Claude Haiku 5.5: New Small Model, Pricing and Practical Evaluation

Claude Haiku 5.5: what Anthropic announced

Anthropic introduced Claude Haiku 5.5 on October 7, positioning it for high-volume, cost-sensitive work such as summaries, classification and narrowly scoped subagent tasks. The company says it adds an adjustable effort setting and is available through its platform and major cloud providers.

The release page lists input pricing of $0.10 per million tokens and output pricing of $0.50 for prompts up to 100,000 tokens, with higher rates above that threshold. These are vendor-listed API rates at publication, not a subscription price or a guaranteed cost per job. Check current terms before buying.

Anthropic’s announcement provides pricing, availability and evaluation details. Its performance claims are vendor-reported; we have not run a hands-on comparison.

Compare the cost of a completed task

Our analysis: the cheapest request is not necessarily the cheapest useful result. Retries, longer outputs, review time and external tool charges can change the economics. Measure a completed task against your quality requirements.

For example, an illustrative support-routing pilot could use a set of anonymized tickets with known destinations. Include ambiguous requests and cases that should go to a person. Count incorrect routing and unnecessary escalation separately, because their consequences differ.

Record response time, token consumption and how often a reviewer corrects the result. Compare the same examples with your existing workflow. Avoid a broad claim that one model is best for every job based on a single aggregate benchmark.

Keep an inexpensive agent’s permissions limited

A smaller model still needs task-appropriate access. A classification assistant may need to read a ticket without permission to refund a payment or change an account. Separate producing a recommendation from carrying out a consequential action.

• Choose one repeatable task and define success before testing.
• Use representative examples, including failures and edge cases.
• Set a spending limit and inspect retry behavior.
• Keep sensitive records out of the first pilot.
• Provide a clear route to human review when confidence is insufficient.

Read our AI tool evaluation guide for a broader comparison framework and our permissions checklist before connecting business systems. A model upgrade deserves a measured pilot before it becomes a routine workflow.