qwen -max-thinking

Core Insights - The article discusses the Qwen3-Max-Thinking reasoning model, highlighting its significant performance improvements through technological innovations. Group 1: Core Innovations - The model features an adaptive tool invocation capability, allowing it to autonomously judge and utilize built-in functions like search engines and code interpreters, reducing the likelihood of generating false information [1] - Test-Time Scaling is introduced as an efficient reasoning strategy, enabling the model to extract key insights from past reasoning processes, thus enhancing performance without increasing computational costs [1] Group 2: Performance Metrics - Qwen3-Max-Thinking achieved a score of 58.3 in the "Human Last Exam" (HLE) assessment, surpassing competitors like GPT-5.2-Thinking (45.5) and Gemini 3 Pro (45.8), although it still has room for improvement in specific areas [1] Group 3: Application Benefits - The model has over one trillion parameters and has undergone extensive reinforcement learning, establishing a strong foundation for reasoning and knowledge integration capabilities [2] - It possesses robust agent capabilities, enabling it to autonomously complete complex task processes beyond mere text generation [2] Group 4: Pricing Structure - The pricing for input and output tokens varies based on length, with specific rates outlined for different token ranges, indicating a structured cost model for usage [3] - Comparatively, Qwen's pricing for one million tokens is 3.2 yuan for input and 12.8 yuan for output, positioning it within a competitive pricing landscape [5]