Claude Opus 5.5 vs GPT-6 Astra
Opus 5.5 has lower standard token rates; Astra offers a slightly larger context window. Compare API tooling, long-context pricing and your own task results.
Reviewed 2026-09-25 · Official API facts and attributed benchmark results
| Specification | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|
| Context window | 1M | 1.05M |
| Max output | 128K | 128K |
| Input / 1M tokens | $4 | $10 |
| Output / 1M tokens | $20 | $50 |
| Cache read / 1M tokens | $0.2 | $1 |
| Reasoning | Adaptive, always on · medium by default | Effort: low, medium, high, xhigh, max |
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 GPT-6 Astra
Evaluate for workflows built around the Responses API, hosted tools and computer use.
Cross-vendor benchmark numbers below are reported by Anthropic. They are not an independent, identical-settings evaluation.
Standard rates up to 272K input tokens. Above 272K, the full request uses 2× input/cache rates and 1.5× output rates. Tools may add fees.
Official specificationsReported benchmark results
| Benchmark | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|
| Terminal-Bench 4.0Opus 5.5: xhigh effort | 66.4% | 57.9% |
| FrontierCode v1.1Opus 5.5: max effort | 54.4% | 53.3% |
| CursorBench 4.0Opus 5.5: max effort | 57.8% | Not reported |
| GDPval-AA v2.1Opus 5.5: max effort | 1846 Elo | 1542 Elo |
| AutomationBenchOpus 5.5: max effort | 40% | 41.4% |
| Humanity's Last ExamWith tools · Opus 5.5: max effort | 67.7% | 57.2% |
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.