Thomson Reuters Launches $40M Proprietary AI Model Built on Alibaba's Open-Source Foundation

Thomson Reuters built its own AI model on Alibaba's Qwen

Thomson Reuters says it spent $40mn training its first in-house model, and that it started from an open-source foundation. Its announcement never names that foundation. Its chief technology officer told Business Insider it is Alibaba's Qwen. Thomson Reuters launched its first proprietary large language model on Monday. The company calls it Thomson. It says it spent $40mn on talent and compute to train the model, and that it began from what its announcement describes as "a strong open-source foundation". The announcement does not say which foundation. Its chief technology officer named it in an interview. What the company says it spent Thomson Reuters trades in Toronto and on the Nasdaq as TRI. It sells legal, tax, accounting and compliance products, and it owns Reuters. The announcement puts the training bill at $40mn, covering talent and compute. It sets that against the frontier labs. Those labs have "typically spent billions of dollars on compute and years of infrastructure investment to reach the frontier", the company says. It describes the result as a model it controls outright, "without the heavy inference costs of typical frontier models". The company has trained Thomson on less than 10% of its own content so far, according to the release. It names Westlaw, Practical Law, Checkpoint and Reuters as the sources. What the base model is Joel Hron, the chief technology officer, told Business Insider that Thomson starts from a model called Snowdon. Snowdon came out of "realigning" an open-source Qwen model from Alibaba. Business Insider identified the base as Qwen3.5, and reported the new model under the name Thomson-1. The company's own press release describes the starting point only as "a strong open-source foundation". It does not name it. Who built Snowdon Hron said a joint team from Thomson Reuters and Imperial College in the UK adapted Qwen over several months. That work ensured the result was "ethically and politically de-biased and safe to use", he said. "There's nothing that necessarily ties us to Qwen," Hron told Business Insider. Alibaba said last month that it wants to charge its biggest users. Where the model runs first Thomson's first deployment sits inside Tabular Analysis in CoCounsel Legal, the company's AI assistant for lawyers. The release calls that "high-volume, structured document review". It reaches law firms and corporate legal departments in the next release. Thomson Reuters says CoCounsel Legal "remains multi-model by design". It plans to extend Thomson across its legal and tax portfolio. The iManage partnership four days earlier Thomson Reuters and the document-management company iManage announced an expanded partnership on 20 August. It pushes CoCounsel Legal deeper into the iManage platform, alongside HighQ, Noetica and Legal Tracker. The two companies also said they would add support for Model Context Protocol, so that approved Thomson Reuters tools can reason over iManage content while keeping access controls, ethical walls and privilege boundaries in place. Rawia Ashraf, co-head of CoCounsel Legal, said legal work "lives in too many places". What Claude still does Hron said CoCounsel still relies mostly on Claude. Thomson Reuters expanded its partnership with Anthropic in May, for that same product. "Our main objective is to make Thomson the model that powers more and more of CoCounsel's capabilities over time," Hron told Business Insider. He said the new model does not replace the company's work with Anthropic and other labs. How Hron described the decision Hron cited cost as one of the main reasons. Owning a model lets the company build on its own intellectual property, he said, rather than paying outside AI companies for theirs. He compared it to renting against buying a house. "Renting a house, you still have a roof over your head, and somebody's taking care of it, and it's great," he said. "But you're not building any equity that compounds into something valuable for you long term." River AI raised $1.1bn this month to let companies train and keep their own models. What Anthropic has said about Alibaba Anthropic has accused Chinese labs of illicitly distilling the outputs of its models to train their own. In June it named Alibaba, in what it called the largest distillation campaign yet run against Claude. It has called for the US to impose restrictions. Senator Tom Cotton has raised concerns about US companies using Chinese open-source models, among them Airbnb and Cursor. He cited potential security threats such as backdoors. Anthropic and Alibaba did not respond to Business Insider's requests for comment. The two academics quoted in the release Thomson Reuters said it opened the model to legal and AI academics before launch. Its announcement quotes two of them. Jonathan H. Choi of Washington University School of Law tested Thomson against ChatGPT and Claude, using questions from his Corporate Tax class. "All three models answered the questions correctly, but I preferred Thomson's responses overall," he said, citing the links to treatises. Professor Samuel Dahan directs the Queen's Conflict Analytics Lab and the Cornell Legal AI Lab. His evaluation found Thomson's "citation quality generally competitive with leading frontier models", including on Canadian employment-law questions. The open-weight release Thomson Reuters is publishing a "small" version of Thomson on Hugging Face as an open-weight model, for academic and non-commercial use. It says a technical report covering the foundation model's development is available. The same move at Harvey, a day earlier Harvey launched Tenet on Sunday, its first proprietary model for legal work. Harvey post-trained it on Kimi K3, the open-weight model from the Chinese lab Moonshot AI. The desk covered Harvey's in-house model that day. Thomson Reuters cut engineering roles in July and said it was hiring AI-native staff instead. The desk covered those engineering layoffs at the time. What the release claims about capability Steve Hasker, the chief executive, said "our early evaluations put Thomson on par with the latest frontier models across a range of tasks". That evaluation is the company's own, and the release publishes no figures alongside it. The release says Thomson shows "a meaningful uplift" over its base model in instruction following, and a greater uplift in navigating dense, domain-specific content. It says evaluations of the underlying foundation model appear in a technical report on the model's development. What Thomson Reuters promises customers The release sets out a standard the company calls Fiduciary-Grade AI. It defines that as AI built for professionals with duties of care and accountability, "where almost right is not good enough". The company says it does not use customer data to train the model without explicit consent. Hundreds of subject matter experts worked on Thomson, according to the release, from the design of training objectives through to the final evaluations. Thomson Reuters says more "sovereign AI options" will follow.

