real time.

References

1. DeepSeek R1 Technical Report - "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning" (2025)
2. Meta AI - "Llama 4: Open Foundation Models" Official Blog (2025)
3. Alibaba Cloud - "Qwen3 Technical Report" (2025)
4. Mistral AI - "Mixtral 8x22B: A Sparse Mixture of Experts" (2024)
5. Red Hat - "The State of Enterprise Open Source AI 2025" Report
6. Nature - "Reinforcement Learning for Language Model Reasoning" (2025)
7. Ollama Official Documentation - ollama.com/docs
8. llama.cpp GitHub Repository - github.com/ggml-org/llama.cpp
9. vLLM Official Documentation - docs.vllm.ai
10. Hugging Face Open LLM Leaderboard (2025)
11. LMSYS Chatbot Arena Leaderboard (2025)
12. "The Economics of Open Source AI" - a16z Research (2025)
13. EU AI Act Official Document - Commission Regulation (EU) 2024/1689
14. "Scaling Laws for Mixture of Experts" - arXiv (2025)
15. NVIDIA DGX Spark Specifications - nvidia.com/dgx-spark
16. "Distillation of Reasoning: From Large to Small Language Models" (2025)
17. Alibaba DAMO Academy - "Multilingual LLM Benchmark Suite" (2025)