Claude Haiku 5.5 benchmarks are useful for choosing what to evaluate next. They are not a guarantee that the model will solve your application's tasks. This guide separates three questions: what the publisher reports, which evaluation settings matter, and what independent signals AI Rank currently has available.
The practical approach is to shortlist Haiku 5.5, test it against your current model on the same workload, and record both successful outcomes and the resources required to get them. All data observations below were checked on October 9, 2026.
Selected official Haiku 5.5 benchmark scores
The following figures come from Anthropic's launch report. They are publisher-reported results, not tests conducted by AI Rank.
| Benchmark and condition | Haiku 5.5 | Haiku 4.5 |
|---|---|---|
| OSWorld 2.1, offline subset | 72.4% | 15.7% |
| Humanity's Last Exam, no tools | 45.9% | 10.2% |
| Terminal-Bench 4.0 | 39.2% | 0.0% |
This is a selection of benchmarks, not an overall score. Keep the version, subset and tool setting attached to each number. In particular, the reported 0.0% is a result for that Terminal-Bench evaluation; it does not mean Haiku 4.5 has no coding ability.
How to read a Claude Haiku 5.5 benchmark
A comparison is much more informative when the model identifier, prompt, tools, effort setting and output limit are recorded together. Otherwise, you may be comparing different systems while attributing the difference entirely to the model.
Anthropic's effort documentation lists five settings for Haiku 5.5 and says its Claude API default is medium. Raising effort can change quality, latency and token consumption. An omitted setting therefore should not be assumed to mean the highest available setting.
For your own evaluation, make the effort level explicit and keep the surrounding workflow constant. If you choose a different configuration for each model, report it as a comparison of those configurations rather than a clean model-only comparison.
What AI Rank's current data adds
AI Rank provides additional signals for selecting candidates. It does not convert one leaderboard into another.
In the Arena snapshots checked for this article, both dated October 8, 2026, claude-haiku-5-5 was not present in the overall or coding lists. We therefore do not assign Haiku 5.5 an Arena rank here. The absence of a record is not a zero score or evidence that the model performs poorly.
The older claude-haiku-4-5-20251001 identifier was present:
| Arena category | Recorded rank | Rating | Rating interval | Votes |
|---|---|---|---|---|
| Overall | 142 | 1414.03 | 1411.61–1416.45 | 146,351 |
| Coding | 107 | 1481.31 | 1477.26–1485.36 | 37,782 |
Source: AI Rank's overall API and coding API, using Arena's text_style_control policy. Original data: Arena and its leaderboard dataset, licensed CC BY 4.0. Displayed ratings and interval endpoints are rounded to two decimal places. Live pages can change after this article's observation date.
→ Track it on AI Rank: Claude Haiku 4.5 — Arena rating, rank and 90-day history
Those ratings describe a different evaluation from OSWorld or Terminal-Bench. They are not percentages, and the recorded Haiku 4.5 ratings cannot be carried over to Haiku 5.5.
AI Rank also tracks OpenRouter usage. That is another question again: how much a model is used within a stated platform, category and time window. Token usage is not a benchmark score. Check the source and dates on the model usage board before treating a popular model as the best option for your task.
A practical evaluation before switching
Define what a pass means before choosing a winner. Anthropic's evaluation guide recommends measurable criteria aligned with the application's purpose. The following is AI Rank's suggested starting worksheet, not a benchmark we have already run:
| Your workload | Define a pass | Record alongside it |
|---|---|---|
| Structured extraction | Required fields are correct and output parses | Missing fields, invalid output and corrections |
| Repository changes | The intended change passes your checks | Regressions, retries and review time |
| Tool workflow | The requested end state is reached | Incorrect tool calls and recovery attempts |
Use a held-out set of representative cases, including examples your current system struggles with. Run the same cases with the current model and Haiku 5.5, repeat uncertain cases, and keep the prompts and configuration with the results.
For each run, record task success, end-to-end latency, billed cost and whether a person had to intervene. Compare cost per successful task as well as cost per request: a cheap failed attempt followed by manual repair may be a poor trade.
Where to continue
Use the Haiku 4.5 model profile for the historical identifier discussed above, and the top-rated model board to find other candidates. Check the category and snapshot date before drawing a comparison.
The next decision is whether Haiku 5.5 meets your own acceptance criteria. Keep the official scores as one piece of evidence, then make that decision with comparable task results.
Compare before you choose: ratings and rankings move daily — check the current top-rated models on AI Rank before you commit.
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Prepared with AI assistance. AI Rank checked the cited official documentation and its recorded data; this article does not claim an independent hands-on benchmark.