QwQ-32B is another artificial intelligence reasoning model from a Chinese e-commerce company, Alibaba’s Cloud. This model was first introduced in November 2024 as a QwQ-32B Preview to offer an open-source alternative. 

The Qwen team stated that they started with a basic model, which was QwQ-32 Preview and then added RL scaling. They first used RL scaling for coding and math tasks. After getting accurate and verified outcomes, the team applied it to general tasks, which improved overall performance, and now we have QwQ-32B. 

What is QwQ-32B?

Alibaba released an open-source QwQ-32B on March 6 to align with its goals of major investments in cloud and AI infrastructure. Its 32 billion parameters make it way too small compared to DeepSeek-R1 with 6,710 billion parameters. The model uses RL for critical thinking and adaptive learning. RL refers to Reinforcement Learning, and it is a part of the training process in which the AI models learn through responses over time. QwQ-32B is lightweight compared to its counterparts. That’s why it requires less powerful and comparatively inexpensive computing resources to run effectively. 

What is the difference between QwQ-32B Preview and QwQ-32B?

QwQ-32B Preview is released as an experimental research model and has its limitations. It is designed to showcase AI reasoning capabilities and works on users’ feedback for further development. It also has an open-source access on Hugging Face, where you can test it and give your feedback. 

QwQ32B Preview has some limitations, such as this model unexpectedly switching between languages, which makes things less clear. It also has a recursive learning loop in which it gets stuck with repetitive learning that makes responses too long and confusing. Along with that, it has to improve in areas like common sense and reasoning while requiring strong safety features to deploy it. 

QwQ-32B is a refined and official release that is designed to handle advanced reasoning tasks and solve challenges like coding and mathematical reasoning. It is released as an open-weight model with Apache 2.0 license, which makes it free to use and distribute. This model is improved after gaining insights on the Preview version. 

QwQ-32B Vs DeepSeek

DeepSeek uses a Mixture of Experts (MoE) architecture and has a total of 671 billion parameters. On the contrary, QwQ-32B is developed by scaling Reinforcement Learning (RL) techniques with 32 billion parameters. Let’s see if QwQ-32B has an impact on the country’s leading AI model, DeepSeek-R1.

 Well, QwQ-32B is giving a performance challenge to DeepSeek R1 in various standards. It outperformed both Open AI and DeepSeek R1 versions in math and coding tests. QwQ-32B even scored higher in evaluations like IFEval and LiveBench. 

DeepSeek-R1 was developed by a Chinese AI company to solve tasks requiring mathematical problem-solving that need logical inference and real-time decision-making. DeepSeek R1 started with R1-Zero, which is a foundational model trained by using Reinforcement Learning. Although it does develop strong learning capabilities, its outputs are difficult to read and sometimes mix languages. This makes R1-Zero less reliable to use in real-world applications. 

To address these issues, DeepSeek learned through responses and made changes in R1’s development. The company made a hybrid approach by supervised fine-tuning and improving the model’s coherence. This strategy reduced problems like fragmented reasoning and language mixing. 

QwQ-32B, on the other hand, is designed to answer complex problems. Just like DeepSeek’s R1 QwQ-32B Preview shows shortcoming. The QwQ-2B is finely tuned to reason through complex problems.

Feature By Feature Comparison 

QwQ-32B, despite being much smaller in size, is tested on a range of benchmarks. It is surprisingly close to evaluating structured reasoning, math, and coding and performed near DeepSeek R1 levels. Here’s the comparison of both models in terms of different features. 

AI Reasoning Capabilities

Language models learn by predicting the next word. The models are fluent in outcomes due to their vast data, but they are not necessarily good at problem-solving. To solve these issues, QwQ-32B and DeepSeek are introduced by Reinforcement Learning feedback systems. In which they judge better when solving logical reasoning, coding, and mathematics. 

QwQ-32B moves one step ahead of DeepSeek by integrating agent-related capabilities to adapt to its reasoning based on environmental feedback. Which means that it memorizes patterns and refines its responses dynamically.

Smart Training Module

This is again an impactful aspect where QwQ-32B with only 32 billion parameters performs comparably to DeepSeek-R1 with 671 billion (with 37 billion activated) parameters. This means that scaling up RL makes the model as efficient as increasing its size does. 

Coding Capabilities Comparison

Coding capabilities are an essential requirement for AI models that are designed to assist with software development. LiveCodeBench, which is a contamination-free benchmark of Large Language Models, measures the capability of these models to generate and refine code. Its evaluation of general problem-solving skills shows that QwQ-32B, with 73.1, surpasses DeepSeek R1, scoring 71.6. This reinforces the idea that even a well-optimized, comparatively smaller model will efficiently do the structured tasks. 

In LiveCodeBench findings that are about coding capabilities, DeepSeek-R1 scored 65.9, and QwQ-32B scored 63.4. Which refers to QwQ-32B’s impressive ability to reason through coding problems.

Will the QwQ-32B Release Have an Impact on DeepSeek R1?

Although QwQ-32B just like any other traditional instruction tuned models, struggle with complex reasoning tasks. According to Qwen Teams research the reinforcement learning will have a significant impact on boosting this model’s ability to solve complex mathematical reasoning, coding and general problem-solving. 

QwQ-32B has undoubtedly benchmarked against DeepSeek R1 and shows competitive results with comparable performance, even with a smaller footprint. It is because QwQ-32B is built with multi-stage RL training to make certain the idea of enhanced coding proficiency and general problem solving it does have the ability to surpass DeepSeek R1 in the future. 

Conclusion

Reinforcement learning is a key driver for next-generation AI models. Scaling QwQ-32B with RL makes it a highly performant and effective reasoning model that will surely offer a test to its competitors.