---
library_name: mlx
tags:
  - mlx
  - model-routing
  - synthetic-data
---

# Succinct Router 14M

A 14M-parameter decoder-only routing model trained from random initialization. It predicts an
independent pass probability for each of these candidate configurations:

- `gpt-5.6-luna-none`
- `gpt-5.6-terra-low`
- `gpt-5.6-sol-medium`

This model does **not** answer prompts. It selects the cheapest candidate configuration expected to
pass a calibrated quality threshold.

## Evaluation

```json
{
  "exact_route_accuracy": 0.9508599508599509,
  "unsafe_downroute_rate": 0.005528255528255528,
  "selected_model_pass_rate": 0.9944717444717445,
  "abstention_rate": 0.0,
  "savings_vs_always_large": 0.8486732186732187
}
```

## Run with MLX

```bash
pip install "mlx>=0.32,<0.33" tokenizers
python route_mlx.py --model-dir . --prompt "Extract the invoice number from INV-4821."
```

The result contains calibrated pass probabilities and the selected candidate configuration. If no
candidate clears the threshold, `abstained_to_largest` is true and the route falls back to the
largest configuration.

## Limitations

Training and evaluation data are synthetic and mechanically graded. The artifact is a learning
prototype, not a production-ready router. Validate it on anonymized real traffic before making
product or cost claims. MLX runtime parity and latency must be measured on Apple Silicon.