The video introduces Quen 3, an open-source AI model with 235 billion parameters that outperforms many proprietary models in benchmarks like coding and reasoning, thanks to its hybrid thinking approach and extensive training on 36 trillion tokens. It highlights Quen 3’s ability to dynamically adjust reasoning depth, support tool calling, and deliver high performance and speed, making it a significant breakthrough in accessible AI technology.
The video introduces Quen 3, an open-source AI model that is highly comparable to the proprietary Gemini 2.5 Pro, showcasing impressive benchmarks across various tasks. Quen 3’s flagship model, with 235 billion parameters and 22 billion active parameters, outperforms many frontier models like Gemini 2.5 Pro and Deepseek R1 in several benchmarks, including code performance, reasoning, and function calling. The model’s efficiency is highlighted by its ability to excel in coding and agentic tasks, with faster inference times and higher scores on tests like Live Codebench and BFCL. The presenter emphasizes that Quen 3’s open-source nature and open weights make it a significant breakthrough in accessible AI technology.
A key feature of Quen 3 is its hybrid thinking approach, which allows the model to dynamically adjust its reasoning depth based on the task. It can operate in a non-thinking mode for quick responses or switch to a more thoughtful, step-by-step reasoning process for complex problems. This flexibility is controlled through a “thinking budget,” enabling users to optimize performance and efficiency depending on their specific needs. The model’s ability to balance speed and depth makes it particularly suitable for applications like coding, where different tasks require different levels of reasoning.
The video also discusses the extensive pre-training process of Quen 3, which involved nearly twice the amount of data used for Quen 2.5, totaling 36 trillion tokens across 119 languages. The training incorporated synthetic data, including textbooks, question-answer pairs, and code snippets, generated using previous models like Quen 2.5VL and Quen 2.5 coder. The training pipeline consisted of multiple stages, including initial pre-training on large datasets, reinforcement learning for reasoning, and fine-tuning to blend reasoning with quick responses. This comprehensive process resulted in a highly capable and versatile model.
Furthermore, Quen 3 supports tool calling during chain-of-thought reasoning, enabling it to perform complex tasks like fetching data from GitHub or organizing files on a desktop within a single inference run. The model’s ability to invoke external tools seamlessly enhances its utility for agentic and coding applications. The presenter demonstrates this with live examples, showcasing how Quen 3 can efficiently manage tasks that involve multiple steps and external interactions, making it a powerful tool for developers and AI practitioners.
Finally, the video compares Quen 3 to Llama 4, noting that despite Llama 4’s larger overall size, Quen 3’s optimized active parameters and performance benchmarks give it a competitive edge. Quen 3’s speed is highlighted through a live coding example, where it quickly writes a game of Snake in Python on a high-end Mac. The presenter concludes by emphasizing the open-source availability of Quen 3, encouraging viewers to experiment with it and explore its capabilities further, positioning it as a significant advancement in accessible, high-performance AI models.