Claude Haiku 5.5: Features, Pricing and Use Cases for Developers

Anthropic released Claude Haiku 5.5 on 7 October 2026, and it changes what a “small” model can do. It keeps the speed and low price the Haiku line is known for, but adds a 1M-token context window and up to 128K output tokens.

If you run high-volume jobs such as classification, routing, data extraction or test-log triage, this is the model to look at first. It also comes with breaking API changes, so moving from Haiku 4.5 is not a simple name swap.

What is Claude Haiku 5.5?

Claude Haiku 5.5 is the small, fast tier of the Claude 5.5 family. Anthropic describes it in its official Haiku 5.5 documentation as built for high-volume, latency-sensitive work. Larger models handle deep reasoning; Haiku handles the many simple or medium-difficulty calls where cost and speed matter most.

Anthropic has committed to keeping the model available until at least 7 October 2027, which gives teams a full year of stability.

Claude Haiku 5.5 pricing

Base rates and the long-prompt surcharge

The base rate is $0.10 per million input tokens and $0.50 per million output tokens, for prompts up to 100K tokens. Past 100K tokens, a long-context rate of five times the base applies to every token in that request. Cache reads cost far less than normal input.

A quick cost example

Classifying 1,000,000 support tickets of about 400 input tokens and a 20-token label is 400M input and 20M output tokens. At base rates that is roughly $40 for input and $10 for output, about $50 in total. The same job on a larger model can cost many times more.

Breaking API changes you must handle

Sampling parameters and prefill

If your code sets temperature, top_p or top_k, the API returns a 400 error on Haiku 5.5. Prefilling the assistant turn also errors. Control behaviour through clear prompt instructions and the new effort parameter instead.

A minimal working call

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=1024,
    messages=[
        {"role": "user",
         "content": "Classify this ticket as bug, feature or question: 'Login button does nothing on Safari.'"}
    ],
)
print(response.content[0].text)

There is no temperature and no prefilled assistant message. Run your existing prompts through a test set before switching production traffic, because default adaptive thinking can change tone and length.

Best use cases for Claude Haiku 5.5

Classification, routing and extraction

These tasks need consistent answers at scale. Haiku 5.5 can tag tickets, route emails, pull fields from invoices and clean messy data cheaply. The 1M window also lets you pass a large rulebook or many examples in one request.

Sub-agents in agentic workflows

In multi-agent systems a stronger model plans while small models do the repetitive steps. Haiku 5.5 suits the worker role: searching, summarising, validating and calling tools. See our guides on AI agents best practices and the stateless MCP architecture.

Testing and QA workflows

QA teams produce huge volumes of logs and reports. A fast, cheap model can sort failures into product bug, flaky test or environment issue, and draft first-pass bug reports. This builds on AI-powered test automation and Playwright MCP. Keep a human reviewing anything that blocks a release.

Fast coding help

For quick edits, explanations and lint-style review, speed matters more than depth. Teams using AI coding tools, as described in vibe coding in 2026, can send simple requests to Haiku and keep larger models for design work.

Haiku 5.5 vs bigger models

• Choose Haiku when requests are simple and numerous, latency affects users, or budget per request is tight.
• Choose a larger model for deep multi-step reasoning, costly mistakes (legal, financial, security) or open-ended writing and architecture.
• Use a router: send everything to Haiku first and escalate only low-confidence cases.

Also deciding between prompting, retrieval and tuning? Read RAG vs fine-tuning vs prompt engineering.

Migration checklist from Haiku 4.5

1. Update the model ID to claude-haiku-5-5.
2. Remove temperature, top_p and top_k.
3. Replace assistant prefill with explicit format instructions.
4. Test the effort parameter to balance cost and quality.
5. Re-run your evaluation set and compare accuracy, latency and cost.
6. Roll out gradually with a fallback to the previous model.

Final thoughts

Claude Haiku 5.5 is a strong default wherever volume and speed matter. The 1M context and 128K output open jobs that once needed a larger model, but the removed sampling controls mean older code must change first. Start with one low-risk workflow, measure it against your current model, then expand. Check Anthropic’s documentation for the latest limits and prices.

Frequently asked questions

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