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":

Model reasons autonomously and produces results
Results verified with code interpreter or math solver
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

Model	Parameters	VRAM	AIME	MATH
QwQ-32B	32B	~24GB	✅ Beats o1-preview	✅ Beats o1-preview
DeepSeek-R1	671B (37B active)	1500GB+	✅ Top-tier	✅ Top-tier
o1-preview	Unknown	Cloud	Baseline	Baseline

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

All Tags

Items tagged with Reasoning (2)

Claude Opus 4.5

QwQ-32B-Preview