Harvey has released Harvey Tenet, its first post-trained model, as a research preview on August 20, 2026. Tenet is built upon the Kimi K3 base model and has undergone post-training using Fireworks through asynchronous reinforcement learning, specifically targeting long-horizon legal work. The comprehensive training corpus integrated synthetic data, publicly accessible legal datasets, and data provided by human legal experts. Harvey has explicitly stated that no customer data was utilized in the training process. When evaluated against the base K3 model, Tenet demonstrated a significant improvement, completing nearly twice as many held-out tasks on Harvey's proprietary Legal Agent Benchmark (LAB). Furthermore, it achieved 20% more completions on the LAB: Contracts sub-benchmark, leading to an increase in the overall pass rate by 9 percentage points and the contracts pass rate by 2 percentage points. Harvey reports that Tenet has achieved state-of-the-art performance on LAB: Contracts and secured second place on the broader LAB. These performance gains have also shown transferability to other agent systems, including Mercor's APEX Agents and Crosby's Redline Bench, even without further training on those platforms. The dual objectives behind the development of Harvey Tenet are to advance frontier legal intelligence using open-weight models and to provide law firms with the capability to develop and own their specialized AI models. However, Harvey Tenet is not yet deployable as a standalone product. As of its announcement on August 20, 2026, it remains a research preview. Harvey has not yet published the model's weights, a detailed model card, or an API endpoint for external access. While the base Kimi K3 model is open-weight, Tenet itself is a proprietary checkpoint developed by Harvey. The company indicates that the advancements made in this research will be integrated into Harvey's existing products over time, transitioning from research to production. The current offering provides the methodology and training approach rather than a ready-to-use artifact. Access to Harvey's platform, which is sold to law firms, mid-sized firms, and in-house legal teams, is required for enterprise-tier engagement. A laboratory equipped with a reinforcement learning stack could potentially replicate the training methodology. The training process involved approximately 150 NVIDIA B300 GPUs utilized over a two-month period. The primary industries targeted for this technology include legal services, corporate in-house legal departments, private equity and investment banking for M&A diligence, and regulated sectors with high contract volumes such as insurance, financial services, healthcare, and energy. Potential applications encompass the generation of M&A due diligence memos from datarooms, contract drafting, review, and redlining, structured data extraction from up to 10,000 documents, and precedent search across a firm's internal knowledge base.