Which Claude model to choose? Haiku, Sonnet, Opus and Fable

Current Claude models: official API IDs and prices

Prices below are USD per million tokens, standard API input/output, before applicable taxes and extra features. They are not subscription prices. The latency labels are Anthropic’s relative descriptions, not LearnIA timing measurements.

Sources: official model comparison and API pricing.

Haiku’s threshold matters: for prompts over 100,000 tokens, the listed input/output rates become $0.50 / $2.50 per million. Check the Haiku 5.5 specification before estimating long-document workloads.

The current overview lists a 1M-token context window and 128K maximum standard output for these models. Those are capacity limits, not a promise that every application, account or request can use the full amount or that every fact in a large input will be recalled correctly.

Which model fits your task?

The following are candidates to evaluate, not observed performance rankings.

Keep the environment constant when comparing. Changing the model, tools, documents and prompt at once prevents a useful explanation of the result. For coding, read Claude Code best practices; for document work, use the traceable synthesis tutorial.

Calculate API cost before running a batch

For a simple uncached request:

Estimated token cost =
  input tokens / 1,000,000 × input price
  + output tokens / 1,000,000 × output price

Illustrative calculation: 10,000 input tokens and 2,000 output tokens, using the standard prices above:

This is arithmetic, not a measured bill or benchmark. Actual usage can include reasoning tokens, repeated tool interactions, cache operations and other billable features. Repeated attempts also change the total. Confirm the relevant provider’s rates if you use a third-party platform.

The cheapest response can still require expensive correction. For your own workflow, record accepted outputs and human review time alongside API cost; do not declare a lower “cost per successful task” until you have actual results.

Free Claude, paid plans and API access

The current plan comparison lists Haiku and Sonnet on Free, with usage limits. Availability of a family in a plan does not guarantee an unlimited allowance, an exact version in every interface or access to every tool.

Application subscriptions and API usage have different billing mechanisms. Some paid plans now advertise monthly API credits; check eligibility, account linking, activation and the amount actually displayed in your account. “All API calls are always separate” and “my subscription gives unlimited API calls” are both unreliable shortcuts.

With a zero-euro budget, prepare the test cases below, consult the primary documentation and use only an existing free allowance if you choose to try them. Do not enable paid extra usage to follow this article.

A small evaluation set with a worked answer

Here are fictional tasks you can assess without buying benchmark access. Keep the same input and rubric for each candidate.

Worked calculation: 24 × 25 = 600; 600 × 0.75 = 450; 600 − 450 = 150. A correct final number without a checkable calculation does not satisfy a requirement to show the method. A confident invented resolution of the participant count fails the contradictory-source case.

Score each attempt against your predefined criteria: facts correct, evidence traceable, format usable and no action outside the brief. Leave latency, cost and success-rate cells blank until you measure them. These four teaching cases are too small to establish general model superiority.

How to read public benchmarks without overclaiming

A published score applies to a particular task set, version and execution setup. Record the benchmark name, date, model ID, tools, effort settings and who ran the evaluation. A vendor announcement and an independent replication are different kinds of evidence.

Do not turn a coding score into a guarantee of accurate accounting or a general “best AI” label. If two scores use different scaffolds or budgets, present the mismatch instead of computing a misleading winner.

Use our benchmark reading guide for the evaluation checklist and the broader AI model comparison for other providers. This article contains no proprietary LearnIA benchmark scores and no paid model trials.

Practice: write your selection rule

Prepare a model-selection sheet for one fictional task. State the current model IDs, available free allowance or budget, three acceptance criteria and a known-answer case. Separate official specifications from hypotheses and measurements. Leave unmeasured latency, cost and success rates blank. Do not run API calls or activate paid usage.

Frequently asked questions

Which Claude model should I start with? Anthropic currently recommends Opus 5.5 for most workloads. Your own budget and acceptance criteria may justify testing a lower-cost available candidate first. Use the task table above as a starting hypothesis.

Does a Claude subscription include unlimited API usage? No. Check API billing, limits and any eligible credits in your account before running requests.

Are these LearnIA benchmark results? No. The specifications and prices are sourced; the calculations and evaluation cases are teaching materials.

Sources and verification

Checked October 22, 2026: model overview, Haiku 5.5 specifications, Sonnet 5.5 specifications, Opus 5.5 specifications, Fable 5.1 specifications, and official plans and API pricing. Selection scenarios and worked exercises are LearnIA editorial proposals, not provider guarantees.