Model orchestration

Match model tier to task weight, the same way the tool-precedence gate matches tool weight to task weight. A strong model earns its cost on judgement — planning, decomposition, ambiguous design, review. A cheap model is faster and far cheaper on well-specified mechanical work — applying an agreed edit, renaming, boilerplate, search. Spending a frontier model on a one-line rename is waste; spending a cheap model on an architecture call is a bug. Route deliberately.

This is a decision policy, not a framework. It costs nothing until it fires.

Tiers (route by shape, not by brand)

Think in three tiers so the policy survives model renames. Today's mapping:

Other providers tier the same way (GPT-5 / mini / nano; Gemini Pro / Flash / Flash-Lite). Route by the task's shape; let the harness or /model pick the concrete model.

Effort before model-switch

Switching tier is not the only knob. Modern models expose a reasoning-effort / thinking-level dial (Claude effort low→max; GPT-5 reasoning.effort; Gemini thinking level), and tuning it is often a finer, cheaper lever than changing model:

• Lower effort (or none) for easy, well-specified, latency-sensitive work — it cuts billed reasoning tokens without dropping a tier.
• Higher effort only for genuinely hard reasoning; more is not automatically better — on a weakly-specified task high effort causes overthinking and wasted tokens.

So the ladder is right effort → right tier → orchestration: reach for the next lever only when the cheaper one is exhausted.

Measured caveat (Haiku × trivial/easy SWE tasks, n=3, 2026-06): the effort dial moved neither correctness nor cost. Effort bites on reasoning-bound work; where a weak model's cost is dominated by tool turns, right-size the tools, not the thinking.

The default pattern: plan strong, execute cheap, escalate on failure

These three steps are a Thinker → Worker → Verifier division of labor. Reserve the strong tier for the Thinker step; a cheap tier handles Worker (execute) and Verifier (check).

1. Plan with the strong tier. Decompose the task into sub-tasks each small enough to hand off with an unambiguous spec: the file(s), the exact change, and a verifiable success check. A vague hand-off ("make it work") forces the executor to re-plan and burns the savings — a precise one (a written diff intent + the command that proves it) lets a cheap model finish independently. Pair this with answer-shapes so hand-offs are parseable.
2. Execute with the cheapest capable tier. Delegate each well-specified sub-task to a subagent (the Agent/Task tool, or a cheaper /model). Subagents also isolate context — the executor's intermediate reads never enter the planner's window, which is itself a large token saving on long tasks. If your config ships subagent roles (e.g. implementer/doc-writer/verifier/researcher), delegate to them instead of hand-authoring the boilerplate.
3. Verify, then escalate only on failure. Check the result (verification-before-completion). If a cheap tier fails the check twice, escalate that sub-task one tier up — do not loop a weak model on a problem above its weight. Escalation is the exception, not the plan.

Delegation protocol

If your config ships pinned subagent roles (implementer / doc-writer / verifier / researcher), delegate to them instead of hand-authoring the boilerplate, and follow the six field-tested delegation rules — plan files with hard acceptance criteria, per-wave verification, diff-checking cheap-tier claims, structured statuses — in references/delegation-protocol.md.

When NOT to orchestrate

Orchestration has real overhead — a planning round, hand-off tokens, subagent spin-up. A single capable model in one context often wins. Stay single-model when:

• The whole task is one or two steps, or fits comfortably in one context.
• The work is exploratory/ambiguous throughout (no stable spec to hand off) — keep it with the strong tier rather than thrashing a cheap one.
• Splitting would duplicate context across agents that all need the same large file set.
• The task value can't justify the overhead. A single agent can use ~4× the tokens of a chat and a multi-agent run ~15× — reserve fan-out for high-value, parallelizable tasks, not routine edits (figures: Anthropic's multi-agent research system write-up).

Rule of thumb: orchestrate when the task fans out into independent, well-specified pieces. If you can't write the sub-task spec in two sentences, it isn't ready to delegate.

Token economy (independent of tiering)

These cut the bill whatever model you are on — apply them first; they often remove the need to orchestrate at all:

• Cache the stable prefix. Keep instructions/system/context order stable so the prompt cache hits. See prompt-caching-playbook.
• Isolate context in subagents. Hand a subagent only what its sub-task needs; return a short structured result, not the transcript.
• Retrieve, don't stuff. Pull the relevant code via structural/search tooling (AST-aware search, code-graph navigation) instead of pasting whole files into context.
• Structured hand-offs. answer-shapes skeletons make a cheap model's output complete and an orchestrator's parsing trivial — fewer correction round-trips.
• Write less code. The cheapest tokens are the ones never generated — enforce a do-less decision ladder before implementing.

Composes with

Anti-patterns

• Spawning subagents for a task that fits in one context (overhead > savings).
• Delegating an under-specified sub-task, then paying to re-plan it.
• Looping a cheap model on a problem above its tier instead of escalating once.
• Using a frontier model for bulk mechanical edits a cheap tier would nail.
• Splitting a task so finely that every subagent re-reads the same large files.