Introducing Tenet, our first model post-trained for legal. Tenet is a Kimi K3 base that we post-trained with @FireworksAI_HQ on a corpus of publicly available legal data, synthetic data, and human expert data simulating long-horizon legal work. Training increases Tenet's all-pass rate by 82% on LAB and 22% on LAB Contracts relative to the Kimi K3 base model. It achieves state-of-the-art performance on LAB Contracts and places second on LAB. These gains generalize to other leading agentic benchmarks including @mercor's Apex Agents - Corporate Law, @crosbylegal's Redline Bench, and @scale_AI's Professional Reasoning Bench. Tenet is also optimized for token efficiency, operating at less than a fourth the cost of leading foundation models. We additionally post-trained three specialist models for Tenet to use as subagents: 1) M&A Diligence: post-trained with @baseten on our LAB Diligence environment in an RLM harness, this model is optimized for high-scale, long-horizon tasks. 2) Review Tables: trained with @appliedcompute on our Review Table environment, this model is state-of-the-art and cost-effective at high-volume document review and structured data extraction. 3) Firm Knowledge: trained with @EngramLab on our synthetic law firm environment, this model is optimized to learn and search over a firm's knowledge via memory and structured notes. More details on model training, environment design, benchmarking, results, and more in the article by @gabepereyra below. What's next for Harvey’s research? - Scaling LAB to more jurisdictions, practice areas and workflows - Scaling compute to bring new generalist models and capabilities to Harvey More to come soon.

We're open sourcing a 100M+ token synthetic law firm we built with @EngramLab. The firm contains work product from 250+ synthetic matters across 46 clients, spanning ~10k files. We built this environment to evaluate an agents' ability to search and understand a firm's past practice to inform present work - the same knowledge that a tenured associate or partner would have. It's our first step towards building agents that deeply understand a firm's work and processes. More to come soon Deep dive by @ItsJulioPereyra and @nikogrupen:

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Introducing Harvey Research: We've shared our model strategy. We've open-sourced Legal Agent Benchmark, the largest benchmark for long-horizon legal work spanning 1,200 tasks across 24+ practice areas. And we've collaborated on research with leading neolabs and inference providers like @baseten, @trajectorylabs, @LangChain, @FireworksAI_HQ, @appliedcompute, and @EngramLab. Now we have a home base for it. Live at: harvey.ai/research

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Harvey is scaling quickly, and we’re expanding our team. 2025 highlights: - Used by 1000+ firms in 59+ countries - 500+ employees - $190M+ in ARR As we look ahead, Harvey is hiring for several high-impact roles. See our open roles here: t.co/ZQtzZBzUlm

How does a seasoned Supreme Court lawyer prepare for the biggest case of his life? Using Harvey. Read how Harvey supported @neal_katyal in refining his arguments before the Supreme Court and how we are bringing those tools to law schools with Harvey Moot: t.co/r3IIKiTwxV