Thomson Reuters has launched Thomson, its first proprietary large language model, to give customers more control, specialisation, and efficiency in their AI use.
“For years, the AI industry has treated scale as the answer: bigger models, more compute, more money. Thomson shows there is another path,” said Joel Hron, chief technology officer, Thomson Reuters. “Start with a strong foundation, specialise it deeply for the work that matters, and you can build intelligence that is highly capable, far more efficient, and entirely under your control. We think that changes the economics of professional AI.”
Thomson
Thomson Reuters has invested $40 million to train Thomson from a strong open-source foundation to deliver frontier-level capabilities at a fraction of the cost of developing and operating comparable models.
Developed using Thomson Reuters’ proprietary content, technology, and domain expertise, including material from Westlaw, Practical Law, Checkpoint, and Reuters, training Thomson involves hundreds of subject-matter experts who define training objectives and evaluations.
According to the company, less than 10% of its content has been used to train the model, with future development focusing on specialisation rather than increasing training data volume.
Thomson Reuters initially deployed Thomson in Tabular Analysis within CoCounsel Legalto support high-volume, structured document review. The company said it will release a smaller version of Thomson as an open-weight model on Hugging Face for academic and non-commercial evaluation.
“Thomson proves what’s possible when you build AI on decades of proprietary content and editorial expertise,” said Steve Hasker, CEO of Thomson Reuters. “That’s an advantage only Thomson Reuters has, and it shows in the results: our early evaluations put Thomson on par with the latest frontier models across a range of tasks.”
