Route every Claude Code message to the right model with Jev

TypeSafe AI’s Jev, released in September 2026, is presented as a fast, low-cost “System One” decision model that classifies, scores, or selects from predefined options rather than generating language, using a training approach called reinforcement learning for calibrated decisions. The open-source jev-router project uses Jev as a local proxy for Claude Code, evaluating each user-written message to select an appropriate Claude model tier, from Haiku for mechanical tasks to Sonnet, Opus, or optionally Fable for more demanding work. To preserve prompt-cache efficiency and reduce disruptions during ongoing work, the router can raise a session’s model tier but generally does not lower it, while tool calls retain the tier assigned to their originating message. It includes safeguards such as explicit model pins, secret scanning, and questions designed to detect attempts to manipulate routing decisions, treating Jev’s classifications as advisory rather than authoritative. A local dashboard and reporting tools expose routing outcomes, probabilities, estimated costs, and savings without logging prompts or keys, while setup supports macOS and Linux services, Claude Code by default, and additional Codex CLI routing options.

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