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Thomson Reuters will use its new legal model for specialized tasks while retaining third-party frontier models elsewhere in CoCounsel. Its performance claims have not yet received extensive independent validation.
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Thomson Reuters is positioning its legal model as a specialized layer inside a broader AI stack, rather than a replacement for frontier models. Thomson, adapted from an open-weight base and trained with Westlaw and Practical Law expertise, will power CoCounsel’s Tabular Analysis by default, while administrators retain model choice. The project cost about $40 million over two years, but its performance claims remain company-reported pending a technical report and independent validation.
The model was developed from an open-weight base, not trained from scratch.
Thomson Reuters reported about $40 million in project costs and $450,000 for the final training run.
CoCounsel remains multimodel; customers can override Thomson for Tabular Analysis.
Thomson Reuters has launched Thomson, its first proprietary large language model, and made it the default engine for Tabular Analysis, the document-review capability in its CoCounsel Legal AI assistant. The rollout puts a model trained on the company’s legal content and tools into a high-volume legal workflow.
Thomson is not replacing every model in CoCounsel. Thomson Reuters says the product will remain multimodel, using Thomson where legal specialization is advantageous and third-party frontier models for other work. Administrators can select another model for Tabular Analysis despite Thomson being the default.
The company did not build a foundation model from scratch. It started with an open-weight model, then added proprietary content, training methods and professional expertise. Thomson Reuters says it is pursuing legal intelligence rather than competing with the largest AI labs across every field.
Thomson Reuters says hundreds of subject-matter experts helped define objectives, create legal-question examples and judge responses in blind comparisons. The tool-focused training is designed around the company’s existing legal research and practical-guidance products, not just a standalone chat response.
Thomson Reuters said people and computing for the project cost about $40 million over two years.
The company said the final training run cost about $450,000.
Thomson Reuters says internal tests found Thomson broadly competitive with leading models when each had web access only. When connected to Thomson Reuters content, the company says it performed roughly equally or slightly better. The evaluations measured answer completeness and whether citations supported the claims made.
Those company-supplied results have not received extensive independent validation. Thomson Reuters expects to publish a technical report with additional benchmark results, leaving the external evidence for the performance comparison unfinished at launch.
Thomson Reuters says customer data is not used to train Thomson. It is discussing direct model access with large law firms and corporations, and is open to customers adapting the model to their own knowledge and workflows.
The company has begun sharing Thomson with legal experts and academic institutions for testing. It also plans to release a smaller open-weight version on Hugging Face under a noncommercial academic license and is developing a portal for outside developers to request API keys and test the model directly.
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