Sarvam AI open-sources Sarvam Prism for 44 ms single-pass enterprise decisions

Sarvam AI released Sarvam Prism on 3 November 2026 under the Apache 2.0 licence, introducing an open-weight System One decision model fine-tuned with LoRA on its Sarvam 105B-10B mixture-of-experts base. Rather than generating tokens autoregressively, Prism reads a state, a question and candidate options within a 131,072-token window in one forward pass and outputs calibrated probabilities through a lightweight scoring head.

A split attention mask reads the state and question bidirectionally while isolating each option until the final answer token, adding roughly 3 points on the Jev Decision Index and running attention 2.6 times faster above 2,048 tokens. Label-smoothed cross-entropy and Brier losses keep actual mistakes below predicted uncertainty at 90% confidence (2.2% actual versus 3.3% expected in English), while canonical option sorting eliminates answer flips across 2,000 shuffled prompts (0.0%, versus 10.3% for Perplexity Decider v1.2). Converting weights to FP8 (106 GB on disk, 120 GB VRAM), grouping 128 experts into one kernel and capturing a CUDA Graph cut median latency from 664 ms on two 16-bit B200 GPUs to 44 ms on one B200, 15.1 times faster.

Sarvam renders its benchmark charts interactively in HTML rather than static images, ranking Prism first under 100 ms across 25 tested decision models. Hosted API access is live on Sarvam Indus; per-request API pricing was not printed.

Key numbers

• 71.8 average score at 44 ms (67.0 Jev, 65.9 Artificial Analysis recreation, 82.5 Indian languages), ahead of Perplexity Decider v1.2 (68.0 at 117 ms) and Jev 1.14 (64.5 at 196 ms).
• 78.5% accuracy judging 700 agent traces in roughly 0.5 s per run, versus 70.7% in 2.5 s for Claude Haiku 5.5.
• 89.0% accuracy on a 5,517-question cybersecurity guardrail benchmark, ahead of Clef (84.5%) and Jev 1.14 (78.6%).