Claude Opus 5.5 vs GPT-6 Astra: Complete Comparison and Report on Pricing, Benchmarks, Effort Levels, and Cost per Completed Task
GPT-6 Astra launched on September 3, 2026, as OpenAI's flagship. Claude Opus 5.5 launched nineteen days later, priced at less than half of Astra's rate, and Anthropic's own launch material places the two directly against each other on several benchmarks, at explicitly different effort settings for each.
That effort mismatch is not incidental to this comparison; it is close to the entire story. This report works through where the two models were tested under matching conditions, where they were not, and what the surviving benchmarks say once the effort-level asterisks are accounted for.
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RELEASE TIMELINE AND MODEL IDENTITY.
Model identifiers, lineage, and where each sits in its vendor's lineup.
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Attribute | GPT-6 Astra | Claude Opus 5.5 |
Vendor | OpenAI | Anthropic |
Release date | September 3, 2026 | September 22, 2026 |
Model ID | gpt-6-astra | claude-opus-5-5 |
Predecessor | GPT-5.6 Sol | Claude Opus 5 |
Position in lineup | Sole flagship | Anthropic's recommended default for most work |
Restricted sibling | None disclosed independently of Daybreak | None disclosed |
Knowledge cutoff | April 30, 2026 | June 2026 |
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Astra carries no internal ceiling above it in OpenAI's lineup and is rated Critical for cybersecurity capability, a threshold documented in this outlet's earlier coverage. Opus 5.5 is explicitly positioned by Anthropic as the model most teams should reach for by default, a recommendation that places it below Fable 5.1 in raw ceiling but ahead of it in Anthropic's own stated guidance for typical work.
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PRICING AND RATE STRUCTURE.
Full API rates, including the long-context penalty only one of the two applies.
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Rate per 1M tokens | GPT-6 Astra | Claude Opus 5.5 |
Input | $10 | $4 |
Output | $50 | $20 |
Cached input | $1.00 | $0.20 |
Cache read as % of input | 10% | 5% |
Long-context penalty | Above 272,000 input tokens: 2x input/cache, 1.5x output, entire request | None |
Zero data retention | Available for eligible API customers | Not confirmed in this comparison |
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Astra costs 2.5 times Opus 5.5's rate on input and output alike. Its cache-read discount is also proportionally shallower: cached tokens cost 10% of the input rate on Astra against 5% on Opus 5.5, meaning Opus 5.5's cache economics are better by ratio as well as by absolute dollar figure, a combination this outlet has not found in the other cross-vendor comparisons covered so far.
Astra's 272,000-token pricing cliff has no equivalent on Opus 5.5, which bills its full 1-million-token window at a flat rate regardless of prompt length.
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CONTEXT WINDOW, OUTPUT LIMITS, AND EFFORT CONTROLS.
Specifications and the reasoning-effort settings that shape every benchmark comparison below.
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Specification | GPT-6 Astra | Claude Opus 5.5 |
Context window | 1,050,000 tokens | 1,000,000 tokens |
Maximum output | 128,000 tokens | 128,000 tokens (300,000 in Batch API beta) |
Effort levels | Low, medium, high, xhigh, max | Adaptive, always on; up to xhigh used in published comparisons |
Thinking on/off | Configurable via effort | Cannot be disabled |
Default effort | Low on the API | Medium |
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Both context windows sit within half a percent of each other, removing window size as a differentiator. The more consequential difference is that Astra's effort is fully configurable, including a low-effort default on the API, while Opus 5.5 runs adaptive thinking at all times with no off switch, a breaking change from Opus 5 that this outlet flagged in its dedicated Opus 5.5 coverage.
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BENCHMARK METHODOLOGY: THE EFFORT MISMATCH AT THE CENTER OF THIS COMPARISON.
What Anthropic's own launch material discloses about how its headline Astra comparison was produced.
Anthropic states directly that its Terminal-Bench 4.0 comparison pits Claude Opus 5.5 at xhigh effort against GPT-6 Astra at high effort, "as reported by OpenAI," and describes both figures as "each model's highest score." Astra does support effort levels above high, xhigh and max, but OpenAI's own published figure for Astra on this benchmark uses high, not the model's absolute ceiling, while Anthropic ran its own model one tier higher on the same table.
