Haiku 5.5 Lowers Token Costs, but SEO Automation Still Needs a Review Budget

The cheapest part of an automated SEO task can be generating its answer. Reviewing a wrong redirect, repairing a misleading title, or recovering an accidentally changed page can cost much more. Claude Haiku 5.5’s pricing makes narrow classification work cheaper to consider, but the release does not eliminate that second budget.

Anthropic announced Haiku 5.5 on October 7 with model ID claude-haiku-5-5 and adjustable effort. Direct API input and output rates are $0.10 and $0.50 per million tokens for prompts up to 100,000 tokens, rising to $0.50 and $2.50 above that threshold. Cache read/write rates are separate. Anthropic lists availability through its platform, AWS, Google Cloud, and Azure; provider-specific access and regions still require checking.

A small batch, calculated rather than tested

Suppose an SEO team classifies 1,000 query records. Each request uses 1,000 uncached input tokens and produces 200 output tokens. Every prompt is below the stated length threshold. The batch therefore uses one million input tokens and 200,000 output tokens.

Input: 1,000,000 / 1,000,000 × $0.10 = $0.10
Output: 200,000 / 1,000,000 × $0.50 = $0.10
Illustrative direct token cost: $0.20.

This is arithmetic using the announced rates, not a measured bill. It excludes tool calls, retrieval, cache writes, retries, hosting, taxes, and human work. Actual tokenization and output length can differ. A million tokens across a batch does not make every individual prompt a million-token request; the price tier applies to prompt length, not cumulative monthly usage.

A proposed label is a lower-risk output than a live edit

Query-intent classification is a reasonable evaluation candidate because the output can be reviewed without changing the website. Ask for a label, the supporting phrase, and an explicit uncertain state. If the query “apple repair near me” lacks enough context to resolve a proposed taxonomy, uncertainty is more useful than a confidently specific label.

Move up the risk ladder cautiously. Drafting a meta description introduces factual and commercial claims. Recommending consolidation changes the page strategy. Applying a redirect changes access to the URL. The same model can participate in all three workflows, but the authority granted to it should differ across them.

Use an escalation rule that names the consequence

• Keep for sampled review: low-impact topic labels that cite the supplied text and do not trigger site changes.
• Send for individual review: ambiguous intent, conflicting evidence, unsupported numbers, or text containing claims beyond the supplied record.
• Require explicit approval: redirects, canonical changes, deletion, indexability settings, and publication.
• Stop the batch: a parser failure, unexpected schema, or repeated unsupported outputs that make downstream actions unreliable.

These are proposed operating rules, not demonstrated Haiku error rates. A model’s confidence wording cannot substitute for a checked reference set. Before scaling, use examples that include ambiguity, brand names, mixed intent, and missing context. Count wrong confident outputs separately from useful abstentions.

Price a finished decision

An inexpensive response that requires repeated correction may cost more per usable result than a slower response that can be checked quickly. Track direct inference, retry cost, and reviewer effort separately. When a reviewer needs to open source pages, that work should be included in the workflow budget even if the model call costs a fraction of a cent.

Our Perplexity Search API analysis covers retrieval charges, which should not be hidden inside a token estimate. The AI keyword research workflow shows how labels become page decisions. Use the content audit checklist before releasing public copy. No SEO classification benchmark has been run for Haiku 5.5 in this report, so the release supports a cost hypothesis, not a claim of task accuracy.