Samaya AI Releases Sterling: A 3B-Active Finance Research Agent That Outperforms Larger Models at 11x Lower Cost

Samaya AI has released Sterling, a 3-billion-active-parameter model post-trained specifically for finance research tasks, which achieves better scores than Claude Fable 5.1 on the FrontierFinance benchmark while costing 11 times less to run. Sterling is designed as a finance research agent capable of processing earnings reports, market data, and regulatory filings with domain-specific accuracy that general-purpose models struggle to match. This release is a notable example of efficient post-training yielding specialized models that punch well above their parameter weight, a trend with significant implications for enterprise AI deployment in cost-sensitive verticals. For developers building fintech or financial data applications, Sterling offers a viable path to high-accuracy AI at dramatically lower inference costs. The model's benchmark results also underscore the value of domain-specific fine-tuning over scaling general-purpose models for specialized tasks.