Guides Anthropic published oct 7, 2026

How to Use Claude Haiku 5.5 in Your Workflows

beginner

The Rundown

Give Haiku a clear, repeatable job. Check its output, then send the harder cases to Sonnet, Opus, or a person. That is the workflow we would test before moving a whole task to a cheaper model.

Anthropic released Claude Haiku 5.5 on October 7, 2026. It targets tasks such as sorting messages, pulling facts from documents, and preparing short summaries. Anthropic also recommends it as a helper alongside Sonnet and Opus.

In this guide, you will use Haiku to sort customer messages, then have Sonnet review the exceptions. The exercise uses Claude Code, but the task involves plain text rather than building an app. Nothing sends a reply, issues a refund, or changes an account.

University Pro members get the prepared Haiku helper, 20 fictional messages, prompts, an answer key, and an offline output checker. The public walkthrough includes eight messages you can use without a download.

Where Haiku fits in a workflow

Choose tasks with short inputs, clear rules, and results you can check. Here are five starting points we would test:

WorkflowGive Haiku this partKeep this part for deeper review
Customer supportSuggest a category and quote the relevant message.Refund requests, account changes, security concerns, and unclear cases.
ResearchExtract a named fact from a supplied source, with its location.Resolve conflicting sources and write the recommendation.
MarketingTag feedback by a fixed list of themes.Choose the campaign, check claims, and approve the copy.
CodingFind relevant files or summarize a test log.Design a change, diagnose a hard bug, and approve a release.
Recurring reportsPull stated figures and dates from short updates.Explain what changed and decide what to do.

These are proposed uses, not measured results. Anthropic positions Haiku for narrow, repeated work and its larger models for complex coding and longer tasks.

Use ordinary code for exact checks. Counting rows, checking required fields, and adding known numbers rarely need another model call. A post-edit hook can run a fixed test without adding another model call.

Haiku vs Sonnet vs Opus: what changes?

Haiku 5.5 has a 1 million-token context window and supports adjustable thinking effort. A token is a small unit of text the model processes. Start this exercise at medium effort, then compare low only after you have a correct result to check against. Anthropic warns that lower effort can miss steps in longer tasks.

Here are the standard Claude API rates, in US dollars per million tokens:

ModelInputOutput
Haiku 5.5, prompts up to 100,000 tokens$0.10$0.50
Haiku 5.5, prompts over 100,000 tokens$0.50$2.50
Haiku 4.5$1.00$5.00
Sonnet 5.5$2.00$10.00
Opus 5.5$4.00$20.00

These rates exclude caching, tools, and other adjustments. The higher Haiku band depends on the prompt length for each request and raises both input and output rates. A large context window does not make long inputs cost the same.

Keep API prices separate from your Claude subscription. In Claude Code, the account you use determines whether work draws from a plan allowance or metered billing. A lower token rate does not reduce your fixed monthly subscription price.

What the cost difference could look like

Here is a hypothetical API example, separate from the eight-message exercise:

1,000 requests, each with 2,000 uncached input tokens and 200 total billed output tokens. Every request stays within Haiku's lower price band.

Model for all 1,000 requestsInput chargeOutput chargeTotal
Haiku 5.5$0.20$0.10$0.30
Sonnet 5.5$4.00$2.00$6.00
Opus 5.5$8.00$4.00$12.00

These are arithmetic examples using the published rates. They hold token counts equal and exclude tools, caching, retries, orchestration, regional premiums, and human review. They are not measured task costs.

If all 1,000 requests use Haiku and 100 also need a Sonnet pass of the same size, the token charges total $0.90. A real handoff may require more context and a longer answer. Include those costs before claiming a saving.

When upgrading from Haiku 4.5, retest actual token use as well as rates: Haiku 5.5's newer tokenizer can count the same text differently.

How to adapt this to your own workflow

Keep the same sequence: define the small task, request source evidence, check the output, and review exceptions.

For research, ask Haiku to extract a date and quote from each supplied source. Give conflicting passages to your main model. For marketing, tag each feedback item before anyone decides which theme deserves a campaign. For coding, use Haiku to locate relevant files, then have the main model inspect those files and run real tests before accepting a change.

If you build an API integration, make required-field and coverage checks part of the application. Send missing, malformed, or flagged output to review. Keep actions such as sending, deleting, purchasing, and changing access behind separate permission checks. A routing label must not become authorization.

Start with drafts on approved data. Measure performance on new examples before allowing the workflow to handle a larger batch.

Learn to test the model choice

Nate Grahek's Intro to AI Evals teaches how to choose real test inputs, define expected answers, and compare quality, speed, and cost. Use those methods to decide which parts of your work Haiku can handle and which need more review.

Watch Intro to AI Evals with University Pro.

The prepared practice kit is available in this guide's Pro resources. Claude access and usage have separate terms. The course teaches evaluation methods, not a dedicated Haiku 5.5 lesson.

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