Claude Opus 5.5 vs Claude Fable 5.1
Both offer a 1M context window and 128K output. Opus 5.5 has 60% lower standard input and output rates; the best choice still depends on task quality.
Reviewed 2026-09-25 · Official API facts and attributed benchmark results
| Specification | Claude Opus 5.5 | Claude Fable 5.1 |
|---|---|---|
| Context window | 1M | 1M |
| Max output | 128K | 128K |
| Input / 1M tokens | $4 | $10 |
| Output / 1M tokens | $20 | $50 |
| Cache read / 1M tokens | $0.2 | $0.25 |
| Reasoning | Adaptive, always on · medium by default | Adaptive, always on · high by default |
When to evaluate Claude Opus 5.5
A starting point for evaluating coding and agent workloads, with lower token rates than Opus 5 or Fable 5.1.
Benchmark settings differ from the default medium effort. Measure task completion and total tokens on your own workload.
Standard API rates. Batch input/output is 50% lower; fast mode is $8 / $40. Cache writes: $5 (5 min), $8 (1 hour) per million tokens.
Official specificationsWhen to evaluate Claude Fable 5.1
Worth retaining in evaluations where a specific task already performs well with Fable.
The shared launch table does not establish that either model wins every task. Compare success rates alongside cost.
Standard input/output rates per million tokens. Check the official pricing documentation for caching and service tiers.
Official specificationsReported benchmark results
| Benchmark | Claude Opus 5.5 | Claude Fable 5.1 |
|---|---|---|
| Terminal-Bench 4.0Opus 5.5: xhigh effort | 66.4% | 55.8% |
| FrontierCode v1.1Opus 5.5: max effort | 54.4% | 50.3% |
| CursorBench 4.0Opus 5.5: max effort | 57.8% | 51.8% |
| GDPval-AA v2.1Opus 5.5: max effort | 1846 Elo | 1735 Elo |
| AutomationBenchOpus 5.5: max effort | 40% | 31.4% |
| Humanity's Last ExamWith tools · Opus 5.5: max effort | 67.7% | 65.6% |
Vendor-reported launch results, September 22, 2026. Opus 5.5 uses max effort except Terminal-Bench (xhigh). Other models' effort, harness and tool settings can differ; see the original methodology. These are not AI IDE List tests and are separate from Anthropic's medium-effort cost charts.
Benchmark source and methodologyTry the work that matters to you
Use the same task, inputs, tools and success criteria. Record the model version, effort, total cost, latency and any human fixes. Repeat tasks before choosing a model; a single demo does not establish a general winner.