Workflow
“新版DeepSeek-R1”的深度测评
Seek .Seek .(US:SKLTY)2025-05-29 15:25

Summary of Deepseeker R1 Conference Call Company and Industry - The discussion revolves around the performance and updates of the Deepseeker R1 model, a product in the AI and machine learning industry, particularly focusing on its capabilities in data retrieval and code generation. Core Points and Arguments - Performance Improvement: The accuracy of Deepseeker R1 in CLion improved from 4/8 to 6/8 in version 0.528, although it still lags behind Claude 3.7 (7/8) and CosmoFlow with Claude 4 (8/8) [1][3][19]. - Context Length Enhancement: The new version increased the maximum context length to 128K for clients, addressing previous issues where excessive web content retrieval exceeded context limits [5][19]. - Challenges in Data Retrieval: The model faced difficulties using the fetch tool to retrieve China’s GDP data due to low success rates and lack of API support from the World Bank, indicating compatibility issues between MCP tools and large models [6][19]. - Comparison with Other Models: Readcloud 3.7, Readcloud 4, Grok 3, and Gemini 2.5 Pro demonstrated better performance in using MCP tools and parameter settings, successfully completing tasks that Deepseeker R1 struggled with [7][19]. - Code Generation Quality: While the new version shows improvements in reasoning and text generation quality, the code generation aspect still has flaws compared to Claude series models [4][19]. - Error Handling in MCP Tools: The MCP tools often encounter issues when a tool fails, and the selection of alternatives is not always ideal. Readcloud has shown the ability to quickly find substitutes when issues arise [13][14]. Other Important but Possibly Overlooked Content - Task Complexity: The complexity of tasks requiring multiple MCP tools can lead to cascading errors if one tool fails, emphasizing the need for careful planning and tool selection [11][19]. - Improvements in Cloud 4: Cloud 4 outperforms Cloud 3.7 in data scraping and webpage generation, with faster speeds and higher accuracy, showcasing advancements in the technology [10][19]. - Devsec Error Handling: Devsec's error handling is contingent on initial tool selection, suggesting a need for improved recognition and selection of backup options to enhance reliability [15][19]. - Limitations in Code Generation: Despite improvements, the new version's code generation still falls short in quality compared to Claude 3.7 and 4, particularly in achieving expected outcomes in specific projects [17][19]. - Overall Model Comparison: Claude 4 is noted for its superior speed and accuracy, especially in programming tasks, indicating a competitive edge over Deepseeker R1 [18][19].