Claude Sonnet 5.5 and Haiku 5.5: Choosing Faster, Lower-Cost Models for Business AI

The releases

Anthropic introduced Claude Sonnet 5.5 on 28 September 2026. It positions Sonnet 5.5 for well-scoped everyday work, coding fixes and creating documents, presentations and spreadsheets. Anthropic says it is more than 30% faster than Sonnet 5 and can cost up to 30% less for most work in its testing. Its launch page lists base pricing of $2 per million input tokens and $10 per million output tokens.

On 7 October 2026, Anthropic introduced Claude Haiku 5.5 for high-volume, cost-sensitive and latency-sensitive tasks such as summarisation, classification and database queries. Anthropic lists $0.10 input and $0.50 output per million tokens for prompts up to 100,000 tokens, with higher rates above that threshold. These are Anthropic’s published claims and prices as reviewed for this article; MindFish has not benchmarked either model.

Read vendor performance claims carefully

Speed, token price and cost per task are different measurements. A model can use fewer tokens and cost less for a task even when another model has a lower listed price. Retries, longer context, tool calls and human corrections also affect total cost.

Anthropic publishes benchmark results and comparisons, including improvements over earlier models. Benchmarks can help shortlist options, but they do not replace testing on a business’s own documents and decisions. Vendor results should be attributed and qualified, not presented as independent ROI evidence.

Matching models to work

A fast, lower-cost model may suit high-volume classification, extraction or summarisation where outputs follow a clear format and errors are easy to detect. A more capable model may be better for ambiguous, multi-step or high-consequence tasks. Some designs use a simpler model first and escalate unclear cases.

That routing approach is our proposed design pattern, not an automatic feature to assume from these announcements. Escalation needs confidence criteria, review rules and monitoring. A cheap model that frequently sends work back to people may not reduce overall effort.

An illustrative selection test

Imagine classifying incoming service requests into categories and identifying missing information. A business could test a representative, authorised sample with each candidate model. It would compare correct classifications, missing-detail detection, latency, tool use, total usage cost and time spent correcting outputs. This is a suggested evaluation method, not a reported result.

The test should include unusual and ambiguous requests. It should also check whether outputs keep the required format and whether escalation works. The best model is the one that meets the business standard reliably at an acceptable total cost.

Prices and availability change

Anthropic’s Haiku announcement also described a reduction in Sonnet cache-read pricing on 7 October. That shows why pricing assumptions should be checked again before committing to a design. Product availability, regions, limits and terms can also differ by platform.

Avoid locking a workflow to one model without monitoring. Changing a model can alter output style, accuracy and cost, so updates need regression testing.

What this means for Australian businesses

Australian SMBs should evaluate the whole operating cost, including subscriptions, implementation, review time and support. Check provider data-handling terms and whether the intended platform suits the information involved.

Avoid choosing a model only because of a benchmark headline. Use local business examples and the language customers actually use.

Practical next steps

1. Define the task, quality threshold and consequences of an error.
2. Create an authorised test set including difficult exceptions.
3. Measure cost per acceptable result, not token price alone.
4. Escalate uncertain cases to a stronger model or a person.
5. Retest after model, prompt, workflow or pricing changes.

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