Thomson Reuters Launches Thomson, Its Own Proprietary LLM Trained on Westlaw and Practical Law Content

Thomson Reuters has made its proprietary large language model (LLM) official with the launch of Thomson. The company emphasizes that this achievement was accomplished at a fraction of the cost of other frontier models like ChatGPT and Claude, which it fully owns and controls.

The development of Thomson is significant not only because of its impressive performance but also due to its unique training data set. Unlike other LLMs, Thomson was trained on proprietary content built up over decades by Thomson Reuters, including Westlaw and Practical Law. This domain expertise no other company can match has been a crucial factor in the model’s success.

Joel Hron, chief technology officer at Thomson Reuters, believes that this achievement could change the economics of professional AI. He notes that for years, the industry has focused on scale as the answer: bigger models, more compute, and more money. However, with Thomson, there is another path – starting with a strong foundation, specializing it deeply for the work that matters, and building intelligence that is highly capable, far more efficient, and entirely under your control.

Thomson Reuters has invested around $40 million in developing this model over the past two years, covering both talent and compute. The final training run for the version launching today cost just $450,000. This number indicates what Thomson Reuters was able to achieve with its data and content on top of world-class open-source models.

The first deployment of Thomson will be in an upcoming release of CoCounsel Legal, driving its Tabular Analysis functionality. Thomson will be the default model for this feature, although administrators can switch it to another model through CoCounsel Legal’s admin settings. The company emphasizes that CoCounsel will remain multi-model, tapping into whichever LLM is best suited for the task at hand.

Over time, Hron expects Thomson to take a bigger and bigger share of the tokens and work done by CoCounsel. Thomson Reuters plans eventually to extend its models across its portfolio of legal and tax products. The company believes that Thomson does not need to keep pace with the frontier of general intelligence across all dimensions but should instead set the frontier for legal.

Beyond its own products, Thomson Reuters has begun early conversations with large law firms and corporations about direct access to the model. This includes firms interested in fine-tuning it with their own data as they look to bring greater sovereignty to their knowledge and expertise. The company is open to working with these organizations and licensing Thomson directly.

The development of Thomson involved starting with an existing open-source model, training it to become a legal specialist, rather than building from scratch. Although the models used changed over the course of the project, the most recent was Qwen 3.5. Central to this process were subject-matter experts – the armies of lawyers and tax professionals employed by Thomson Reuters.

Andrew Bean, who heads TR’s evaluations team, said hundreds of experts helped decide what the model should be trained to do in the first place, created many thousands of examples of high-quality answers, and then judged the model’s outputs in blind, head-to-head comparisons against frontier models. None of this training data comes from customers; instead, Thomson Reuters replicates relevant tasks with its own experts.

Thomson Reuters has engaged several third-party security firms to validate the hosting, serving, and security controls around the model. Because open-source models keep improving, the company built a process that is repeatable – swapping out starting points close to half a dozen times over the life of the project as better models emerged.

The result is a model that gained deep capability in legal and tax work without losing general abilities such as writing, math, and reasoning over long documents. This achievement marks a significant milestone for Thomson Reuters, which now owns not only its content but also the intelligence powering it.

Thomson Reuters’ internal testing of Thomson has shown strong results on various benchmarks. One evaluation described during the briefing was an in-house benchmark called Deep Research, built around legal research queries drafted by experts based on their work in practice. For each query, they supplied criteria for a good answer to contain.

The answers were scored on two dimensions: completeness and factuality – whether the citations provided actually supported the claims being made. Thomson Reuters compared its model against GPT 5.4 and Claude Sonnet 5 using this benchmark, scoring each twice – once with access only to web content and once with access to TR’s proprietary content.

While Thomson performed respectably in the web-only test, it did much better when trained on TR’s own data, outscoring both of the other LLMs. This demonstrates that there is a significant uplift from training on one’s own tools – a key advantage of having an in-house model like Thomson Reuters’ proprietary LLM.

Thomson Reuters will publish a technical report this week with comprehensive evaluations, including results on standard public AI benchmarks and human testing. The company has also begun making the model available to legal and AI academics for direct evaluation and is releasing a ‘small’ version as an open-weight model on Hugging Face under a non-commercial academic license.

One of those academics, Jonathan H. Choi at Washington University School of Law, tested Thomson against ChatGPT and Claude using challenging questions from his Corporate Tax class. He preferred Thomson’s responses overall, especially appreciating the links to treatises that made them more transparent and useful for legal work.

Thomson Reuters is building a developer portal that will eventually allow outside parties to access the model directly with API keys, configurable parameters, and sample documentation. The company emphasizes that this marks a new chapter – Thomson Reuters no longer only integrates content but also builds intelligence powering professional work.