Choosing a Model

Model choice is a vehicle decision.

You would not use a freight truck for a grocery run, and you would not use a scooter to move construction equipment. Agent work is the same: different jobs need different levels of capability.

Routine monitoring and mechanical checks usually belong on lighter, faster models. These tasks are frequent and low-complexity, so efficiency matters most.

Strategy, planning, and high-stakes writing usually belong on stronger general models. Here, synthesis and judgment matter more than raw speed.

Heavy implementation and deep technical analysis belong on the strongest execution path available, especially when errors are expensive and verification matters.

The operating principle is practical: use the least expensive option that reliably does the job, then step up when complexity or risk demands it.

Teams often learn this the hard way. Early setups run the biggest model for everything "just to be safe." That feels simple, but it wastes budget on tasks that do not benefit and hides where capability actually matters.

One more distinction helps: model versus execution environment. A model can reason about code. A coding environment can reason, edit files, run checks, and verify results in place. Those are different tools, and choosing correctly is part of good system design.

You do not need to become a model benchmark expert. Just match tool to job with the same common sense you would use when picking a vehicle.