A cheaper small model needs task-specific error budgets before high-volume rollout

Anthropic released Claude Haiku 5.5 on 7 October, positioning it for classification, extraction, routing, live support and subagent work. For prompts under 100,000 tokens, the listed price is $0.10 per million input tokens and $0.50 per million output tokens. Reuters reported the same task focus and Anthropic’s claim that average run cost is 75% lower than Haiku 4.5.

The release supplies vendor benchmarks and prices, not production error rates for a buyer’s data. High-volume adoption magnifies denominator risk: a 1% routing error is operationally different at one hundred and one million cases.

Set a budget for each failure mode

Before replacing a larger model, define the acceptable false-positive, false-negative, abstention and escalation rates for each task. Sample real inputs across language, length, role and sensitive categories. Measure the whole pipeline, including retrieval, tool calls and post-processing, rather than the model in isolation.

Route ambiguous or high-impact cases to a stronger model or human reviewer and price that fallback into the comparison. A cheap first pass can still be expensive if it creates rework, customer contacts or missed records. Keep a pinned model version, a drift sample and a rollback threshold.

The counterargument is that this removes the speed advantage. It need not: most low-risk cases can remain automated when the exception path is explicit. The procurement decision should compare cost per correctly completed task, including review and remediation—not token price alone.

Define the budget before seeing the candidate model’s results, otherwise the threshold will drift toward the preferred price. Report confidence intervals and the number of cases in each subgroup. For rare but severe failures, use targeted challenge sets rather than assuming a random sample will contain enough examples. Re-run those sets whenever the provider changes the model alias or routing layer.