When we introduced GPT‑6 Astra, we demonstrated how far our models have come in using computers for professional work, from preparing documents to testing websites. Our next goal is to make agents more capable and efficient at using specialized software to solve complex business problems. We’re exploring how to train models to understand a company’s business rules, execute multi-step workflows, and verify that their work meets the original requirements.
To accelerate this research, we’re partnering directly with a small number of software companies that understand these workflows best. Our first partner is Ironclad, a leader in AI contracting. Working closely with Ironclad’s team, we’ve developed tasks that require agents to configure agreements, approvals, and reusable legal terms. Ironclad’s expertise has been instrumental in defining what success looks like and bringing real customer needs directly into frontier model development.
GPT‑6 Astra is our first frontier model trained on Ironclad tasks. On our research evaluation, its average score was 32% higher than GPT‑5.6 Sol’s, while estimated time per attempt was 48% lower. Ironclad employees and people who use Ironclad at Open AI helped our researchers identify 11 tasks across legal, commercial, and procurement work. We evaluated each task against 8 to 50 criteria, depending on its complexity.
Across the 11 research tasks, Astra’s average score was 55.0%, compared with 41.6% for GPT‑5.6 Sol, while estimated average time per attempt fell from 37.0 minutes for Sol to 19.2 minutes for Astra. An internal model used in the development of Astra achieved an even stronger 63.7% on these tasks, and we aim to bring these further gains to future models.
Source: OpenAI Blog · Summarized by HeadlinesBriefing