Thomson
Thomson Reuters' first in-house proprietary large language model, trained on its Westlaw and Practical Law content for professional-grade accuracy.
Overview
Thomson Reuters announced Thomson, its first proprietary large language model built in-house, on August 24, 2026. The company invested $40 million training it on an open-source foundation combined with decades of proprietary content from Westlaw and Practical Law, targeting what it calls "Fiduciary-Grade" accuracy for professional and legal work at lower inference cost than typical frontier models. Thomson Reuters says the model was trained on less than 10% of its content library so far and is deployed within CoCounsel Legal as part of a multi-model approach, with an open-weight release on Hugging Face for academic, non-commercial use.
This is a sourced directory entry. Features and capabilities are reported in the linked sources; this entry does not document independent Aymorphic testing.
Key features
- Trained on proprietary Westlaw and Practical Law content plus an open-source base
- Built to Thomson Reuters' "Fiduciary-Grade" accuracy standard for professional work
- Deployed inside CoCounsel Legal alongside other models in a multi-model strategy
- Open-weight release on Hugging Face for academic, non-commercial use
- Designed for sovereign AI: full control over training data, behavior, and privacy
Use cases
- Legal research and drafting inside CoCounsel Legal
- Domain-specific professional reasoning tasks requiring high factual accuracy
- Academic research use via the open-weight release
Pricing
Not publicly disclosed for the commercial/CoCounsel deployment; open-weight version available free on Hugging Face for academic, non-commercial use.
Pricing reflects the source record and may change. Confirm current terms with the provider.
Dates and sources
- Released
- 24 Aug 2026
- Record updated
- 23 Sept 2026
- Record last checked
- 23 Sept 2026