One signal

Sthanika says its Sieve 31B model has taken second place on the Jev Decision Index, an open benchmark for AI models that return decisions rather than conversational answers. The model scored 63.8 overall, including private tests, and 62.5 on the public tests, according to a report by ETV Bharat citing ANI.

That makes this a fresh contender in the growing field of AI decision models, which can return a defined choice and confidence score for software to use. Sthanika says its smaller Sieve models also placed among the leaders in their size classes. The scores are Sthanika’s reported benchmark results, not a guarantee of performance on a developer’s own tasks. Still, the practical appeal is clear: routine choices may not need a general-purpose model to compose an answer first.

Discuss: Would you trust a specialised decision model for routine agent tasks, or keep those choices with a general-purpose LLM?

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