$1 Trial Budget: Claude Haiku 4.5 as Your Default

01 / Make $1 a test budget, not a performance promise

Claude Haiku 4.5 is worth testing as your default for short, clearly defined jobs: extracting fields, labeling messages, rewriting paragraphs, and answering questions from supplied text. A default handles your routine traffic. You can still move a difficult request to a more capable model when the result needs it.

Start with a $1 planning budget. That is a suggested experiment limit, not a quoted package price, minimum deposit, or promise about how many requests you will get. Your actual spend depends on current rates, input length, output length, and any billable cache activity.

02 / Pick three jobs you can check yourself

A tiny budget goes further when success is easy to recognize. Choose tasks you already understand, so you can judge the answers without paying another model to review them.

• ■ Extract an invoice number, date, and total from pasted text. Check every value against the original.
• ■ Classify a support message into one of four categories you define. Include an unclear message to test whether it guesses.
• ■ Rewrite a short email in under 100 words while preserving every date, price, and commitment.

These are useful starting points because the source material and expected behavior fit inside a compact prompt. Haiku is enough when its answers consistently meet your requirements with little correction. Fluency alone does not establish that.

03 / Connect once, then send one small request

CheapRelay offers pay-as-you-go access with no monthly fee or subscription. Sign up for CheapRelay, create an API key, and check the current Claude Haiku 4.5 rates before testing.

If you use an OpenAI SDK, set its base URL to https://api.cheapapirelay.com/v1 and supply your relay key. Copy the exact model identifier from the service's current model listing; a display name is not always the identifier an API accepts. Keep the key in an environment variable rather than in source code.

Send one short request first. Ask it to extract three fields from a few lines of sample text. Confirm that authentication works, the response arrives, and usage is visible before you connect a larger workflow.

04 / Run 20 checks before making Haiku the default

Build a small evaluation set: six examples for each of your three jobs, plus two messy examples. That gives you 20 checks. Include missing information, an ambiguous category, or an email containing several dates. Use synthetic examples if your real data is sensitive.

Give each task a precise instruction. For extraction, say: Return only the invoice number, date, and total. Use null for missing values. Do not infer missing information. For classification, list the permitted categories and explain when to choose an uncertain category.

Record whether each response passes, needs a minor edit, or fails. Set your acceptance rule before looking at results. For example, you might require every invoice total to be correct while allowing small wording edits in emails. This is your workflow test, not a general benchmark.

05 / Count the cost of a usable answer

CheapRelay bills real tokens, including cache reads and writes. Calculate cost from the applicable token categories and current rates. If a rate is quoted per million tokens, multiply the relevant token count by that rate and divide by 1,000,000, then add the billable categories.

Track total spending alongside successful results. Divide the full experiment cost, including retries, by the number of usable answers. A request that needs three retries consumes more of your budget than its first response suggests.

For a $1 experiment, use $0.80 as a manual review point and reserve $0.20 for follow-up checks. These are planning amounts. A note in your spreadsheet does not enforce a billing cap; use any available spending controls and keep requests small.

06 / Escalate when correction becomes the real expense

Keep Haiku as the default for tasks that pass your checks. Review or escalate requests involving conflicting instructions, reasoning across many documents, unfamiliar code, or consequential decisions. First fix unclear prompts and missing context. If the same failures persist, test a more capable model on those specific cases.

Recheck a few examples whenever your prompt or source material changes. Your default earns its place by producing usable work at an acceptable measured cost.