This means the comparison is not two labs running both models under one shared methodology; it is each lab's self-selected best result, placed side by side. That does not make either figure false, but it does mean the published gap between them describes a comparison of two different labs' choices about how hard to push their own model, not a controlled test.
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THE INTELLIGENCE INDEX AFTER ARTIFICIAL ANALYSIS'S RESCALE.
Where the two stand on the same independent index, using the most recent published snapshot.
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Model | Artificial Analysis Intelligence Index (September 22 snapshot) |
Claude Opus 5.5 | 58, described as the highest score the index has recorded |
GPT-6 Astra | 53 |
Claude Fable 5.1 | 53 |
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This outlet's earlier coverage of Astra, published shortly after its September 3 launch, cited an Artificial Analysis score of 61.2, a figure that cannot be reconciled with the 53 in this later snapshot without assuming a methodology change at Artificial Analysis between the two dates, a change this outlet flagged in its Opus 5.5 coverage and for which no public changelog was located. Readers should treat the 58-versus-53 gap in this September 22 snapshot as the relevant comparison, rather than mixing it with the earlier-published 61.2 figure for Astra.
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WHERE EACH MODEL LEADS: DIRECTLY COMPARABLE SCORES.
Benchmarks both vendors, or an independent evaluator, scored on both models.
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Benchmark | GPT-6 Astra | Claude Opus 5.5 |
Terminal-Bench 4.0 (each vendor's own best-effort figure) | 57.7% (OpenAI, high effort) | 66.4% (Anthropic, xhigh effort) |
Terminal-Bench 4.0 (Artificial Analysis independent figure) | 59.6% | 59.6%, described by the evaluator as tying Astra |
AutomationBench | 41.4% | 40.0% |
Terminal-Bench-Science 0.1 | 64.6% | 58.7% |
FrontierCode v1.1 Main | Not published in this comparison | 54.4% |
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The independent Artificial Analysis figure is the one genuinely apples-to-apples row in this table, and on it the two models are tied. Astra leads on both AutomationBench and Terminal-Bench-Science by a clear margin using each vendor's own reporting, which are the two rows where Anthropic's own Opus 5.5 launch material acknowledges Astra ahead.
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BENCHMARKS ONLY ONE VENDOR PUBLISHED.
Where the comparison cannot currently be completed.
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Benchmark | GPT-6 Astra | Claude Opus 5.5 |
GPQA Diamond | 96.0% | Not published in this comparison |
FrontierMath Tier 4 v2 | 97.6% | Not published in this comparison |
ARC-AGI-2 | 95.0% | Not published in this comparison |
ARC-AGI-3 | 99.9% (stateful harness; independent stateless runs: 17-63%) | Not published in this comparison |
MRCR v2, 512K-1M band | 96.3% | Not published in this comparison |
Humanity's Last Exam, no tools | Not published in this comparison | 61.4% (previous best: 59.1%, Fable 5.1) |
SciCode | Not published in this comparison | 66.9% (previous best: 63.1%, Fable 5.1) |
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OpenAI publishes a distinct cluster of hard reasoning and long-context benchmarks that Anthropic does not run at all in its own comparisons, and Anthropic publishes knowledge and coding benchmarks against its own prior models rather than against Astra directly. Neither gap can be filled from the material available for this report, and any claim of overall superiority based on aggregating across both tables would be combining figures that were never measured against each other.
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OUTPUT TOKEN CONSUMPTION AND WHAT IT MEANS FOR COST.
A specific, sourced multiple that changes the per-token price comparison.
Artificial Analysis measured Claude Opus 5.5 using approximately 119,000 output tokens per task at max effort, a figure the evaluator describes as roughly four times GPT-6 Astra's own output token consumption on comparable work. At $20 per million output tokens, 119,000 tokens costs approximately $2.38; the same task completed with a quarter of the output tokens on Astra, at $50 per million, would cost roughly $1.49 in output alone, before input and cache costs on either side.
