Haiku 5.5 targets inexpensive, high-volume work. Budget by prompt-length tier and accepted task, not a blanket percentage saving or a model-ranking headline.
The price depends on prompt length
Anthropic announced Haiku 5.5 on October 7. Published prices per million tokens are $0.10 input and $0.50 output for prompts up to 100,000 tokens, versus $0.50 and $2.50 above that boundary. Cache reads are $0.01/$0.05 and cache writes $0.125/$0.625 for those tiers. The provider identifies claude-haiku-5-5 for its platform and lists cloud-provider availability.
A small request can still need a total-cost model
Our worked estimate: 10,000 requests with 1,000 input and 200 output tokens each consume 10 million input and 2 million output tokens. At the lower-tier standard rates, that is $2 before caching, tools, retries and other charges. This is arithmetic, not a billing test. Save actual usage totals when experimenting; a request that needs several repairs may cost more than the first successful-looking response suggests.
Choose a bounded classification task first
Our suggested trial is support-ticket routing into five predefined queues, with an explicit unknown category. Include ambiguous tickets, irrelevant messages and examples that should be escalated. Review false routing separately from harmless wording differences. A useful automation sends uncertain cases to a person rather than forcing every input into a confident label. Count how much review time remains; a cheap token rate does not make incorrect routing inexpensive.
Keep long context from becoming a surprise
Before sending a large history, count the complete prompt rather than only the newest user sentence. Our recommendation is to store a compact, reviewable summary of established decisions and retrieve the relevant evidence for each task. Do not omit information essential to the classification merely to reduce tokens. Compare the shorter and longer prompt on identical examples, then inspect which disagreements affect the outcome and whether either variant needs more correction.
Escalation is a workflow decision
A sensible starting policy uses the small model for a narrowly defined task and a stronger model or human reviewer for exceptions. Define the escalation rule before measuring savings, including missing evidence and invalid output. We have not benchmarked Haiku. Vendor speed and capability descriptions are not a guarantee on your workload. Pair this cost worksheet with the weekly model roundup, which compares release types rather than declaring one universal winner. For the ticket-routing trial, include a message that mentions two departments and one that provides no actionable information. Decide in advance whether these belong in an exception queue. That small policy choice prevents a superficially complete classification table from hiding ambiguity.
Frequently asked questions
What should I include in a Haiku budget?
Count input, output, cache operations, retries, tools and review time. Separate estimates from actual provider billing.
Why is the 100K boundary important?
Anthropic publishes different rates above that prompt length; a long history can change the applicable tier.
What is a useful first evaluation?
Try a bounded classification problem with ambiguous examples, an unknown category and human review of costly mistakes.
Sources & further reading
Primary sources checked Oct 11, 2026. Vendor statements are attributed; editorial advice is our own.
- 1Introducing Claude Haiku 5.5 ↗Anthropic
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