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Match your situation to a row.
| Your situation | Do this | Where |
|---|---|---|
| Any workload, any model | Turn on prompt caching and trim unneeded tokens; both are free | Cache repeated context · Trim tokens |
| A person waits between turns | Use the 1-hour cache duration once about 1 turn in 20 follows a pause between 5 minutes and an hour and few gaps run over an hour. On Claude Fable 5.1, keep the 5-minute cache warm while pauses run minutes, and buy the 1-hour duration when pauses run toward an hour | Pick the cache duration |
| Costs are too high; quality is fine | Sweep effort down on your current model | Tune effort |
| You are not on the latest model | Upgrade; the current model solves more tasks, at a cost per solved task from about 40% lower to about 20% higher | Upgrade the model |
| You are choosing or switching models | Compare on cost per completed task, not per token | Compare models |
| Quality isn't good enough | If you lowered effort, restore it; otherwise try the next tier up at low effort | Tune effort · Compare models |
Attempts end with stop_reason: max_tokens | Raise max_tokens; 64,000 covered all but 2 of 14,000 turns measured at the default effort, and 128,000 cost nothing extra per solved task | Set budgets |
| You can check outputs (tests, a verifier) | Run everything at low effort and re-run failures at the default (high); on the coding benchmark measured, the pass rate held at about half the cost | Re-run failures |
| Agent loops with a few very costly runs | Set a task budget (beta; check the support table for which models), a Claude Managed Agents session budget, and a workspace spend limit | Set budgets |
| A lower-cost model stalls only on hard decisions | Add a frontier advisor. It pays off when priced well above the executor and actually consulted, so first price the advisor's model alone at low effort and measure the consult rate | Advisor strategy |
| The work exceeds one context window | Delegate partitions to cheaper workers | Orchestrator strategy |
These results are Anthropic-internal (Benchmarks referenced) and directional, not guarantees, so measure on your own workload with the four-step method.