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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsChoose Claude Haiku 5.5 when you need many fast, low-cost responses to clearly defined tasks—such as classification, extraction, routing, summarization, or live support—and can check whether its answers are good enough. Choose Sonnet 5.5 when a well-scoped task needs a stronger speed-and-capability balance, and Opus 5.5 when complex coding or knowledge work calls for Anthropic’s highest-capability tier. These are Anthropic’s recommendations, not a guarantee that Haiku will meet your quality or latency targets; validate it on examples from your workload.
What Haiku is best suited to
Anthropic describes Haiku 5.5 as the fastest model in its current Claude lineup and recommends it when speed and volume matter most. That points to repeated, bounded requests where each response is relatively easy to judge and the cost or delay of using a larger model across many calls would matter.
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Anthropic lists these use cases for Haiku 5.5: summarization, classification, routing, text generation, real-time chat or voice, live support, subagent work, repetitive browser or desktop tasks, and focused simple coding. The common thread is not that these tasks are trivial; it is that a well-defined task can be repeated and evaluated. For example, a support system might classify incoming messages or draft a first response, while a human or another process handles uncertain cases.
When a larger Claude model is the better fit
Use the task’s reasoning demands and the cost of a mistake—not model size alone—to decide whether Haiku is appropriate. Anthropic positions Sonnet 5.5 as a fit for well-scoped work that needs a balance of speed and intelligence, and Opus 5.5 for complex coding and knowledge work. Its overview labels Haiku fastest, Sonnet fast, and Opus moderate in comparative latency; those labels are relative descriptions, not latency guarantees for a particular application or deployment.
| Model | Consider it when… | Anthropic’s relative latency label |
|---|---|---|
| Haiku 5.5 | Many clearly scoped calls, including classification, extraction, routing, summaries, live support, repetitive computer use, subagents, or focused simple code. | Fastest |
| Sonnet 5.5 | The task is well-scoped but benefits from a stronger speed-and-capability balance. | Fast |
| Opus 5.5 | Complex coding or knowledge work calls for the higher-capability tier. | Moderate |
These distinctions reflect Anthropic’s own guidance; they do not show that Haiku is sufficient for every task in the list. A task with ambiguous inputs, several dependent reasoning steps, or high consequences for an incorrect answer deserves a direct quality comparison with Sonnet or Opus.
How much the API rates differ
Anthropic’s published API rates, accessed October 7, 2026, show a substantial price difference for prompts up to 100K tokens. Rates are per million tokens and vary by model and whether tokens are input or output.
| Model | Input, prompts up to 100K | Output, prompts up to 100K |
|---|---|---|
| Haiku 5.5 | $0.10 per million tokens | $0.50 per million tokens |
| Sonnet 5.5 | $2 per million tokens | $10 per million tokens |
| Opus 5.5 | $4 per million tokens | $20 per million tokens |
For Haiku 5.5 prompts above 100K tokens, Anthropic lists a separate rate of $0.50 per million input tokens and $2.50 per million output tokens. See Anthropic’s pricing page before estimating spend: rates can change, and the applicable terms may differ by service route. If you use Bedrock or Google Cloud, check that provider’s pricing as well. For a realistic estimate, count both input and output tokens at the tier that applies to your prompt length rather than comparing only the input rate.
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A practical way to decide
- Describe the workload. Record the request volume, typical prompt size, required response time, expected output, and the consequence of a wrong or incomplete answer.
- Try Haiku on the bounded, high-volume work. Start with the kinds of tasks Anthropic documents, and use representative inputs rather than only easy examples.
- Compare against a larger model where the task demands it. Test Sonnet or Opus on the same sample if reasoning is complex, inputs are ambiguous, or errors are costly. Judge quality against your actual acceptance criteria, not a general impression.
- Estimate the full token bill. Apply the current input and output rates, account for Haiku’s separate tier above 100K tokens, and include the pricing terms for the platform through which you access the model.
- Route uncertain cases to review or escalation. If evaluation shows that Haiku misses your quality target on a subset of requests, keep a way to send those cases to a larger model or a human reviewer.
This workflow is a practical way to apply Anthropic’s model guidance; it is not a claim that Anthropic mandates a particular routing design. Actual response time and output quality depend on the workload and deployment, so the model’s relative speed label should not replace testing against your own requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check the model version before implementing
Model names and availability change. Anthropic’s lifecycle documentation lists Haiku 5.5 and Haiku 4.5 as active, and Haiku 3 and Haiku 3.5 as retired; it names Haiku 4.5 as their replacement. Check the current lifecycle page and the model ID you intend to call before implementing or migrating an integration.
Keep older-version claims attached to the version they describe. Anthropic’s October 15, 2025 announcement reported 73.3% on SWE-bench Verified for Haiku 4.5; that is a vendor-published figure about Haiku 4.5, not a result for Haiku 5.5 or an independent validation. It should not be used to predict how Haiku 5.5 will perform on your coding tasks.
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