Model ML completes finance work more efficiently with GPT-5.6 Sol

Before financial analysis can stand up in front of clients or senior decision-makers, teams have to carry it through a demanding last mile: reconciling evidence, building and formatting the file, checking every number, and linking each claim to its source. The finished PowerPoint deck or Excel workbook has to be editable and ready for scrutiny.

Model ML cofounders and brothers Arnie and Chaz Englander saw how much work that required when, after two successful exits, they began investing through a private family office and, as builders do, building software to help themselves.

Grown out of that software, Model ML’s agents help finance professionals carry a workflow from the initial request through research, analysis, and a finished deck or workbook. At the center, a core agent plans the work, selects the right tools, reconciles evidence, and runs calculations, routing each step to the model best suited to it, which is often GPT‑5.6 Sol. Among those tools is Model ML’s own document tooling, which creates native PowerPoint and Excel files with traceable sources.

In Model ML’s Composite, the company’s evaluation benchmark for AI in financial services, GPT‑5.6 Sol used 36% fewer tokens per workbook than Opus 5 in an Excel workflow.

By carrying the work through that last mile, the agent can also save time on individual deliverables and help teams process large volumes of source material. At one global asset manager, a bespoke tearsheet that took an analyst about an hour to assemble now takes about five minutes. In another workflow, Model ML agents processed virtual data rooms containing more than 100,000 rows and hundreds of files in one pass.

GPT‑5.6 Sol completed the PowerPoint workflow in 100% of test cases, compared with 76% for Opus 5, and cleared Model ML’s professional-readiness gate, a measure of whether the output was ready for substantive review, in 43.3% of cases, versus 26.7%. It also led Opus 5 on deck quality, brief adherence, hierarchy, and consistency.

Model ML helps finance teams by automating finance workflows end to end. From a brief and source material, the agent can carry an assignment through research and modeling to finished materials for clients or deal teams. The finance professional checks the assumptions, sources, and message before sharing the work.

Model ML calls the product “surface-agnostic.” A finance professional can start an assignment in email, or the Model ML app, and continue it in its Microsoft Office plug-ins without explaining it again.

That continuity extends into the finished work. For an investment committee deck, Model ML can turn a brief and source material into an editable PowerPoint. For an Excel task, the agent can start with a client template or blank workbook, gather the required data, build formulas and logic across multiple tabs, then apply finance-specific formatting to produce a complete spreadsheet or financial model.