With "Thomson," the professional information company is launching its first in-house language model, built on Alibaba's Qwen. The model hits top marks when it can tap into the company's own content and tools.
Thomson Reuters spent about $40 million on staff and computing power over more than two years, according to the company. The more widely touted figure of $450,000 covers only the final training run of the current version. Even the full sum leaves out the real capital: That's decades of content from Westlaw, Practical Law, Checkpoint, and Reuters, plus the working hours of hundreds of domain experts.
The foundation is Alibaba's open Qwen, most recently Qwen3.5-397B, the company says. Working with Imperial College, Thomson Reuters first retrained the Chinese model for safety, ethics, and political neutrality. This intermediate version is called "Snowdon," named after the mountain in Wales.
Then came pre-training on the company's own content, post-training with domain experts, and agentic reinforcement learning inside the company's own tool environments. So far, less than 10 percent of the available content has gone into training.
CTO Joel Hron says the company has "changed the open source starting point like probably close to a half dozen times already." The bigger finding is "less the individual model and more the model factory we built," adds research chief Jonathan Schwartz.
