刚刚，豆包编程模型来了，我们用四个关卡考了考它！

Core Insights - The article discusses the evolution of AI programming assistants, highlighting the shift from simple code completion tools to more advanced models capable of understanding complex tasks and contexts. This evolution is represented by two main routes: IDE enhancement and Agentic coding [1][2]. Group 1: AI Programming Assistant Evolution - AI programming assistants have significantly changed development workflows, with even skeptics like Linus Torvalds acknowledging their utility [1]. - The article identifies two main routes for AI programming assistants by 2025: IDE enhancement (e.g., GitHub Copilot) and Agentic coding (e.g., Claude Code) [2]. Group 2: Doubao-Seed-Code Introduction - Doubao-Seed-Code, developed by Volcano Engine, aims to address the limitations of existing models by providing a robust programming model designed for complex tasks [2][4]. - The model has shown exceptional performance in various authoritative benchmarks, even surpassing Claude 4.5 Sonnet in some evaluations [6][8]. Group 3: Key Features of Doubao-Seed-Code - Doubao-Seed-Code boasts a native 256K long context capability, allowing it to handle complex projects that span multiple files and dependencies [10][11]. - The model is the first in China to support visual understanding, enabling it to generate code based on UI designs and perform visual comparisons for style and bug fixes [11]. Group 4: Performance Evaluation - The article outlines a series of practical tests to evaluate Doubao-Seed-Code's capabilities, including task planning, long context handling, and debugging abilities [18][22]. - In a test involving the refactoring of a poorly structured Python script, Doubao-Seed-Code completed the task in under three minutes, demonstrating its debugging capabilities [23][24]. Group 5: Advanced Task Execution - Doubao-Seed-Code successfully executed a complex task of converting a C++ game to Python, showcasing its long context and task planning abilities. The entire process took approximately 40 minutes [26][30]. - The model autonomously planned and executed the project, demonstrating its capability to handle significant programming challenges [31]. Group 6: Cost and Accessibility - Doubao-Seed-Code aims to address pricing and usage limitations faced by developers, offering a subscription service with competitive pricing [48][50]. - The "Coding Plan" subscription service provides significant discounts and aims to lower costs by 62.7%, making it accessible to a broader range of developers [49][50]. Group 7: Conclusion - Doubao-Seed-Code is positioned as a powerful alternative in the Agentic coding space, capable of handling complex tasks autonomously and efficiently [52][53]. - The model not only addresses performance issues but also offers a cost-effective solution for developers, paving the way for widespread adoption of Agentic coding [53][54].