OpenAI customer story: Terminal X says GPT-6.1 Sol matches senior investor judgment 88.6%
OpenAI’s Terminal X case study describes Capital Memory agents on GPT-6.1 Sol and GPT-6 Luna; its Eval Agent agreed with senior human reviewers 88.6% of the time across 500 scored judgments, with about 25% more mandate-drift catches and ~30% lower cost per query versus the next-best model, per the companies.
In an Oct 20, 2026 customer story, OpenAI says Terminal X builds a Capital Memory operating system for investment firms: forward-deployed engineers encode each firm’s memos and unwritten processes into asset-class and strategy-specific agents that run on-premise or in the firm’s own cloud, inside a private data room, across English, Korean, Japanese, and Chinese documents. OpenAI models supply the reasoning—GPT-6.1 Sol for judgment-heavy, long-context stages (thesis synthesis, key considerations, mandate fit) and GPT-6 Luna for high-volume steps.
Terminal X’s Eval Agent grades every agent output against a 54-criterion rubric (sources, numbers, narrative coherence, house-view fit), turning each failure into Memory. Per Terminal X’s own evaluation cited by OpenAI: across 500 scored judgments, the Eval Agent on GPT-6.1 Sol agreed with the senior human reviewer 88.6% of the time versus 86.4% for the next-best model (+220 bps); on style-drift / mandate-departure errors, GPT-6.1 Sol caught about 25% more departures; and the same workflows ran at about 30% lower cost per query. The piece also cites one private-equity customer’s claimed $2.4M annualized savings on diligence and memo workflows, plus Terminal X’s figures of about 1,700 firm Memories, more than 1 million custom workflows, and weekly active users equal to 81% of monthly active users. These figures are company/customer claims, not independently re-measured here.