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Start with claude-haiku-5-5 when an API workload is high-volume and latency-sensitive—especially classification, extraction, or routing—then compare it with Sonnet, Opus, or Fable using examples from your own task. Anthropic lists Haiku 5.5 as the fastest model in its current lineup, but that relative label does not establish which model will be most accurate, fastest in your application, or cheapest overall.
The practical choice depends on task quality, end-to-end latency, token use (including tool overhead), and how your prompts fit Anthropic’s pricing tiers. The published model details below are from Anthropic’s documentation as accessed October 7, 2026; verify current pricing, availability, and lifecycle status before deployment.
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Which Claude model should you try first?
Use the workload to form a shortlist, not to declare a winner. Anthropic positions Haiku 5.5 for “high-volume, latency-sensitive tasks such as classification, extraction, and routing.” Sonnet is described as a balance of speed and intelligence; Opus for long-running agentic coding and knowledge work; and Fable for demanding reasoning and long-horizon agentic work. These are Anthropic’s descriptions, not independent evaluations of your API task. Anthropic’s models overview labels their relative latency as follows:
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches| Model | Anthropic’s latency label | Published standard input / output price | Workload to include in your evaluation |
|---|---|---|---|
Claude Haiku 5.5 (claude-haiku-5-5) |
Fastest | $0.10 / $0.50 per million tokens for prompts up to 100,000 tokens; $0.50 / $2.50 for prompts over 100,000 tokens | High-volume classification, extraction, and routing |
| Claude Sonnet 5.5 | Fast | $2 / $10 per million input / output tokens | Tasks where you want to compare a speed-and-intelligence balance |
| Claude Opus 5.5 | Moderate | $4 / $20 per million input / output tokens | Long-running agentic coding and knowledge work |
| Claude Fable 5.1 | Slower | $10 / $50 per million input / output tokens | Demanding reasoning and long-horizon agentic work |
Prices and latency labels are Anthropic’s published figures in documentation accessed October 7, 2026, not a workload benchmark. Haiku’s price tier depends on prompt length; the other listed prices are overview comparison rates. See the pricing documentation for current rates and conditions.
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Haiku 5.5: a candidate for fast, repeated API calls
Try Haiku first when a large number of requests must return promptly and the task is bounded enough to evaluate clearly—for example, assigning a category, extracting specified fields, or choosing a route. The exact API model ID is claude-haiku-5-5; use it to distinguish this model from older Haiku generations. Anthropic lists a 1-million-token context window and a maximum output of 128,000 tokens. Those limits describe capacity, not a reason to send unnecessarily large prompts or request lengthy responses.
Sonnet: compare when the task needs more than a simple decision
Include Sonnet when your test cases require a balance of response speed and more involved work. For example, compare it with Haiku on the same extraction task if inputs are messy, fields depend on context, or errors are costly. The model description alone does not prove Sonnet will outperform Haiku on those cases; use the same evaluation set and production constraints for both.
Opus and Fable: candidates for longer or more demanding work
Include Opus for long-running agentic coding or knowledge work, and Fable for demanding reasoning or long-horizon agentic work, when those descriptions match the task. Their published latency labels and higher listed token rates are reasons to measure carefully, not proof that either is necessary—or that a cheaper model cannot do the job.
How do you choose with a representative evaluation?
Run candidate models on realistic requests before selecting one. A useful evaluation checks not just whether the answer sounds plausible, but whether it meets the exact output contract and performs acceptably under the load and tool setup you intend to ship.
- Build a human-reviewed reference set. Include typical requests, edge cases, ambiguous inputs, and examples where a mistake has a meaningful cost. Record expected classifications or extracted values, and decide in advance what counts as a valid result.
- Run the same workload on each candidate. Keep prompts, tool definitions, output constraints, and input examples consistent. Use the candidate model IDs from Anthropic’s overview and account for any model-specific changes needed to make a fair, working integration.
- Score task quality. Measure accuracy or completeness against the reference set, format validity, and the kinds of errors that matter to your application. For structured output, a semantically correct answer in invalid JSON may still fail downstream.
- Measure latency under realistic traffic. Track median (p50) and tail latency with your expected request volume, input sizes, and tool behavior. Anthropic’s “Fastest,” “Fast,” “Moderate,” and “Slower” labels are relative vendor descriptions; they do not tell you the latency your users will see.
- Calculate total operating cost. Count input and output tokens, prompt-length tiers, tool overhead, retries, invalid responses, and any caching or batch processing you actually use. Compare cost per successful, valid task—not only the price of a single model call.
- Choose against explicit thresholds. Select the lowest-cost candidate that meets your quality and latency requirements, or choose a more capable candidate if the cost of errors makes it worthwhile. Keep the evaluation set to retest when prompts, traffic, or model versions change.
How much do long prompts and tool calls cost?
For Haiku 5.5, Anthropic lists $0.10 per million input tokens and $0.50 per million output tokens when the prompt is up to 100,000 tokens. For prompts over 100,000 tokens, the listed rates are $0.50 per million input tokens and $2.50 per million output tokens. The input and output rates both change with the prompt-length tier, so estimate cost from the distribution of actual prompt sizes rather than applying the lower rate to every request.
Tool use adds tokens beyond the visible user message. Anthropic says tool definitions and a model-specific tool-use system prompt contribute to usage; server-side tools can also have usage-based charges. A request with a large schema or repeated tool interactions may therefore cost more than a simple prompt-and-answer estimate suggests. Include those tokens and any retries in your measured cost.
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For work that can run asynchronously, Anthropic’s pricing documentation lists a 50% discount on input and output tokens through the Batch API. Check whether the workflow can tolerate batch processing and confirm the current terms before budgeting; the discount does not make an interactive, latency-sensitive task a batch workload.
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Anthropic lists Haiku 5.5 with a 1-million-token context window and a 128,000-token maximum output. Check that the complete request—including conversation history and tool definitions—fits the model’s context, and request only the output length the application needs. A large context allowance is a capacity limit, not evidence that sending more context improves a result; for Haiku, prompts above 100,000 tokens also fall into a higher published price tier.
Anthropic lists June 2026 as Haiku 5.5’s reliable knowledge and training data cutoff. If a task depends on facts after that cutoff, supply current information in the prompt or use an appropriate retrieval workflow, then evaluate the result. A cutoff date does not determine whether the model can process information you provide in the request.
Quick Recap
What should you verify before making Haiku a production dependency?
- Model lifecycle: Anthropic’s overview lists Haiku 5.5’s retirement as “Not sooner than October 7, 2027.” This is a lower-bound horizon, not a promised retirement date. Check the live model deprecations page; Anthropic says deprecated models remain functional but are no longer recommended.
- Replacement readiness: Anthropic advises developers to test replacement models in their own applications before migration. Its migration guides index includes a Haiku 5.5 guide; follow the current guide rather than assuming another model is a drop-in replacement.
- Platform and region: Anthropic’s overview identifies model IDs for its API and cloud platforms, including Amazon Bedrock, Google Cloud, and Microsoft Foundry. Verify availability, regional and procurement requirements, features, and pricing directly for the platform you plan to use; a listed platform ID does not establish that every route has identical terms.
- Live commercial details: Model identifiers, availability, lifecycle status, and prices can change. Confirm the official overview and pricing page at selection and procurement time.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
