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.
Владимир Зеленский. Фото: Anatolii Stepanov / Reuters
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正是这种“既想发力又偏保守”的纠结心态，造就了美团AI如今拧巴的状态：2B端的AI布局相对完善，大多用来加固履约效率的护城河；但AI2C的进展十分缓慢，核心的AI决策入口始终处于缺失状态。