The video reviews the Quen 3 235B A22B model, highlighting its strong capabilities in complex coding tasks and creative applications, though it occasionally struggles with troubleshooting and producing polished outputs compared to top-tier models. Overall, Quen 3 is seen as a solid, open-source contender that excels in certain areas but still lags behind proprietary models like Claude 3.7 and Gemini 2.5 Pro in reliability and completeness.
The video features an in-depth testing and review of the newly released Quen 3 235B A22B model, focusing on its capabilities as a coding and AI assistant. The host begins by exploring its performance on various coding benchmarks, noting that it took a notably long time to generate complex outputs, such as a self-contained HTML file for a solar system simulation. Despite the lengthy processing, the output was impressive, with smooth functionality and detailed interactions, suggesting that Quen 3 is quite capable in handling intricate coding tasks.
Throughout the testing, the host compares Quen 3’s performance to other models like Gemini 2.5 Pro, OpenAI’s GPT-3.5, Anthropic’s Claude 3.7, and Google’s Gemini. While Quen 3 demonstrates strong abilities, especially in generating complex simulations and handling multi-step prompts, it occasionally struggles with troubleshooting and fixing errors autonomously. The host observes that models like Gemini 2.5 Pro tend to produce more complete code and better troubleshoot issues without requiring as much manual intervention, highlighting some limitations in Quen 3’s self-correcting capabilities.
The review also covers creative and interactive applications, such as generating a 3D galaxy simulation, a Python-based soccer game with reinforcement learning, and an interactive story using API integrations. Quen 3 performs well in some areas, like creating text-based versions of games and stories, but falls short in rendering more complex visual or audio components, such as 3D graphics or voice narration with API keys. Notably, it successfully produces a text-based reinforcement learning pipeline for a snake game, which the host finds clever, though it doesn’t fully meet all visual expectations.
Further testing involves prompts for multimedia projects, including a webcam-controlled music player and an interactive audiobook with voice narration. Quen 3 handles these tasks reasonably well, generating functional code that incorporates API keys and user interaction. However, it sometimes produces incomplete or less polished outputs compared to models like Gemini 2.5 Pro, which tend to deliver more comprehensive and ready-to-use code. The host emphasizes the importance of how models handle sensitive information like API keys, appreciating Gemini’s cautious approach to security warnings.
In conclusion, the host finds Quen 3 to be a solid and impressive model, especially considering its open-source nature. While it excels in certain coding and creative tasks, it still lags behind top-tier proprietary models like Claude 3.7 and Gemini 2.5 Pro in terms of reliability, troubleshooting, and completeness. The overall assessment suggests that Quen 3 is likely to be a strong contender among open-source models and may outperform some competitors in specific areas, but it probably won’t surpass the leading proprietary models in overall performance. The host invites viewers to share their own experiences and thoughts on the model’s capabilities.