Thomson Reuters Leverages its World-Class Data Assets to Launch Its Own Frontier Model

TORONTO, August 24, 2026 – Thomson Reuters (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. Thomson Reuters 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 Thomson Reuters fully controls, without the heavy inference costs of typical frontier models.多伦多，2026年8月24日——全球内容与技术公司汤森路透（Thomson Reuters，纳斯达克/多伦多证券交易所代码：TRI）今日宣布推出“Thomson”，这是该公司自主研发的首个专有大语言模型。前沿实验室通常需要投入数十亿美元的计算资源和数年的基础设施建设才能达到行业领先水平。汤森路透则选择了不同的路径：以强大的开源基础为起点，投入4000万美元用于人才和算力，将Thomson训练成最符合核心业务需求的智能模型。最终成果是一个由汤森路透完全掌控的模型，且无需承担典型前沿模型高昂的推理成本。

As one of the world's leading providers of trusted content and expertise for professionals, Thomson Reuters 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™ standards, at a fraction of the typical cost.作为全球领先的专业内容与专业知识提供商之一，汤森路透基于数十年来积累的、其他公司无法比拟的专有内容、技术和领域专长构建了Thomson。正是基于这一基础的训练，才使得Thomson成为可能：一个以Fiduciary-Grade™（受托人级）标准构建，且成本仅为同类模型一小部分的高效模型。

“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, 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.”“多年来，人工智能行业一直将规模视为答案：更大的模型、更多的算力、更多的资金。Thomson证明了还有另一条路径，”汤森路透首席技术官Joel Hron表示，“从强大的基础出发，针对核心工作进行深度专业化训练，你就能构建出能力卓越、效率更高且完全自主可控的智能。我们认为这改变了专业人工智能的经济模式。”

What Makes Thomson DifferentThomson的与众不同之处

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.Thomson始于强大的开源基础。其独特之处在于后续的训练过程：利用Westlaw、Practical Law、Checkpoint和路透社数十年来权威内容，采用最先进的中期和后期训练技术，并整合了数百名主题专家，从训练目标的设定到最终评估全程参与。

“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. We’re 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.”“Thomson证明了当你基于数十年的专有内容和编辑专长来构建人工智能时，会产生什么样的成果，”汤森路透首席执行官Steve Hasker表示，“这是汤森路透独有的优势，这一点在结果中得到了体现：我们早期的评估显示，Thomson在多项任务中已与最新的前沿模型不相上下。我们正将其应用于CoCounsel Legal，未来还将提供更多功能和主权人工智能选项。我们将不断提高这一标准。”

The model has been trained on less than 10% of Thomson Reuters 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.该模型目前的训练数据量不到汤森路透总内容量的10%，接下来的工作不仅仅是填喂更多数据，而是继续探索新的专业化和理解深度，这只有建立在数十年的专有内容和编辑专长之上才能实现。

AI Sovereignty, and Why It Matters Now人工智能主权及其重要性

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 Thomson Reuters into a world where those questions are answered directly, not left to third parties alone.专业人士正日益关注人工智能主权问题：模型如何训练、内部存在哪些行为和偏见、运行在何处，以及如何保护信息隐私。Thomson标志着汤森路透进入了一个新阶段，即直接解答这些问题，而不是将其留给第三方。

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 Thomson Reuters proprietary tools like Westlaw and Practical Law, which makes it more sophisticated and nuanced in its work.与基础模型相比，Thomson在指令遵循方面有了显著提升，能够精准执行复杂的多部分专业指令。在处理密集、特定领域的内容时，它的提升更为明显，展现了高难度专业任务所需的细致推理能力。它还能与Westlaw和Practical Law等汤森路透专有工具协同训练，使其在工作中表现得更加成熟和细腻。

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. Thomson Reuters’ early results suggest otherwise. Proprietary training and human subject matter expertise, applied to a strong foundation, produces gains that content access alone does not.这种领域特定的增益挑战了一个普遍假设，即最强大的通用模型只需访问正确的内容即可达到专家水平。汤森路透的早期结果表明事实并非如此。将专有训练和人类主题专长应用于强大的基础，所产生的增益是单纯的内容访问无法比拟的。

Evaluations of Thomson's underlying foundation model are available in the technical report about the model’s development.有关Thomson底层基础模型的评估结果，可参阅关于该模型开发的技术报告。

