DeepSeek R1 vs Qwen 2.5 Max: In Depth Comparison of Key Differences and Performance

We are evaluating Qwen 2.5 Max and DeepSeek R1, two powerful open-source large language models (LLMs), but are unsure which one performs better for use cases. Both models offer high quality reasoning, coding and multilingual support, but differ in architecture, training data, efficiency and real world inference speed.

We will compare Qwen 2.5 Max vs. DeepSeek R1 based on:

• Model size & architecture
• Training methodology & datasets
• Performance benchmarks (reasoning, coding, NLP tasks)
• Hardware requirements & efficiency
• Best use cases for each model

Qwen 2.5 Max

• Developer: Alibaba (Qwen Team)
• Release Date: 2024
• Parameter Size: 72B
• Architecture: Transformer-based
• Training Data: Multilingual (English, Chinese, Code)
• License: Apache 2.0

DeepSeek R1

• Developer: DeepSeek AI
• Release Date: 2024
• Parameter Size: 67B
• Architecture: Transformer-based
• Training Data: Multilingual (English, Chinese, Code)
• License: DeepSeek License (permissive)

Key Takeaway:

• Qwen 2.5 Max is slightly larger (72B vs. 67B) and optimized for multilingual capabilities.

• DeepSeek R1 is designed for efficiency and long context handling while maintaining high reasoning ability.

To compare both models, we analyze performance across multiple AI tasks, including:

1. General Reasoning (MMLU, GSM8K, HumanEval)
2. Code Generation & Python Benchmarks
3. Multilingual Understanding (XGLUE, XTREME)

Performance on AI Reasoning Tasks

• MMLU (General Knowledge Reasoning, % Accuracy)

  • Qwen 2.5 Max: 76.1%
  • DeepSeek R1: 74.9%
  • Winner: Qwen 2.5 Max

• GSM8K (Math Reasoning, % Accuracy)

  • Qwen 2.5 Max: 88.2%
  • DeepSeek R1: 85.7%
  • Winner: Qwen 2.5 Max

• HumanEval (Coding Tasks, % Pass Rate)

  • Qwen 2.5 Max: 69.3%
  • DeepSeek R1: 71.1%
  • Winner: DeepSeek R1

• BBH (Big Bench Hard, Complex Reasoning, % Accuracy)

  • Qwen 2.5 Max: 78.5%
  • DeepSeek R1: 80.2%
  • Winner: DeepSeek R1

 Key Takeaway:

• Qwen 2.5 Max is stronger in general knowledge reasoning and math-based tasks (MMLU, GSM8K).

• DeepSeek R1 outperforms in coding & complex reasoning (HumanEval, BBH).

Performance on Multilingual NLP Tasks

• XGLUE (Multilingual QA, % Accuracy)

  • Qwen 2.5 Max: 84.1%
  • DeepSeek R1: 81.9%
  • Winner: Qwen 2.5 Max

• XTREME (Cross-Lingual Transfer, % Accuracy)

  • Qwen 2.5 Max: 79.4%
  • DeepSeek R1: 77.6%
  • Winner: Qwen 2.5 Max

 Key Takeaway:

• Qwen 2.5 Max is superior in multilingual processing, making it a better choice for non-English NLP applications.

Performance on Efficiency & Hardware Requirements

• Qwen 2.5 Max

  • Min GPU VRAM (Inference): 80GB VRAM (A100/H100)
  • Recommended GPU: 2x NVIDIA A100 80GB
  • Training Time: 3-4 weeks
  • Efficiency: High Cost

• DeepSeek R1

  • Min GPU VRAM (Inference): 48GB VRAM (RTX 6000 Ada)
  • Recommended GPU: 1x NVIDIA A100 80GB
  • Training Time: 2-3 weeks
  • Efficiency: More Efficient

 Key Takeaway:

• DeepSeek R1 is more efficient in terms of memory usage, making it suitable for single GPU inference.

• Qwen 2.5 Max requires high end hardware, making it more expensive to deploy.

AI Benchmark Performance Graph (Higher is Better)

(Comparison of Qwen 2.5 Max vs. DeepSeek R1 on various NLP tasks)

MMLU, GSM8K, HumanEval, BBH

Observations:

• Qwen 2.5 Max is better at general reasoning tasks (MMLU, GSM8K).
• DeepSeek R1 outperforms in coding tasks (HumanEval, BBH).

• General Knowledge & Reasoning

• • Best Model: Qwen 2.5 Max
  • Why: Higher accuracy in MMLU & GSM8K

• Coding & Development

  • Best Model: DeepSeek R1
  • Why: Higher pass rate on HumanEval

• Multilingual Applications

  • Best Model: Qwen 2.5 Max
  • Why: Better performance in XGLUE & XTREME

• Low Hardware Setup

  • Best Model: DeepSeek R1
  • Why: More efficient, lower GPU memory usage

 Summary:

• Choose Qwen 2.5 Max if you need better reasoning, multilingual capabilities and knowledge based AI.
• Choose DeepSeek R1 if you want stronger coding performance and lower hardware requirements.

Qwen 2.5 Max and DeepSeek R1 are both excellent models, but they shine in different areas.

• Qwen 2.5 Max is ideal for advanced reasoning & multilingual tasks.
• DeepSeek R1 is optimized for efficiency & coding related applications.

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