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Benchmark Claude Haiku 5.5 on the prompts your application actually uses, scoring quality against a fixed rubric while measuring repeated response times and calculating cost from real token usage. Anthropic announced on September 28, 2026 that Haiku 5.5 would join the Claude 5.5 family “in the coming weeks,” but the announcement published no Haiku 5.5 benchmark results or price. Confirm the model is available, and verify its official identifier, endpoint, and current pricing before testing.
What is known about Haiku 5.5?
Anthropic described Haiku 5.5 as built for “high-volume and cost-sensitive applications” in its September 28, 2026 announcement. The announcement said it would join the Claude 5.5 family in the coming weeks. Its published performance figures and prices are for Sonnet 5.5, not Haiku 5.5; they should not be treated as Haiku measurements.
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The reviewed model information does not establish a Haiku 5.5 identifier, endpoint, availability, latency, benchmark score, or price. Check Anthropic’s current model and pricing information when you are ready to test. The steps below describe a reproducible local benchmark, not published Haiku 5.5 results or an Anthropic-prescribed test protocol.
How do I benchmark Haiku 5.5 on my own prompts?
1. Confirm access and record the setup
Before sending requests, verify that Haiku 5.5 is available to you and confirm the official model identifier and endpoint from current Anthropic documentation or your account. Record the test date, endpoint, route, and region. Keep concurrency, streaming choice, prompt and response limits, and other relevant settings consistent across runs and any comparison models.
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2. Build a representative prompt set
Use a fixed set of prompts drawn from the work you expect the model to perform. Include frequent everyday cases and difficult edge cases whose failure would matter. Keep each prompt’s wording, input data, requested output format, tool configuration, and model settings unchanged between models and repetitions. Anthropic’s prompting best practices recommend clear instructions and relevant examples.
Describe the prompt set when sharing results: include the task mix and enough information about input and expected output sizes to make the scope understandable, while removing private or sensitive data.
3. Set quality criteria before reviewing outputs
Write a short rubric for each task before looking at results. It might score correctness, completeness, format compliance, and task-specific requirements. Define what counts as a pass and identify critical errors that should fail a response even if other parts are good.
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Apply the same rubric to every model output. Use consistent reviewers; when practical, hide model identity from reviewers to reduce expectation bias. Anthropic does not provide a Haiku 5.5-specific evaluation rubric in the cited material.
4. Repeat calls under controlled conditions
Run each prompt multiple times. Keep the route, region, concurrency, streaming choice, input, and settings stable; if any of these change, record the change and treat the results as a separate condition. Log failed requests, timeouts, and retries as well as successful responses. Repeated runs help show whether an apparent result is consistent rather than a single unusually fast or good response.
How should I measure latency?
State exactly what your timer measures. Time to first token and time to complete response answer different questions: the first reflects how soon a streamed response begins, while the second includes generation through the end of the response. Say whether your clock includes network transit, queueing, and your application’s own overhead.
Report repeated measurements with a central statistic and a tail statistic, such as the median and a high percentile, rather than reporting only the fastest run. Separate workloads when prompt length or expected response size differs substantially; otherwise, an aggregate can conceal meaningful differences. Results describe your route, region, load, workload, and test date—not a universal Haiku 5.5 speed.
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Record input and output token usage for each call, along with any applicable cache or batch usage. Check the current pricing for the specific endpoint and date tested, then calculate the charge using that schedule. Anthropic’s pricing page is the reference for current prices; its pricing documentation explains token-based pricing and usage modifiers. Neither source establishes a Haiku 5.5 price in the material cited here.
Compare cost per successful task, not just cost per request. Use your predefined pass criteria to identify successful completions, and include the usage and costs of failed attempts or retries in the operational total. Put quality, latency, and cost together: a cheaper response that fails the task, or a fast response that routinely needs retries, may not be cheaper or faster in practice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a useful results report include?
| Measure | Record | How to interpret it |
|---|---|---|
| Quality | Per-task rubric scores, pass rate, and critical errors | State the task mix and pass definition; an aggregate is meaningful only with that context. |
| Latency | Repeated time-to-first-token and/or full-response times, statistic reported, and test conditions | Results are specific to the route, region, load, prompt and response sizes, and test date. |
| Cost | Input/output tokens, applicable cache or batch usage, cost per task, and cost per successful task | Use the verified price for the endpoint and date tested. |
| Reliability | Failures, retries, and run-to-run spread | Shows whether performance is repeatable and how errors affect operational cost. |
Include the prompt characteristics, model identifier, endpoint, settings, region, concurrency, measurement boundaries, and test date. Do not fill a results table with estimates: the reviewed announcement provides no Haiku 5.5 results to report.
How fast is Haiku 5.5?
No Haiku 5.5 latency statistic is established by the cited announcement or documentation. The announcement’s performance figures belong to Sonnet 5.5. Measure Haiku 5.5 on your own workload using the method above, and label the result with its conditions rather than presenting it as a general model speed.
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