Sarvam 105B shows strong, balanced performance across core capabilities including mathematics, coding, knowledge, and instruction following. It achieves 98.6 on Math500, matching the top models in the comparison, and 71.7 on LiveCodeBench v6, outperforming most competitors on real-world coding tasks. On knowledge benchmarks, it scores 90.6 on MMLU and 81.7 on MMLU Pro, remaining competitive with frontier-class systems. With 84.8 on IF Eval, the model demonstrates a well-rounded capability profile across the major workloads expected of modern language models.
В Белом доме ответили на вопрос о снятии санкций с России00:46
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督察组表示，将进一步调查核实有关情况，并按要求做好后续督察工作。。新收录的资料是该领域的重要参考
45. Which AI Roles Offer the Highest Salaries in 2026? - Schiller International University, www.schiller.edu/blog/which-…