The Hard Part Comes Before the Choice
TL;DR for operators A specialized model that selects directly among predefined answers can outperform leading general-purpose LLMs on some benchmarks, yet struggle when those answers require calculation. The evidence: Jev ranks first among 20 compared models on MMLU-Redux (95.0%) and ARC-Challenge (97.6%). On MathQA, it scores 75.0%, below all 19 general-purpose LLMs and 17.7 percentage points behind the frontier median. The capability boundary: A fixed answer format does not remove the computation needed to identify the correct option. Recognition-oriented and calculation-intensive decisions should be evaluated separately. The deployment implication: Businesses can test specialized decision models for constrained classification and answer selection while retaining reasoning-capable alternatives for quantitative tasks. A hybrid routing policy is a hypothesis, not a demonstrated result. The qualification: Multilingual results vary by benchmark origin, and mixed evaluation protocols complicate model rankings. The study does not measure latency, cost, confidence calibration, or production reliability. A fixed answer does not mean a simple decision Consider two business systems that must choose one answer from five options. One classifies an incoming request into a predefined category. The other determines which of five amounts correctly reconciles a set of transactions. ...