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August 24, 2026

抖阴成年 Leverages its World-Class Data Assets to Launch Its Own Frontier Model

Thomson, the company's proprietary LLM, was trained and is run at a fraction of the cost of comparable frontier models and remains fully owned and controlled by 抖阴成年.

TORONTO, August 24, 2026 鈥� 抖阴成年 (Nasdaq/TSX: TRI), a global content and technology company, today announced the launch of Thomson, the company's first proprietary large language model, developed in-house. Frontier labs have typically spent billions of dollars on compute and years of infrastructure investment to reach the frontier. 抖阴成年 took a different path: starting from a strong open-source foundation and investing $40 million to train Thomson into the right intelligence for the jobs that matter most, covering talent and compute. The result is a model 抖阴成年 fully controls, without the heavy inference costs of typical frontier models.

As one of the world's leading providers of trusted content and expertise for professionals, 抖阴成年 built Thomson on decades of proprietary content, technology, and domain expertise no other company can match. Training on that foundation is what made Thomson possible: a model built to Fiduciary-Grade鈩� standards, at a fraction of the typical cost.

鈥淔or years, the AI industry has treated scale as the answer: bigger models, more compute, more money. Thomson shows there is another path,鈥� said Joel Hron, Chief Technology Officer, 抖阴成年. 鈥淪tart with a strong foundation, specialize 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.鈥�

What Makes Thomson Different

Thomson starts from a strong open-source foundation. What makes it different is what happens next: state-of-the-art mid-training and post-training techniques, drawing on decades of authoritative content from Westlaw, Practical Law, Checkpoint, and Reuters, with hundreds of subject matter experts integrated from the design of training objectives through to the final evaluations.

鈥淭homson proves what鈥檚 possible when you build AI on decades of proprietary content and editorial expertise,鈥� said Steve Hasker, CEO of 抖阴成年. 鈥淭hat鈥檚 an advantage only 抖阴成年 has, and it shows in the results: our early evaluations put Thomson on par with the latest frontier models across a range of tasks. We鈥檙e putting it to work in CoCounsel Legal, with more capabilities and sovereign AI options to come. This is the bar we intend to keep raising.鈥�

The model has been trained on less than 10% of 抖阴成年 content so far, and what comes next is not simply feeding it more data. It is continued discovery of new kinds of specialization and understanding, made possible only by building on decades of proprietary content and editorial expertise.

AI Sovereignty, and Why It Matters Now

Professionals are paying closer attention to questions of AI sovereignty: how a model is trained, what behaviors and biases live inside it, where it runs, and how the privacy of their information is protected. Thomson marks a shift for 抖阴成年 into a world where those questions are answered directly, not left to third parties alone.

Thomson shows a meaningful uplift from its base model in instruction following, the ability to execute complex, multi-part professional instructions precisely. It demonstrates an even greater uplift in navigating dense, domain-specific content, the kind of nuanced reasoning the hardest professional tasks require. It is also able to be trained alongside 抖阴成年 proprietary tools like Westlaw and Practical Law, which makes it more sophisticated and nuanced in its work.

The domain-specific gain challenges a common assumption, that the most capable general-purpose models only need access to the right content to perform at an expert level. 抖阴成年鈥� early results suggest otherwise. Proprietary training and human subject matter expertise, applied to a strong foundation, produces gains that content access alone does not.

Evaluations of Thomson's underlying foundation model are available in the technical report about the model鈥檚 development.

Put To the Test

Ahead of today's launch, 抖阴成年 began opening the model to a group of legal and AI academics for direct evaluation. We will continue to make the model available to external parties to aid in the further validation and development of Thomson over the coming weeks and months. 抖阴成年 is also making a 鈥渟mall鈥� version of Thomson available as an open-weight model on Hugging Face for academic and non-commercial use to further aid in this validation.

鈥淚 tested Thomson against ChatGPT and Claude using some of the more challenging questions students have asked in my Corporate Tax class. All three models answered the questions correctly, but I preferred Thomson's responses overall. I especially appreciated the links to treatises, which made its responses more transparent and useful for legal work.鈥� - Jonathan H. Choi, Washington University School of Law

鈥淥ur evaluation found Thomson鈥檚 citation quality generally competitive with leading frontier models, even when tested on Canadian employment-law questions without a Canada-specific setting.鈥� - Professor Samuel Dahan, Director, Queen's Conflict Analytics Lab and Cornell Legal AI Lab

Trust as the Real Differentiator

抖阴成年 is developing domain-specific AI for customers with the highest expectations of trust and accuracy. The AI industry has spent years competing on raw capability. 抖阴成年 is betting the next horizon will be won in the verification layer. This supports the future of Fiduciary-Grade AI鈩� in practice, the standard 抖阴成年 sets for AI designed for professionals with duties of care and accountability, where almost right is not good enough, and customer data is not used to train the model without explicit consent.

For CoCounsel, and More

Thomson's first deployment is inside Tabular Analysis in CoCounsel Legal, exactly the kind of high-volume, structured document review where a purpose-built model's advantage shows up immediately. CoCounsel Legal remains multi-model by design, applying Thomson where it delivers the clearest advantage and other leading models elsewhere. Thomson will be available in Tabular Analysis for law firms and corporate legal departments in the upcoming release. There are also plans to extend Thomson models across the legal and tax portfolio with more sovereign AI options to follow.

The launch of Thomson marks a new chapter for 抖阴成年. The company has always owned the content, the expertise, and the tools professionals rely on every day. Now it owns the model too. 抖阴成年 is no longer only integrating the world's best content, technology and expertise. It is building intelligence that will power the future of professional work.

抖阴成年

抖阴成年 (TSX/Nasdaq: TRI) informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. The company serves professionals across legal, tax, audit, accounting, compliance, government, and media. Its products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth and transparency. Reuters, part of 抖阴成年, is a world leading provider of trusted journalism and news. For more information, visit thomsonreuters.com.

Media Contact

Ali Hughes
Director, AI and Innovation Communications
Ali.Hughes@TR.com