Thomson-1.0-Small is a 35.1 billion parameter language model developed by Thomson Reuters with partners, specialized for legal, tax, and journalism applications. Built on Qwen architecture with a 262K token context window, it achieves 74.6% average benchmark performance through continual learning and proprietary data curation, designed for high-stakes professional work requiring factual accuracy.