Decision

Introduction Over the past month I’ve written two articles on decision models: Jev vs Laya compared the closed-source Jev with the open-source Laya, and Microsoft-Decision-1 Explained added Microsoft to the picture. The question I’ve been asked most since then is: can I train one myself? Yes, you can, and the bar is lower than you might think. Just look at the third-party leaderboard JevBench: as of mid-October, its open-weights board ranks more than 160 systems, the vast majority built by the community and small teams. The ones at the top aren’t mysterious either: H2O-Lightning-4B, first on the composite score, is a fine-tune of Alibaba’s open-source Qwen3.5-4B; Quyet-1.0-Large, first on the capability score, is Gemma-4-31B plus an adapter; and Microsoft’s own Microsoft-Decision-1 is also built on the open-source Qwen3.5-9B. ...

Introduction Last month I wrote Jev vs Laya, comparing TypeSafe AI’s closed-source decision model Jev with the community’s open-source Laya. Back then, “decision models” were a brand-new category: Jev launched on September 15, and Laya was open-sourced three days later. Just three weeks later, the field is crowded: on October 6, OpenAI opened its Decisions API built on GPT-6 Luna; companies such as Liquid AI and Inception have launched their own decision APIs; and the third-party leaderboard JevBench now ranks more than 140 open-weight systems. On October 9, Microsoft joined the race with Microsoft-Decision-1. ...

Introduction In September 2026, a new kind of model that “doesn’t talk” suddenly became the talk of the AI world. On September 15, TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released Jev. It doesn’t chat, write articles, or generate code. It takes a piece of state and a set of predefined questions, and directly returns structured decisions with calibrated probabilities. ...