Put To the Test接受测试

Ahead of today's launch, Thomson Reuters 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. Thomson Reuters is also making a “small” version of Thomson available as an open-weight model on Hugging Face for academic and non-commercial use to further aid in this validation.在今日发布之前，汤森路透已开始向法律和人工智能学术界的部分人士开放该模型进行直接评估。在接下来的几周和几个月里，我们将继续向外部各方提供该模型，以协助Thomson的进一步验证和开发。汤森路透还将在Hugging Face上提供Thomson的“小型”版本作为开放权重模型，供学术和非商业用途使用，以进一步辅助验证。

“I 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.” - Jonathan H. Choi, Washington University School of Law“我用学生在公司税课程中提出的一些更具挑战性的问题对Thomson、ChatGPT和Claude进行了测试。三个模型都正确回答了问题，但我总体上更喜欢Thomson的回答。我特别欣赏它对论文的引用链接，这使得其回答对于法律工作而言更加透明且实用。”——Jonathan H. Choi，华盛顿大学法学院

“Our evaluation found Thomson’s citation quality generally competitive with leading frontier models, even when tested on Canadian employment-law questions without a Canada-specific setting.” - Professor Samuel Dahan, Director, Queen's Conflict Analytics Lab and Cornell Legal AI Lab“我们的评估发现，即使在没有加拿大特定设置的情况下测试加拿大就业法问题，Thomson的引用质量也普遍与领先的前沿模型具有竞争力。”——Samuel Dahan教授，女王大学冲突分析实验室及康奈尔法律人工智能实验室主任

Trust as the Real Differentiator信任是真正的差异化因素

Thomson Reuters 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. Thomson Reuters is betting the next horizon will be won in the verification layer. This supports the future of Fiduciary-Grade AI™ in practice, the standard Thomson Reuters 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.汤森路透正在为对信任和准确性有最高期望的客户开发领域特定人工智能。人工智能行业多年来一直在原始能力上竞争。汤森路透坚信，下一个制高点将在验证层胜出。这支持了Fiduciary-Grade AI™（受托人级人工智能）的未来实践，这是汤森路透为那些承担注意义务和问责制的专业人士所设计的人工智能标准——在这些领域，“差不多”是远远不够的，且未经明确同意，客户数据绝不会被用于训练模型。

For CoCounsel, and More应用于CoCounsel及更多领域

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.Thomson的首次部署是在CoCounsel Legal的表格分析（Tabular Analysis）功能中，这正是那种需要专用模型优势的、大批量结构化文档审查场景。CoCounsel Legal在设计上保持多模型架构，在Thomson最具优势的地方应用Thomson，在其他领域则使用其他领先模型。Thomson将在即将发布的版本中提供给律师事务所和企业法务部门的表格分析功能使用。汤森路透还计划将Thomson模型扩展到整个法律和税务组合中，并提供更多主权人工智能选项。

The launch of Thomson marks a new chapter for Thomson Reuters. The company has always owned the content, the expertise, and the tools professionals rely on every day. Now it owns the model too. Thomson Reuters 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.Thomson的发布标志着汤森路透开启了新的篇章。该公司一直拥有专业人士每天依赖的内容、专长和工具。现在，它也拥有了自己的模型。汤森路透不再仅仅是整合全球最优质的内容、技术和专长，它正在构建将推动未来专业工作发展的智能。

Thomson Reuters汤森路透

Thomson Reuters (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 Thomson Reuters, is a world leading provider of trusted journalism and news. For more information, visit thomsonreuters.com.汤森路透（多伦多证券交易所/纳斯达克代码：TRI）通过整合人们和组织做出正确决策所需的受托内容和技术，指引前行之路。公司为法律、税务、审计、会计、合规、政府和媒体领域的专业人士提供服务。其产品结合了高度专业化的软件和洞察力，为专业人士提供做出明智决策所需的数据、情报和解决方案，并帮助机构追求正义、真理和透明度。路透社是汤森路透旗下全球领先的受托新闻与资讯提供商。欲了解更多信息，请访问 thomsonreuters.com。

Media Contact媒体联系人

Ali Hughes
Director, AI and Innovation Communications
Ali.Hughes@TR.comAli Hughes 人工智能与创新传播总监 Ali.Hughes@TR.com