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Sarvam 30B supports native tool calling and performs consistently on benchmarks designed to evaluate agentic workflows involving planning, retrieval, and multi-step task execution. On BrowseComp, it achieves 35.5, outperforming several comparable models on web-search-driven tasks. On Tau2 (avg.), it achieves 45.7, indicating reliable performance across extended interactions. SWE-Bench Verified remains challenging across models; Sarvam 30B shows competitive performance within its class. Taken together, these results indicate that the model is well suited for real-world agentic deployments requiring efficient tool use and structured task execution, particularly in production environments where inference efficiency is critical.,详情可参考新收录的资料

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魔法原子回应创始人离职:确有此事,目前技术团队稳定。新收录的资料对此有专业解读

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朱文,独立研究员,专注于数据分析与市场趋势研究,多篇文章获得业内好评。