Liquid AI just released a model that claims the top spot on a dedicated Decision Index and leads all eight categories in their own 112-evaluation suite. Most AI releases are still judged on general benchmarks (coding, math, knowledge). Decision models are optimized for something narrower: choosing among fixed options under constraints, often with multimodal inputs. That is closer to how a lot of enterprise work actually happens (approvals, prioritization, routing, risk scoring) than open-ended chat. If the benchmark results hold up outside their suite, this is another signal that the market is splitting. General models keep getting more capable. Specialized models are starting to win on the narrower jobs where companies are willing to pay for reliability over flexibility. The companies that figure out which workflows belong in each bucket will get better unit economics than the ones still forcing everything through a single frontier model.