Chinese tech giant Alibaba is set to rival DeepSeek AI with its release of QwQ (Qwen with Questions) -32B, an open-source AI reasoning model. It was first introduced by the company in November 2024, and then on the 6th of March 2025, Alibaba made an official launch of the large language model (LLM), QwQ-32B, which rivaled the common notion about LLMs.
It was believed over time that the more the parameters, the better the LLMs and the higher its performance. Parameters are internal variables or settings that are used to train LLMs to understand and generate human language, influencing the models behavior and performance.
AI models with higher parameters are perceived to be of a higher grade and performance level than those with fewer parameters. However, Alibaba’s QwQ-32B model is breaking the notion of “bigger is better” as it demonstrates impressive performance with a comparatively smaller parameter count.
The QwQ-32B model has only 32 billion parameters, which “pales” in comparison to DeepSeek R1’s 671 billion parameters. Yet, the performance level of the QwQ-32B model rivals DeepSeek R1. As stated in an article released by the company, “Qianwen QwQ-32B has achieved a qualitative leap in mathematics, code, and general capabilities, and its overall performance is comparable to DeepSeekR1.”
In a series of authoritative benchmark tests, a standardized method used to evaluate and compare the performance of AI models, the QwQ-32B was compared with other AI models. During the AIME24 evaluations to test the mathematical ability and Livecode Bench to test code ability, QwQ-32B performed at the same level as DeepSeek-R1 and performed better than OpenAI o1-mini.
In what is termed the “hardest LLMs evaluation,” LiveBench, led by Meta Chief Scientist Yann LeCun, designed to be immune to test set contamination and makes use of recent information sources and procedural questions, QwQ-32B surpassed DeepSeek-R1 in performance scores.
QwQ-32B has an exceptional reasoning capability due to its unique training technique involving reinforcement learning (RL), which allows adaptive learning via feedback loops. This boosts the model’s critical thinking and general intelligence level and allows the model to adapt and improve over time without specific instructions to do so.
Its fewer parameters reduce the computational costs for both training and inference by reducing energy consumption and hardware costs. It also gives the AI model a faster inference speed, which aids faster responses and a smooth user experience.
This makes it suitable for application scenarios with rapid response or high data security requirements, as it can be deployed locally (without the use of a remote server) on consumer-grade hardware. The QwQ-32B model gives developers and enterprises with limited resources the chance to create highly customized AI solutions.
QwQ-32B proves that it is possible to achieve high performance in complex tasks that involve reasoning while reducing computational burden. This serves as a bold step toward a sustainable and accessible AI.
The QwQ-32B model is currently open source, which means its source code, model weight, and training data are available to the general public to use, study, modify, and foster transparency in AI development. It is available on HuggingFace and ModelScope under the Apache 2.0 license and can be accessed through Qwen chat.
