QWQ-32B vs DeepSeek-R1

Which is the best reasoning LLM?

Finally, after trending for about a month, DeepSeek-R1 is overthrown by none other than its Chinese counterpart, Alibaba, which has released the QWQ-32B model, an SOTA reasoning model with just 5% parameters as DeepSeek-R1.

Data Science in Your Pocket – No Rocket Science

What is Alibaba QWQ-32B?

QwQ-32B is a 32-billion-parameter language model developed by Alibaba’s Qwen team. It is optimized for reasoning, mathematical problem-solving, and coding. Despite being significantly smaller than models like DeepSeek-R1 (671B parameters), it delivers comparable performance through advanced reinforcement learning techniques.

Key Features QWQ-32B

• Reinforcement Learning Optimization — Utilizes a multi-stage RL training process to refine mathematical reasoning, coding proficiency, and problem-solving.
• Advanced Math & Coding Capabilities — Incorporates an accuracy verifier for math problems and a code execution server to ensure functional correctness.
• Enhanced Instruction Following — Additional RL training improves alignment with human preferences and instruction comprehension.
• Agent-Based Reasoning — Adapts to environmental feedback, enhancing logical decision-making.
• Competitive Performance — Despite its smaller size, QwQ-32B performs on par with much larger models in various benchmarks.
• Extended Context Length — Supports 131,072 tokens, allowing it to handle long documents, complex proofs, and extensive codebases.
• Multilingual Support — Works across 29+ languages, making it suitable for global applications.
• It’s open-sourced as well

DeepSeek-R1 vs QWQ-32B: Which reasoning LLM is better?

QWQ-32B is taken as a direct rival of DeepSeek-R1 and, given the size, may even overtake it. Let’s compare the two models side by side and see which LLM is better:

• Size: QwQ-32B has 32 billion parameters, making it significantly smaller and more efficient than DeepSeek-R1, which has 671 billion parameters. This allows QwQ-32B to run on less powerful hardware while maintaining strong performance.
• Mathematical Reasoning (AIME24): Both models achieve nearly identical scores (79.5 for QwQ-32B vs. 79.8 for DeepSeek-R1), demonstrating that QwQ-32B can perform high-level mathematical reasoning comparable to a model over 20 times its size.
• Coding Proficiency: QwQ-32B outperforms DeepSeek-R1 on LiveBench (73.1 vs. 71.6) but lags slightly behind on LiveCodeBench (63.4 vs. 65.9). This suggests that QwQ-32B excels in code functionality and execution but may have minor weaknesses in specific coding benchmarks.
• Logical Reasoning: QwQ-32B achieves a higher score on BFCL (66.4 vs. 60.3), indicating stronger capabilities in structured and logical problem-solving, making it better suited for tasks that require multi-step reasoning.
• Web Search Capability: QwQ-32B integrates stronger real-time search capabilities, allowing it to access and process updated information more effectively, while DeepSeek-R1 has more limited web search functionality.
• Image Input Support: DeepSeek-R1 has built-in support for processing and analyzing images, whereas QwQ-32B is limited to text-based tasks, making DeepSeek-R1 the better choice for multimodal applications.
• Computational Efficiency: QwQ-32B is designed to run on significantly lower computational resources than DeepSeek-R1, making it more accessible for users who require strong AI performance without needing large-scale infrastructure.
• Speed: QwQ-32B processes most tasks faster due to its optimized architecture, whereas DeepSeek-R1, being much larger, can take longer to generate responses, especially in real-time interactions.
• Accuracy: QwQ-32B delivers high accuracy but may occasionally miss finer details in complex tasks. DeepSeek-R1, while also highly accurate, sometimes introduces minor execution errors, particularly in coding-related outputs.

When to Use QwQ-32B vs. DeepSeek-R1

Use QwQ-32B When:

• You Need High Reasoning & Coding Accuracy on Limited Resources: With its smaller size (32B parameters), QwQ-32B offers top-tier performance without requiring high-end infrastructure. Ideal for individuals and teams with constrained computing power.
• Logical & Mathematical Reasoning is the Priority: QwQ-32B outperforms DeepSeek-R1 in logical reasoning (BFCL: 66.4 vs. 60.3) and matches its math skills, making it great for structured problem-solving.
• You Want Faster Execution for Text-Based Tasks: Since it’s smaller and optimized, QwQ-32B processes responses quicker, making it more efficient for real-time applications.
• Web Search & Real-Time Data Retrieval Are Important: QwQ-32B has a stronger web search capability, making it a better choice for fetching up-to-date information.
• You’re Focused on Multilingual Text Processing: With support for 29+ languages, QwQ-32B is a strong choice for multilingual tasks without relying on large-scale infrastructure.

Use DeepSeek-R1 When:

• You Need a Large-Scale, Multimodal Model: DeepSeek-R1 supports both text and image input, making it the better choice for multimodal AI applications like document analysis, image-captioning, and computer vision tasks.
• Accuracy in Code Execution Matters More Than Speed: DeepSeek-R1 scores slightly higher in LiveCodeBench (65.9 vs. 63.4), meaning it may be a better option for code generation that requires precise functional correctness.
• You Have Access to High-End Hardware: With 671B parameters, DeepSeek-R1 demands significant computational resources. If you have access to powerful GPUs or cloud-based AI infrastructure, it can be leveraged for large-scale applications.
• Complex AI-Assisted Research & Content Generation: DeepSeek-R1’s broader scope allows it to process and generate more detailed, nuanced responses, making it a strong option for extensive research, long-form content creation, and high-detail reasoning.
• You Need More Comprehensive Responses: While QwQ-32B is optimized for efficiency, DeepSeek-R1 may provide richer, more context-aware answers due to its sheer scale and larger training dataset.

Final Takeaway

• If you need fast, efficient, and accurate reasoning and coding with lower computational requirements, go for QwQ-32B.
• If you require multimodal support, large-scale AI applications, and deeper contextual reasoning with high-end hardware, DeepSeek-R1 is the better fit.

Conclusion

QwQ-32B is a highly efficient and capable reasoning model that delivers performance close to DeepSeek-R1 while being significantly smaller and more resource-efficient. It excels in logical reasoning, real-time web search, and computational efficiency, making it ideal for tasks requiring advanced problem-solving and coding. While it lacks image-processing capabilities, its speed and adaptability make it a strong choice for users who prioritize efficiency and versatility over sheer model size.

QWQ-32B vs DeepSeek-R1 was originally published in Data Science in your pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.