Thomson Reuters launches proprietary AI model for legal work

Thomson Reuters launches proprietary AI model for legal work Global content powerhouse Thomson Reuters Corp. today launched Thomson, its first proprietary large language model, combining the company's trove of legal knowledge with LLMs from outside providers to provide legal advice. The company said Thomson will first be deployed in Tabular Analysis, a high-volume document review capability in its CoCounsel Legal AI assistant. CoCounsel will remain a multimodel product, using Thomson for work where the company's domain-specific model has an advantage and third-party frontier models for other tasks. Thomson will be the default model for Tabular Analysis, although administrators will be able to select other models. Thomson Reuters spent about $40 million over two years on people and computing for the project, but said economies reduced the cost of the final training run to about $450,000. Instead of building a foundation model from scratch, the company began with an open-weight model and added its proprietary content, training methods and professional expertise. It said the approach reduced both training and inference costs compared with general-purpose frontier models. The company isn't seeking to compete with the largest AI labs across every field, said Joel Hron, global head of artificial intelligence and TR Labs at Thomson Reuters. "Thomson needs to set the frontier of intelligence for legal," he said. "That's a different job than what I think a lot of the frontier labs are doing." Professional oversight The training process included realigning the base model with Thomson Reuters' values, pretraining on the company's content, targeted post-training guided by professionals and reinforcement learning that taught the model to work with company tools such as Westlaw and Practical Law. The firm's flagship Westlaw platform encompasses over 40,000 individual databases and more than 150 years of legal publishing and editorial curation. Hron said hundreds of subject-matter experts helped define training objectives, create examples of legal questions and judge responses in blind comparisons. Specialization can damage a model's broader abilities if it is handled poorly, said Jonathan Schwartz, head of foundational research at Thomson Reuters. The research team therefore focused on continual learning, or adding domain skills without erasing existing capabilities. "If you simply take the open-source model without any of these additional steps, it won't know as much," Schwartz said. "You won't necessarily be aligned with your values, and it won't be as good as using the tools that you've built later on." Thomson Reuters said internal tests showed Thomson is broadly competitive with leading models when all had access only to the web. It moved to roughly equal or slightly better performance when connected to Thomson Reuters content, said Andrew Bean, a senior research scientist at the company. The tests assessed both the completeness of answers and whether citations supported their claims. A technical report to be published in the future is expected to provide additional benchmark results. Those results have not yet received extensive independent validation. Thomson Reuters has begun sharing the model with legal experts and academic institutions for testing and plans to release a smaller open-weight version on Hugging Face under a noncommercial academic license. It is also developing a portal through which outside developers can request application programming interface keys and test the model directly. More to come Only about 10% of the company's total information base has been used so far, Bean said. The next step is not simply adding more material but turning the most useful content and product activity into better training signals. Ownership is also central to the company's argument for sovereign AI. Thomson Reuters said customer data is not used to train the model and that controlling the model gives it more authority over deployment, governance and future development. It's discussing direct model access with large law firms and corporations and is open to the possibility that customers could adapt Thomson to their own knowledge and workflows. Hron acknowledged that maintaining a proprietary model raises questions about whether Thomson Reuters can keep pace with faster-moving AI laboratories. He argued that improvements in open models will give the company stronger foundations for later versions, while its own investment can remain concentrated on professional work. "I don't see owning an AI model that embodies the knowledge and expertise that TR possesses as something that's non-core to what we do," he said. "AI is a new mechanism for expertise delivery."

Thomson Reuters Launches Large Language Model

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