Multi-agent Model Routing by Role

These three dimensions make the comparison clearer:

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Role	Default recommendation	Cost-efficient alternative (cost/task)	Quality alternative	Notes
Controller	Sol-medium	Luna-max	Astra-low	Stable judgment matters by default; escalate hard tasks to Astra.
Worker	Luna-max	Luna-max	Sol-medium	For bounded tasks, optimize for lower cost per completed task.
Reviewer (weak)	Luna-max	Luna-max	Terra-xhigh	A weak reviewer mainly needs to catch obvious bugs.
Explorer	Luna-max	Luna-max	Terra-xhigh	Search and probing consume lots of tokens, so cheap throughput matters.
Advisor	Astra-low	Sol-medium	Astra-low	Advisor calls are infrequent, so each judgment can justify more spend.

If the default recommendation is redefined around cost/task

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Role	Best-value default	Extreme savings	Quality-first
Controller	Sol-medium	Luna-max	Astra-low
Worker	Luna-max	Luna-max	Sol-medium
Reviewer (weak)	Luna-max	Luna-max	Terra-xhigh
Explorer	Luna-max	Luna-max	Terra-xhigh / Sol-medium
Advisor	Sol-medium	Sol-medium	Astra-low

That slightly changes the earlier conclusion:

Sol-medium can also be the default Advisor model.
- It is more robust from a cost/task perspective.
- Escalate to Astra-low only for final arbitration, architecture decisions, or difficult multi-option trade-offs.
Terra-xhigh
- Fits best as a quality-upgrade tier for Reviewer / Explorer.
- It is less attractive as the global default.
Luna-max
- Is the natural default for Worker / Explorer / weak Reviewer roles.

Sol = default decision layer, Luna = default execution layer, Terra = mid-tier quality upgrade, Astra = high-value arbitration layer.