AutoRoute Picks the Right Codex Model So You Stop Burning Tokens on Button States
Codex accounts now pack multiple models at different price points, but the default workflow is to either waste expensive reasoning on trivial UI tweaks or burn time retrying cheap models on hard bugs. AutoRoute turns that choice into a repeatable rule, cutting token cost and rerun time without surrendering control.
AutoRoute replaces manual model selection inside Codex with a six-dimensional scoring system that weighs complexity, change scope, reasoning depth, risk, context size, and iteration length. It then recommends a model and one of seven effort levels, keeping the switch command ready but never executing it automatically. Three modes control how assertive it is: suggest-only, manual (current config only), and auto (prepares a new-session command). Two explicit flags — `--run` and `--session` — let a developer apply the recommendation to a fresh session or directly switch the current terminal session by typing `/model` and `/effort`.
Runtime signals feed an adaptive upgrade path: repeated test failures, a spike in changed files, or multi-language scope can raise the score mid-task and trigger a stronger model or higher effort. A lightweight availability probe runs on first use and refreshes every 15 minutes or on each Codex launch via a wrapper script; when the probe is blocked, routing continues with discovered models and marks `probe_status=blocked` instead of failing.
Real routing examples show a button loading state landing on `gpt-5.6-luna` with `low` effort, while a cross-page React state sync bug gets `gpt-5.6-sol` with `high` effort. The tool ships with evaluation scripts for comparing success rate, token spend, retries, and latency, and it accepts a `--workload` flag for explicit workload-type hints.
The core friction isn't model capability — it's decision fatigue. When an account holds several models and the cost gap between them is real, every small task forces a micro-economic choice that most developers skip by either always picking the strongest or always picking the cheapest.
Separating model selection from effort selection acknowledges that reasoning depth and model intelligence are distinct levers. A medium model thinking harder can outperform a strong model thinking shallowly on tasks with moderate complexity but deep dependency chains.
Adaptive upgrading based on runtime failure signals turns the router from a static classifier into a feedback loop. This mirrors how a human colleague would reassess a task after seeing it go sideways, rather than sticking to the initial estimate.
The `--session` flag is a notable workaround for a platform limitation: Skills normally can't touch a running Codex session. By locating the terminal and typing commands into it, AutoRoute sidesteps that restriction without requiring API-level integration.