Thomson Reuters priced the middle path at $40M, and still pays Anthropic

The first use is Tabular Analysis in CoCounsel Legal, which can work across as many as 10,000 documents and answer up to 100 questions about them; Thomson is becoming the default model for that feature.

For now customers will not buy access to Thomson directly; it powers specific capabilities inside Thomson Reuters products, a smaller open-weight version is available to researchers on Hugging Face, and the company said it is looking at ways it could commercialise Thomson in future.

A smaller version of the model is being released under a non-commercial license.

Schwartz says standard fine-tuning techniques "tend to have a strong tendency to degrade general capability" and leave you locked into the provider for inference costs and roadmap, while a smaller in-house model pays off precisely on high-volume work like document review.

Hron describes the choice as "renting a house versus buying a house": every expert review during a product update becomes training data, and with an in-house model "you are building equity in something that you own for the long-term," whereas with third-party models that value evaporates at the provider.

"There's no guarantee any AI model, including Thomson, is error-free," Thomson Reuters said.

Thomson is trained to flag uncertainty rather than force a confident-sounding answer, and the company has kept retrieval: the model can pull from sources such as Westlaw and Practical Law so responses are grounded in material a legal professional can check.

Research chief Jonathan Schwartz says the bigger finding is "less the individual model and more the model factory we built."

The comparison is skewed by method: Thomson competes with test-time scaling while GPT-5.5 runs without a reasoning mode.

Only with access to the company's content does Thomson edge past GPT-5.4, 0.83 to 0.82.

Evaluation lead Andrew Bean says there is "a big uplift that comes from being able to train on and practice with your own tools," something outside providers cannot do, and that with web access Thomson is "within the scope of the other models, but certainly not the leader yet."

The Decoder notes that GPT-5.4 improves just as sharply when given the company's content, so data access does almost as much work as the specialised training, and that the company did not test newer models.

The more widely touted figure of $450,000 covers only the final training run of the current version.

Spread over twenty-four months, about $40 million is roughly $1.67 million a month, and since the spend ran longer than two years that monthly figure is an upper bound.

In Thomson Reuters' in-house Deep Research benchmark with web access alone, Thomson scores 0.53 on factual accuracy while GPT-5.4 hits 0.65.

Access to Thomson Reuters content lifts Thomson's factual accuracy by 0.30 (0.53 to 0.83) and GPT-5.4's by 0.17 (0.65 to 0.82), leaving Thomson ahead by 0.01.

The $450,000 final training run is about 1.1 percent of the roughly $40 million programme, which is about 89 times the run cost.

Thomson was not built from the ground up: the company started with an existing open-source foundation and spent approximately $40 million training the model, including compute and talent.

CTO Joel Hron says the company has "changed the open source starting point like probably close to a half dozen times already."

On Stanford LegalBench, Thomson scores 0.823 and trails Gemini 3.1 Pro and GPT-5.5; on the Harvey Legal Agent Benchmark it sits just behind Opus 4.8; it leads on instruction following and on PrBench Legal; on reasoning and especially coding it falls off sharply.

The New Stack reports that early benchmarks show Thomson competing with models from OpenAI, Anthropic and Google across several professional and general-purpose evaluations.

Working with Imperial College, Thomson Reuters first retrained the Qwen base for safety, ethics and political neutrality; this intermediate version is called Snowdon, after the mountain in Wales.

After Snowdon came pre-training on the company's own content, post-training with domain experts, and agentic reinforcement learning inside the company's own tool environments.