Coinbase says a specially trained smaller language model outperformed a larger frontier model in the exchange’s internal fraud-detection benchmark, illustrating how task-specific evaluation can matter more than headline general AI rankings for sensitive financial applications. In an October 8 engineering report, Coinbase described post-training Qwen3.5-9B on proprietary Onramp fraud data and serving it using Ray Serve and vLLM. The company reported a 9.6% higher F1 score than its Opus 4.5 comparison on its benchmark and a 55% reduction in end-to-end latency in online inference.