Harvey launches Tenet legal model trained on Kimi K3

Engineers at US legal AI company Harvey have put Kimi K3 into a new legal model called Tenet. Tenet is not designed for “legal knowledge Q&A,” but for the lengthy workflows lawyers handle every day: finding clues in piles of client documents, conducting repeated searches and calling tools, identifying risks, filling in citations, and ultimately delivering a revised contract or legal memorandum.

Harvey was founded four years ago, has reached a valuation of $11 billion, and currently serves more than 1,300 organizations and 100,000 lawyers. Tenet is its first model post-trained on an open-weight model, specifically to handle legal tasks that require extended execution and repeated tool calls. Initial results on the company’s legal agent benchmark, LAB, show that Tenet successfully completed nearly twice as many test tasks as the base model.

The key to this approach is not simply giving Kimi a new name. A general-purpose model may know legal concepts, but it may not know where a real task should begin, when another document needs to be checked, or what exactly is wrong with a deliverable. Harvey needs to use its own data, compute and legal expertise to continue training the open model into a tool better suited to lawyers’ workflows.

A similar form of “further processing of a base model” is emerging in programming tools. Cursor’s Composer 2 has been confirmed to be trained on Kimi K2.5, and the subsequent Composer 2.5 continues to use the same base. The UK’s Cosine trained Lumen Outpost, which maintains legacy code, on Kimi K2.6, while Devin has also publicly said it uses Kimi K2.7. In just six months, different versions of Kimi have successively entered these overseas products.

Specifically, this gives small and mid-sized AI companies another viable business path: they do not have to train a foundation model from scratch, nor do they have to keep relying on closed services such as OpenAI or Anthropic and paying by usage as their products grow. Customers get products for legal work, programming and other fields that are more closely aligned with real workflows, rather than a general-purpose model that only knows how to chat; model providers, meanwhile, may become the raw material and upstream foundation for other products.

What can currently be confirmed, however, is still limited to the results published by the companies. Tenet’s “nearly twice” figure comes from the initial testing of Harvey’s LAB legal agent benchmark; the entry of Chinese models into overseas products also does not mean those products can do without industry data, compute and real-world validation. Open weights lower the starting point, but genuine product capabilities still have to be trained through specific work.