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Haiku 5.5 vs Sonnet 5.5

Independent third-party tool

Test whether a routine text task can move from Sonnet to Haiku without relaxing its acceptance criteria.

Manufacturer reference

Official standard API specifications and dated prices. The verified ZenMux connection used by this site can have different limits and billing. No benchmark winner is implied.

ModelContext / max outputDefault effortInput / output (USD per 1M)
Claude Haiku 5.5Checked 2026-10-091,000,000 / 128,000medium
$0.10 / $0.50Standard tier
$0.50 / $2.50From 100,001 input tokens
Claude Sonnet 5.5Checked 2026-10-091,000,000 / 128,000high
$2 / $10Standard tier
Start with the work you already repeatPut exceptions in the samplePin the reasoning settingMake the replacement decision in two passesManufacturer sources

Start with the work you already repeat

A within-Claude comparison is useful when Sonnet already produces answers you accept and you want to test a lower-priced alternative. The manufacturer price table establishes the rate difference. It does not establish how many of your tasks Haiku will pass.

Choose a stable workflow: ticket classification, short summaries, or extraction into a fixed schema. Keep the instructions and input identical for both sides. Do not simplify only the Haiku prompt and then treat the result as a model-only comparison.

Put exceptions in the sample

An easy ticket with one clear issue will not reveal much. Include a ticket that mentions a payment problem and a login problem, a document with a missing amount, and a note that contradicts an earlier sentence. Decide in advance which fact should control the answer.

For example, a support classifier can require one primary category plus a short justification. A plausible category with a justification unsupported by the ticket fails the criterion. This keeps the review tied to your workflow rather than to how confident the wording sounds.

  • A missing field becomes null rather than a guess.
  • Conflicting evidence is handled according to the prompt.
  • Quoted instructions inside the input do not override the task.

Pin the reasoning setting

The two models can have different defaults. Record the selected effort for each side and keep it fixed for a batch. Matching the label is not evidence of equal compute; the useful record is the actual configuration and its resulting output.

If you change effort to investigate a failure, start another comparison and keep the original result. Mixing settings midway through a batch would make an aggregate acceptance rate hard to interpret.

Make the replacement decision in two passes

First review completed pairs against the same standard. Mark Haiku acceptable only when its answer can be used for the intended job. A complete Sonnet answer is still a baseline, not automatically ground truth.

Then inspect token usage and reconciled cost for those pairs. Review the failed cases separately before moving the workflow. A cheaper routine path may make sense while an exception remains with Sonnet, but this site does not automatically route future production traffic.

Manufacturer sources

  • Claude Haiku 5.5 model reference · Checked 2026-10-09
  • Anthropic API pricing · Checked 2026-10-09

These sources describe the manufacturers' products. This page does not publish a site benchmark or reproduce a third-party ranking. Your saved outputs are private observations of your own inputs and settings.

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Independent model comparisons, grounded in your own tasks. Not affiliated with Anthropic or OpenAI.

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