| name | model-routing |
| description | Route each coding task to the cheapest Claude model that can do it well, and escalate only on failure. This is the cost lever ponytail-style rulesets ignore. |
Model routing, the bigger cost lever
Right-sizing code cuts output tokens. Routing the task to the cheapest capable
model cuts the price-per-token underneath them, and that's the larger win. A
ruleset that saves 50% of output tokens on Opus is still paying 5× the price of
Haiku for the same tokens.
The price reality (per 1M tokens, as of 2026-06)
| Model | ID | Input | Output | Use for |
|---|
| Haiku 4.5 | claude-haiku-4-5 | $1 | $5 | trivial / mechanical |
| Sonnet 4.6 | claude-sonnet-4-6 | $3 | $15 | standard single-file work |
| Opus 4.8 | claude-opus-4-8 | $5 | $25 | hard / ambiguous / multi-file |
Output is 5× input everywhere, and Opus output is 5× Haiku output. So the two
biggest levers, in order, are: (1) pick the cheapest capable model, (2) emit
fewer output tokens. Occam's core ruleset does (2); this skill does (1).
Classify the task, then route
Route on the hardest sub-step the task contains, not its surface size.
Haiku 4.5, trivial / mechanical. No design decisions; the answer is
mechanical once you read the code.
- rename / reformat / mechanical refactor across known sites
- stdlib glue, one-liners, regex, simple data transforms
- writing an obvious test for existing, readable logic
Sonnet 4.6, standard. One file, clear requirement, a real but bounded
decision.
- a single-file feature with a stated spec
- a bug fix with a clear repro
- straightforward API/CLI wiring
Opus 4.8, hard. Spans files, the requirement is ambiguous, or a wrong
answer is expensive or hard to detect.
- multi-file features / refactors that cross module boundaries
- concurrency, security boundaries, data-loss-adjacent code
- "figure out why X" debugging with no clean repro
- anything where under-engineering would be silently wrong
Escalate, don't gamble
Start one tier below your first instinct and escalate on a concrete signal,
it's cheaper to retry on a bigger model than to default everything to Opus.
Escalate when any of these fire:
- the runnable check (see core ruleset) fails twice
- the model self-reports low confidence or says "this is ambiguous"
- the change turned out to touch more files than the tier's profile allows
- a right-sizing check (inputs / scale / failure / concurrency) is genuinely in play
De-escalate the next task if the current one finished comfortably below tier.
Don't break the cache when you route
Switching models mid-conversation invalidates the prompt cache (caches are
model-scoped, see caching/NOTES.md). So:
- Route per task / per session, not per turn inside one session.
- For a cheap sub-step inside an Opus session, spawn a subagent on Haiku
rather than switching the main loop's model. The main loop keeps its cache;
the subagent pays only its own (small) prefill.
One-line heuristic
Default to Sonnet. Drop to Haiku when the task is mechanical. Reach for Opus
only when a wrong answer is expensive or the problem spans files. Escalate on
a failed check, never on a hunch.
