claude-haiku
Claude Haiku — Fast/Cheap Tier
Concept of the skill
What it is: claude-haiku is the routing decision for a model provider's fast, cheapest tier — the floor of the roster, used for work where throughput, latency, and cost dominate and deep reasoning is not the binding constraint.
Mental model: A model roster is a tiered ladder, and just below the bottom model rung sits "no model at all — a script." The fast tier is the rung you take when a task needs a little judgment (more than a regex) but not reasoning depth, and runs often or fast enough that a higher tier's cost and latency are wasteful.
Why it exists: The expensive failure mode is using a premium reasoning model for mechanical work — transcription, polling, slot-filling — where it adds cost and latency with zero quality gain. An explicit fast tier gives that work a correct home: cheaper and faster than the implementation lane, while still being a model when a deterministic script can't quite do the job.
What it is NOT: It is not the lane for multi-step synthesis, architecture, or hard debugging — those need a higher tier's reasoning and tunable depth. It is not a universal cost-cutter either: routing a task that needs reasoning to the fast tier to "save money" produces wrong answers, which is the most expensive outcome of all.
Adjacent concepts: the balanced implementation tier (the escalation target for ordinary multi-step work); the frontier reasoning tier (the escalation target for the hardest work); a deterministic script (the drop target below any model for fully repeatable work); cost-aware delegation (the policy that routes mechanical work down to here or to a script).
One-line analogy: The fast tier is the quick, low-cost assistant for high-volume routine paperwork — perfect for the form-filling that floods the inbox, the wrong choice for the case that needs an analyst's judgment.
Common misconception: That the cheapest model is the safe way to cut costs across the board. It is not — it is the right choice only for work that does not need reasoning depth or large context; pushing reasoning-heavy or large-context work down to it trades a small token saving for wrong answers and re-work.