Model Under the Microscope
In head-to-heads with its Claude siblings, Claude Opus 5.5 pulls clearly ahead only once: against Claude Sonnet 5.5 on section prompt generation [^1]. In its other head-to-heads, the scores’ uncertainty margins overlap, so each ends in a tie [^2][^3][^4]. When quality is level, the choice comes down to cost and how reliably a model answers, so check those before defaulting to Opus 5.5.
Only one lead survives the confidence intervals
Each Opus 5.5 score comes with a confidence interval, the ± margin of uncertainty around it. When two models’ intervals overlap, read them as level rather than ranking one above the other.
| Task | Claude Opus 5.5 | Sibling | Reading |
|---|---|---|---|
| Section prompt generation | 9.12 (±0.22) | Claude Sonnet 5.5: 8.18 (±0.46) | Opus 5.5 clearly ahead [^1] |
| Structured output extraction | 9.93 (±0.16) | Claude Sonnet 5.5: 9.88 (±0.16) | Level [^2] |
| Claim continuity classification | 8.64 (±0.24) | Claude Sonnet 5.5: 8.72 (±0.27) | Level [^3] |
| Capability category matching | 8.81 (±0.32) | Claude Haiku 5.5: 8.56 (±0.46) | Level [^4] |
Claim continuity classification is the row to remember. Claude Sonnet 5.5 has the higher point score there, yet the intervals overlap, so neither model leads [^3]. A ranking by point score alone would separate two models that are level.
A tie can make the cheaper sibling the better value
Capability category matching shows what a tie is worth. Opus 5.5 has the higher score there, but its interval overlaps Claude Haiku 5.5’s, and Haiku 5.5 is cheaper on that task at $0.0002 in batch (bulk, non-real-time requests) [^4]. Unless the extra margin matters, the value case favours Haiku 5.5.
One check before switching: the quality scores above are not batch results. Confirm that batch output meets your bar before moving production traffic.
The scores don’t show how often Opus 5.5 answers
Answer rate, the share of calls that return an answer, is the other half of a tie-break, and the quality scores can’t stand in for it. Each score is measured only on calls that returned an answer, so it describes answered output, not every attempt.
None of the Opus 5.5 scores above comes with an answer rate, latency or parse-failure figure. So a score on answered calls cannot be converted into an expected score on every input.
What to do with this
- For your task, check whether a sibling’s interval overlaps Opus 5.5’s; if it does, treat the two as level, whichever score is higher.
- On a tie, compare cost and answer rate, and take the cheaper model unless the extra margin matters to you.
- If every input needs an output, test answer rate, latency and parse failures on your own inputs before deploying Opus 5.5.
Check the live benchmark at https://fronset.ai/benchmark/ to set the quality bar your workload needs before committing to a model.