This content originally appeared on Level Up Coding - Medium and was authored by AI TutorMaster
Who Will Dominate the Chinese AI Race?
“The war between AI models is not about who wins, but about who serves humanity better.”
— Elon Musk
Introduction
The development of large-scale AI models has seen rapid advancements, with both DeepSeek and Alibaba’s Qwen teams pushing the limits of model intelligence. DeepSeek R1 and Qwen 2.5 Max represent two of the most advanced AI models available today. Let’s explore their architectural differences, training methodologies, performance benchmarks, best features, and detailed usage instructions.
1. Model Overviews
DeepSeek R1
DeepSeek R1 is a high-performance AI model developed with reinforcement learning (RL) techniques. Unlike conventional models that rely on extensive supervised fine-tuning (SFT), DeepSeek R1 incorporates:
- Large-scale RL without SFT as an initial step (DeepSeek-R1-Zero).
- A post-training pipeline involving RL and SFT to refine reasoning abilities.
- Open-source distilled models (1.5B to 70B parameters) to support research communities.
- Enhanced self-verification, reflection, and long chain-of-thought (CoT) reasoning capabilities.
- Availability on Hugging Face for offline experimentation.
- Capabilities comparable to OpenAI-o1 in mathematical, coding, and reasoning tasks.
DeepSeek-R1 Models
please refer to DeepSeek repository.
Qwen 2.5 Max
Qwen 2.5 Max is Alibaba’s flagship AI model, designed with a Mixture-of-Experts (MoE) architecture for scalable and efficient computation. Key features include:
- Pretraining on over 20 trillion tokens.
- Supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF).
- Benchmark superiority in key AI evaluation metrics.
- OpenAI-compatible API for seamless integration.
- Availability in Qwen Chat for direct interaction.
- API access through Alibaba Cloud Model Studio.
“AI is the new electricity.”
— Andrew Ng, Co-founder of Google Brain
2. Best Features of DeepSeek R1 and Qwen 2.5 Max
DeepSeek R1 Best Features
- Superior reasoning and logic-based performance: Excels in problem-solving and mathematical reasoning.
- Efficient reinforcement learning (RL) techniques: Developed through RL without initial SFT, improving emergent reasoning capabilities.
- Open-source availability: Various distilled models allow easier adoption across different applications.
- Longer context capabilities: Supports extended responses and maintains coherence over longer conversations.
Qwen 2.5 Max Best Features
- Scalable Mixture-of-Experts (MoE) architecture: Offers greater efficiency for large-scale deployment.
- Superior language understanding and chat-based responses: Optimized for general-purpose AI tasks.
- API integration with Alibaba Cloud: Easily accessible for enterprise solutions.
- High token training dataset: Trained on 20 trillion tokens for improved accuracy and contextual awareness.
3. Architectural Differences
4. Performance Benchmarks
The models were evaluated across a variety of tasks, including knowledge-based reasoning (MMLU-Pro), coding (LiveCodeBench), and general AI capabilities (LiveBench).
Qwen 2.5 Max outperforms DeepSeek R1 in most benchmarks, demonstrating its edge in real-world AI tasks. However, DeepSeek R1 exhibits strong performance in reasoning-heavy applications.
5. How to Use DeepSeek R1 and Qwen 2.5 Max
Setting Up DeepSeek-R1 Locally With Ollama
Ollama simplifies running LLMs locally by handling model downloads, quantization, and execution seamlessly.
Step 1: Install Ollama
Download and install Ollama from the official website.
Step 2: Download and Run DeepSeek-R1
ollama run deepseek-r1
To run a specific model size:
ollama run deepseek-r1:Xb
Step 3: Running DeepSeek-R1 via API
ollama serve
Step 4: Accessing DeepSeek-R1 via Python
import ollama
response = ollama.chat(
model="deepseek-r1",
messages=[{"role": "user", "content": "is AI goood for technology?"}],
)
print(response["message"]["content"])
Using Qwen 2.5 Max
Step 1: Access Qwen Chat
Qwen 2.5 Max is available on Qwen Chat, where users can interact with it directly.
Step 2: Register for Alibaba Cloud API
To use Qwen 2.5 Max via API:
- Create an Alibaba Cloud account.
- Activate Alibaba Cloud Model Studio.
- Generate an API key.
Step 3: Using Qwen 2.5 Max via API
import openai
openai.api_base = "https://api.qwen.ai/v1"
openai.api_key = "your_api_key_here"
response = openai.ChatCompletion.create(
model="qwen-max-2025-01-25",
messages=[{"role": "user", "content": "Hello, Qwen!"}]
)
print(response["choices"][0]["message"]["content"])
6. Conclusion
DeepSeek R1 and Qwen 2.5 Max offer distinct advantages depending on application needs. DeepSeek R1 is best suited for complex reasoning, math, and coding tasks, while Qwen 2.5 Max excels in conversational AI and scalable deployment. By providing extensive API integrations and local deployment options, both models cater to a wide range of AI applications.
DeepSeek R1 vs. Qwen 2.5 Max: Simple Comparision was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Level Up Coding - Medium and was authored by AI TutorMaster
AI TutorMaster | Sciencx (2025-02-03T02:01:53+00:00) DeepSeek R1 vs. Qwen 2.5 Max: Simple Comparision. Retrieved from https://www.scien.cx/2025/02/03/deepseek-r1-vs-qwen-2-5-max-simple-comparision/
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