The video compares Ling 2.6, a massive one trillion parameter AI model, with the smaller 27 billion parameter Qwen 3.6, finding that despite Ling’s size advantage, Qwen often outperforms it in coding, logic, and math tasks due to better optimization and refinement. While Ling excels in certain complex coding scenarios, Qwen’s overall versatility and accuracy make it the more effective model in most practical applications.
In this video, the host compares two impressive AI language models: Ling 2.6, a one trillion parameter model from Ant Group, and Qwen 3.6, a 27 billion parameter model from Alibaba. Despite the massive difference in size, with Ling having significantly more parameters, the comparison aims to see which model performs better across various tasks including coding, logic puzzles, and mathematics. The host highlights that Ling has more active parameters than Qwen’s dense parameters, suggesting it should theoretically outperform Qwen.
The first set of tests involved coding challenges, such as creating a 3D Flappy Birds game and adding a controllable spaceship. Both models produced runnable versions of the game, with Qwen slightly outperforming Ling in terms of speed and functionality. Qwen’s spaceship implementation was more coherent and interactive, earning it higher marks. Similarly, when tasked with creating an MS Word clone, Qwen generated a more advanced and visually accurate version compared to Ling’s very basic output.
When it came to logical puzzles, both models answered correctly on simpler questions like dividing oranges or pill-taking timing. However, Qwen consistently provided more accurate and practical answers, such as recommending driving to a car wash rather than walking, where Ling gave a less sensible response. In a more complex coding task involving an interactive Earth simulation, Ling surprisingly outperformed Qwen by producing a more detailed and visually appealing environment, despite its slower token generation speed.
Mathematical reasoning tests showed both models arriving at correct answers for certain problems, with Ling sometimes using fewer tokens to reach the solution. However, on a challenging math Olympiad question, Qwen provided the textbook correct answer while Ling failed to do so, matching the incorrect response from other models like Open Router. The host also noted that other AI systems like Claude struggled with these questions, often running out of tokens or providing less coherent answers.
Overall, the video reveals that despite Ling’s massive size, Qwen 3.6, the much smaller 27 billion parameter model, frequently outperforms it in practical tasks, likely due to better refinement, more user data, and optimization. Ling’s strengths lie in specific complex coding tasks where it can generate impressive outputs, but Qwen’s versatility and accuracy make it the more effective model in most scenarios. The host encourages viewers to support these open-source models and notes the exciting potential for future improvements in both.