From "Using Other People's Models" to "Training Their Own"

An American unicorn that built an $11 billion legal-AI business is about to ship its first model — and the training base is neither GPT nor Claude, but Kimi K3, the open-weight model released by Chinese startup Moonshot AI.

According to a Business Insider exclusive, legal-AI company Harvey on Tuesday introduced Harvey Tenet, its first in-house, proprietary model for legal work. It is designed to take on more of the tasks typically done by lawyers over hours or days, at a lower cost than the third-party models Harvey currently relies on.

Who Is Harvey, and Why Build a Model Now

Harvey's original playbook was the classic "application layer on top of the model layer": software for law firms and corporate legal teams built on general-purpose models from OpenAI and Anthropic. Business Insider reports it has grown into an $11 billion legal-software business.

The problem: every time a lawyer calls an external model through Harvey, Harvey pays the model provider — and that toll compounds with usage. The more uncomfortable part is that suppliers are turning into competitors. Anthropic has been chasing lawyers with plugins for document review and drafting, while OpenAI hired Ironclad founder Jason Boehmig to lead its legal push.

The motivation for an in-house model is blunt: route more work through your own engine and improve margins without asking customers to pay more. Cofounder Gabe Pereyra, a former Google DeepMind researcher, also cited quality: Harvey already routes different tasks to different models based on their strengths, and Tenet becomes another option in that mix.

The Training Data Was "Manufactured by Hiring Lawyers"

To train a legal model, you first need data that teaches it how lawyers think. BI's reporting lays out the method: Harvey hired attorneys, on staff and on contract through companies like Mercor and Snorkel, to dream up mock disputes and case files, then graded the models on how well they reasoned through them — and trained on the result.

As for the base, BI's wording: Harvey used the material to train a version of Kimi K3 — "a low-cost, open-source model from the Chinese startup Moonshot" that has whipped the tech world into a frenzy over its power and price since its July release.

MoonshotAI's GitHub repository describes Kimi K3 as a 2.8-trillion-parameter open-weight model built on Kimi Delta Attention (KDA) and Attention Residuals, natively multimodal, with a 1-million-token context window.

The Public Kimi K3 Was Already #1 on the Legal Benchmark

Harvey's choice of Kimi K3 didn't come from nowhere. On Artificial Analysis's independent Harvey LAB-AA benchmark — 120 private tasks spanning 24 legal practice areas, where agents read case files in a sandbox and produce real legal deliverables such as memos, disclosure schedules, and deposition summaries — the public Kimi K3 (max) ranks first with a 94.6% criterion pass rate, ahead of Claude Fable 5 (93.6%) and Muse Spark 1.1 (93.1%).

In other words: on the exam Harvey itself helped write, one of the strongest test-takers was already Kimi K3.

Two caveats, though. First, Tenet's own benchmark scores have not been released — Harvey only says research comparing it to other models is coming soon. Second, BI itself cautions that vendor-run benchmarks deserve skepticism: makers use tests to find weaknesses and then train against them, which over time is a bit like taking an exam after helping write the answer key.

Not Just a Model — a Full "Harvey II" Rollout

Tenet is part of a broader rollout the company calls Harvey II. Chief Product Officer Anique Drumright says Harvey is also adding a new "Memory" feature that lets users save preferences about how they work, so agents can carry those instructions across tasks.

Pereyra's endgame is more aggressive: he wants Tenet to become the starting point for law firms to train their own models — each firm layering decades of its lawyers' know-how on top of Tenet to build a proprietary version. BI's take: that would push Harvey toward looking less like a software provider and more like a Big Four professional-services firm. And for a company long dismissed as a "ChatGPT wrapper," if it pulls this off, the wrapper starts to look like the most valuable layer in the stack.

One timing detail worth noting: Tenet isn't live in Harvey's product yet, the company won't say when it will be, and Pereyra declined to name any law firms that might be testing it.

So What

For Chinese open-weight models, this signal is harder than any leaderboard. Download counts only prove a model was taken; being chosen as the training foundation of an American unicorn's first proprietary model proves it is depended upon. When vertical players are willing to build even their in-house model on a Chinese open-weight base, the globalization story of open models has genuinely moved from Hugging Face leaderboards into someone else's production pipeline and cost structure.