Sarvam has released Sarvam Prism, a System One decision model built on Sarvam 105B. The company says Sarvam Prism establishes a new point on the Pareto frontier of accuracy and speed among the 25 decision models it tested, with an average score of 71.8 while responding in 44 milliseconds on an NVIDIA B200 GPU. Sarvam Prism is designed for enterprise decision-making workflows, where a large part of the work involves a sequence of fast and intuitive decisions, and is intended to provide calibrated outputs that enterprises can use when automating workflows and making decisions based on confidence thresholds. Sarvam Prism was tested against 24 other decision models across three indices, with response times measured on the same B200 hardware using the bundled inference code wherever the models could be run. The model recorded an average score of 71.8 across the three indices, with a median response time of 44 milliseconds per request, which the company said makes it the highest-scoring model among the 25 models tested that responds in under 100 milliseconds. Several models recorded higher overall scores, but Sarvam said they took between two and six times as long to respond. Among the tested models, mammoth-1 recorded the highest average score at 75.2, followed by GLiDE 1.2 at 73.2; Sarvam Prism recorded 71.8, followed by elephant-1 at 71.3.