The problem: one expensive model, default settings, everything
Most companies we walk into run every task, from renaming a variable to designing a system, on the most expensive model at default settings. That is the whole failure mode. Frontier models cost several times more per token than mid-tier ones, agentic sessions multiply token use, and the meter runs whether the task needed the intelligence or not. The five rules below fix it in order of leverage.
1. Extract the method into a skill (you keep the process, not the intelligence)
The biggest waste is not the price per token; it is using an expensive model to supply discipline a cheap model could follow if someone wrote it down. So write it down. We distilled the working discipline of a frontier model into a reusable Claude skill: five gates, in order, for any hard task.
- Scope. Define done, and how you will check it, before touching anything.
- Evidence. Open the real file or data. Never design from memory.
- Attack. Try to break your own answer before committing to it.
- Verify."It ran" is not verification. Check the actual output.
- Report. Lead with the answer. Separate verified from assumed.
Load that skill into Sonnet or Opus and the output gets noticeably sharper. You cannot transfer the intelligence between models. You can transfer the method, and the method is most of what you were paying for on routine work. (New to skills? Our guides to the top Claude skills for UI/UX engineers and for DevOps engineers cover how they work and how to install them safely.)
2. Route every task: the right model, not the best model
A routing table is one decision made once, instead of the wrong decision made fifty times a day. Ours, as of July 2026:
Frontier model (Fable 5): planning, orchestration and review of high-stakes work. Never the workhorse.
Opus, high effort: the architect. Design decisions and hard debugging only.
Sonnet, medium effort:the daily workhorse. It now matches last month's Opus at 40% of the price.
Haiku: mechanical scans, admin, classification. Twice the speed, a fifth of the cost.
The Artificial Analysis price-to-intelligence data makes this blunt: Sonnet sits alone in the most attractive quadrant, and every older Opus is now dominated, meaning the same price buys less intelligence. If your default model is anything other than the mid-tier workhorse, you are paying flagship prices for commodity work.
3. Escalate the model, not the effort dial
The counterintuitive finding worth stealing: on the hardest problems in the FrontierCode benchmark, cranking a mid-tier model to maximum thinking effort made it worse, not better. Measured overthinking, not vibes. Extra effort only converted to accuracy on the frontier model. Effort is also a second cost dial: higher settings multiply both token burn and latency.
So the rule is simple. When a task is genuinely hard, escalate the model. Do not max the effort dial on a cheaper model and hope. The tell that you have it backwards: if you keep being tempted to push the mid-tier model's effort higher, that is the signal the task belongs a tier up.
4. Frontier as architect, cheap models as workers
We tested the whole setup live on a real job. The frontier model scoped the work and dispatched two subagents: a Sonnet worker and a Haiku scout. Then it verified their output. Both workers did good work. Both also made small errors that the verification pass caught. The run came out roughly 70% cheaper than doing everything on the frontier model, with no loss in the shipped result.
Worker output is a draft, not a fact, until the orchestrator checks it. The savings come from the cheap models doing the volume; the quality comes from the expensive model doing the checking. Skip the verify pass and you have not saved money, you have deferred the cost to production.
5. Treat your usage limit as a budget
If you are on a subscription rather than API billing, all of this still applies, and arguably more. A weekly usage limit is a budget whether or not a card is attached. Routing decides whether it buys you four days of work or one. Spend the frontier allowance on frontier-worthy work: planning, review, the decisions that are expensive to get wrong. Let the workhorse models eat the volume.
A routing setup like this typically cuts spend by well over half with no quality loss on routine work. The models will keep changing; the prices in the table above will be stale in a quarter. The process is yours to keep.
The short version
Write the discipline down as a skill so cheap models run a frontier process. Route every task to the cheapest model that can do it. When a task is hard, escalate the model, not the effort dial. Let the frontier model plan and verify while workers do the volume. And spend your usage limit like it is money, because it is. Most companies run everything on one expensive model at default settings. Don't be most companies.
Sources
- Artificial Analysis (July 2026). Anthropic model comparison: intelligence vs price. artificialanalysis.ai/providers/anthropic
- FrontierCode benchmark (2026). Accuracy vs cost per task across effort settings, hardest-50 diamond subset.
- Anthropic. Claude Code skills documentation. code.claude.com/docs/en/skills
- Summone field test (July 2026): live architect-and-workers run, frontier orchestrator with Sonnet worker and Haiku scout; cost and error figures observed on a real client job.