How Model ML Uses GPT-5.6 Sol to Get Finance Work Done More Efficiently

**TL;DR:** Model ML uses GPT-5.6 Soul to build financial agents. Through internal evaluation and automated execution (research, calculations, chart generation, page-by-page polish), it gets work like IC memos close to a final deliverable, letting users focus on judgment and refinement. ## From “low-quality content” to “real work” In a world flooded with AI-generated low-quality content, there seems to be a huge gap between low-quality content and genuine work. The same gap exists in financial analysis: auto-generated results are easy, but high-quality deliverables you can actually hand off are hard. ## Model ML’s agent approach Model ML provides ready-made and custom agents that operate the way their clients do. In other words, these agents are not just “tools” — they execute tasks according to the client’s workflows and standards. To ensure results, the first thing Model ML does is run the model through their internal evaluation system. The evaluation results were striking — frankly, it left every metric far behind. ## Case study: Building an IC memo Take the IC memo (Investment Committee Memo) as an example. The user provides a brief and specifies the direction of information the agent should pull. Model ML observed GPT-5.6 Soul performing as follows: 1. **Make a plan**: First define what the slides should contain and look like. 2. **Focused execution**: It does the research, runs the calculations, and generates native graphics, tables, charts, and logos. 3. **Visual review**: It reviews each slide page by page from a visual polish perspective. Model ML says they have never seen another model achieve this level. ## The deliverable In the end, the system delivers an **editable PowerPoint** to the user, complete with all **traceable sources**. This means users don’t need to start the analysis from scratch; instead, they focus on **judgment** — specifically, refining assumptions and distilling information, rather than redoing the analysis. ## Comparison with earlier models Earlier models could also complete an analyst’s work, but users first had to clearly break down the task and specify exactly what the expected output should look like. In other words, earlier models were more like “executors” — users had to think through every step in advance. Thanks to GPT-5.6 Soul, Model ML finds that the agent can now get very close to the final result. The user’s role shifts from “defining every step” to “judging and refining near-complete content.” --- Source: How Model ML Uses GPT-5.6 Sol to Get Finance Work Done More Efficiently - YouTube (https://www.youtube.com/watch?v=OEkxKdhtQng)