QwQ-32B-Preview

QwQ-32B-Preview (Qwen-with-Questions) is Alibaba's open-source reasoning model released in November 2024, designed to compete with OpenAI's o1-preview. With only 32B parameters, QwQ-32B achieves performance comparable to DeepSeek-R1 (671B parameters, 37B activated), representing a breakthrough in reasoning model efficiency.

Core Advantages

Ultra-Efficient Reasoning

• Parameters: 32B (vs DeepSeek-R1's 671B)
• VRAM: Only 24GB (vs DeepSeek-R1's 1500GB+)
• Performance: Competitive with SOTA reasoning models (DeepSeek-R1, o1-mini)

QwQ-32B proves that small models can achieve top-tier reasoning through reinforcement learning.

Beats OpenAI o1-preview

Per Alibaba's testing, QwQ-32B-Preview outperforms OpenAI's o1-preview on:

• AIME (American Invitational Mathematics Examination)
• MATH (mathematical problem set)

First open-source reasoning model to surpass closed-source commercial models.

Technical Approach

Trained using reinforcement learning with "outcome-based rewards":

1. Model reasons autonomously and produces results
2. Results verified with code interpreter or math solver
3. Model reviews and reformulates until correct

Learns self-correction and deep reasoning.

Open Source Benefits

• Apache 2.0 License: Commercial-friendly
• Available on: Hugging Face, ModelScope
• Self-deployable: No API vendor lock-in

Performance Comparison

Use Cases

• Mathematical problem-solving
• Scientific research requiring logical reasoning
• Code debugging and algorithm design
• Education with visible reasoning chains
• Resource-constrained environments needing advanced reasoning

Deployment

Hardware: 24GB VRAM minimum (RTX 4090, A5000) Frameworks: vLLM, TGI, SGLang, Ollama

Pros & Cons

Pros:

• 32B achieving 671B model performance
• Low hardware requirements (24GB VRAM)
• Open-source (Apache 2.0)
• Beats o1-preview on math reasoning
• Visible reasoning chain

Cons:

• Preview version, still optimizing
• Slower reasoning speed
• Math-focused, may underperform in general tasks

Conclusion

QwQ-32B-Preview is a major breakthrough in reasoning models, proving small models can match or exceed large closed-source models through reinforcement learning.

Best for: Advanced math reasoning, resource-constrained scenarios, self-deployable reasoning needs