This is the reverse of the pattern this outlet documented on Grok 4.7's launch, where a model with an unchanged per-token price consumed more than double its predecessor's output tokens per task. Here, the model with the lower per-token price is the one using substantially more output tokens at its highest effort setting, which narrows the effective cost gap between the two well below what the raw $10-versus-$4 input rate would suggest, at least at Opus 5.5's max effort tier.
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COST PER COMPLETED TASK: ANTHROPIC'S OWN CLAIMS.
Vendor-reported cost comparisons naming Astra directly.
Anthropic states that Opus 5.5, at its default effort level rather than max, beats GPT-6 Astra on FrontierCode at roughly 20% of Astra's cost per task, and matches Astra on Terminal-Bench 4.0 for about 40% of the cost. Both figures come from the winning party's own launch material, use Opus 5.5 at default effort rather than the xhigh setting used in the headline Terminal-Bench comparison earlier in this report, and have not been independently reproduced for this report.
The default-versus-max effort distinction matters here specifically: the 66.4% headline score uses xhigh effort, while the 40%-of-cost claim uses Opus 5.5's default medium effort, which means these are two different operating points on the same model, not the same result described twice.
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SAFETY CLASSIFICATIONS AND ACCESS GATING.
How each lab restricts its most capable configuration.
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Dimension | GPT-6 Astra | Claude Opus 5.5 |
Cybersecurity rating | Critical, OpenAI's Preparedness Framework | Not separately published for this release |
Restricted-capability access program | Daybreak (Blue and Red tiers) | None disclosed |
Safeguard behavior during evaluation | Advanced exploit generation refused; tool use under universal monitoring | Cybersecurity tasks during evaluation routed to Claude Opus 4.8; biology and frontier LLM development tasks routed to Claude Opus 5 |
Effect on published benchmarks | Not separately quantified | Anthropic states this likely reduces Opus 5.5's own scores in the affected categories |
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Both companies route their most sensitive capability categories away from the headline model during evaluation, Astra through refusal and monitoring, Opus 5.5 through substitution with a different Claude model, and both disclosures point the same direction: published safety-adjacent benchmark scores for either model likely understate rather than overstate what the underlying model can do.
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AVAILABILITY.
Where each model can be reached today.
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Channel | GPT-6 Astra | Claude Opus 5.5 |
First-party API | Staged rollout | Available at launch |
Amazon Bedrock | Yes | Yes |
Google Cloud | Not listed | Yes |
Microsoft | Azure | Microsoft Foundry |
Agent surface | Codex | Claude Code |
Enterprise default | Off, admin must enable per workspace | Available same day as release |
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Opus 5.5 shipped without the staged rollout Astra underwent, reaching all of Anthropic's listed platforms on launch day, while Astra's enterprise access required explicit administrator activation per workspace at launch.
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WORKLOAD ALLOCATION CRITERIA.
What the evidence actually supports as a decision between these two.
The one genuinely controlled comparison in this report, Artificial Analysis's independent Terminal-Bench 4.0 run, shows the two models tied, which undercuts any claim of a clear winner built from either vendor's own best-effort figures. Astra's real, uncontested advantages sit in a specific cluster: AutomationBench, Terminal-Bench-Science, and a set of hard reasoning and long-context benchmarks, GPQA Diamond, FrontierMath, ARC-AGI-2 and 3, MRCR, that Anthropic does not test Opus 5.5 against at all. Opus 5.5's uncontested advantages are price, proportional cache economics, and a documented output-token efficiency at least on the specific tasks Artificial Analysis measured, where its higher per-task token count at max effort still lands below Astra's cost on a per-completed-task basis according to Anthropic's own worked examples.
A team choosing between them should weight the decision by which cluster of benchmarks matches its actual workload rather than by either vendor's aggregate framing: work resembling AutomationBench, Terminal-Bench-Science, or the reasoning benchmarks unique to Astra's table favors Astra despite the cost; work resembling Terminal-Bench 4.0 or FrontierCode, where the two are either tied or Opus 5.5 leads at a fraction of the cost, favors Opus 5.5. Given that neither vendor has published a shared, equal-effort test across the full benchmark set, and that Astra's own Terminal-Bench figure used a lower effort tier than Opus 5.5's, any procurement decision should re-run the specific task at matched effort settings on both models before treating either launch table as decisive.
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