RightModel: Zero‑LLM‑Call Model Picker Using Precomputed AI

RightModel is a tool that recommends the ideal LLM model for a given task without making any LLM calls in the default request path, evaluating parameters against a pre‑existing, human‑reviewable ruleset and returning a JSON response with the recommended model and reasoning. The core logic maps task types to model tiers and includes tie‑breakers, while the pricing data is refreshed via a scheduled workflow pulling from OpenRouter, keeping the ruleset up‑to‑date without live API calls during user requests. When a request falls into an ambiguous or low‑confidence category, the tool presents a Deep Analysis button that triggers an optional LLM call powered by Gemini 2.5 Flash, allowing users to opt‑in for deeper analysis. This architecture follows the Precomputed AI pattern, which shifts LLM reasoning out of the real‑time request path and relies on a versioned artifact, a regeneration cadence, and a declared escalation path. The JSON response includes task_type, recommended_model, and reason fields, and the system logs the last refresh timestamp for transparency. By eliminating live LLM calls for the majority of requests, RightModel reduces latency and token consumption, making it attractive for enterprises building AI‑powered applications. The pattern encourages developers to audit which parts of their reasoning truly need to be live and to consider precomputing the rest.