Samaya Sterling outperforms Claude Fable 5.1 on financial benchmarks at one-eleventh the cost, proving that co-training small models with their harnesses can dominate specific domains.
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Samaya AI has released Samaya Sterling, a post-trained model for agentic financial research. Sterling is built on NVIDIA’s Nemotron 3.5 Lightning, a 30B mixture-of-experts model with 3B active parameters. On Samaya’s FrontierFinance benchmark, it scores 58.9%. Claude Fable 5.1 scores 55.9%. Each Sterling question costs $0.61, against $6.89 for Fable 5.1. For developers, the takeaway is a concrete recipe: co-train a small model with its harness, and it can win in a narrow domain. TL;DR Size: 30B total, 3B active (MoE). Base model context: up to 1M tokens. Sterling’s serving context: not disclosed. Runs on: the base model fits a single H100 80GB in BF16 (256K context). Sterling’s serving hardware: not disclosed. Performance: beats every high-quality system on the FrontierFinance leaderboard on quality per dollar (self-reported). Best: 58.9% on FrontierFinance (60.6% in high-effort mode), vs 55.9% for Claude Fable 5.1. Worst: 45.2% on the hardest third of FrontierFinance questions. What is Samaya Sterling? Samaya Sterling is a LoRA post-trained version of Nemotron 3.5 Lightning, tuned to work as a financial research agent. It plans research, calls finance tools, gathers evidence and hands findings to a separate report writer. According to Maithra Raghu’s X announcement, it started as a 2-person effort on agentic orchestration efficiency. How does the harness support a 3B-active model? Samaya team adapted its production finance harness for a small model. Detailed guidance was stripped from the system prompt and tool descriptions. Tools were merged to shorten the prompt. Two helper models manage context. A condenser shortens long tool results. A compactor summarizes the trace as it grows. A research budget caps how long the agent works. After that, the report writer answers from what was found. How was Sterling trained? Training used LoRA on Tinker, Thinking Machines Lab’s training API. It ran in 4 stages: SFT on synthetic traces: Samaya generated 968 synthe
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