Claude Opus 5.5 vs GPT 6 Astra: Which Is Better?

Opus 5.5 ships Fable-level capability at Opus pricing while Astra pushes scientific and cybersecurity ceilings higher, so the better model follows your workload. For repository-scale coding, professional knowledge work, and sustained agent loops where cost per completed task controls the budget, Opus 5.5 holds the measurable edge and it costs far less per token. For scientific research workflows, frontier mathematics, and computer-use tasks where speed and retrieval accuracy set the outcome, Astra holds the edge.

Dimension	Claude Opus 5.5	GPT 6 Astra
Release and model ID	September 22, 2026, claude-opus-5-5	September 3, 2026, gpt-6-astra
Context and output	1M tokens context, 128K output (300K via Batch beta)	1,050,000 tokens context, 128K output
Knowledge cutoff	June 2026	April 30, 2026
List price per 1M	$4 input / $20 output, cache read $0.20, cache write $5	$10 input / $50 output, cached input $1, cache write $12.50
Long-context surcharge	None up to 1M	Above 272K input: 2x input and cache, 1.5x output for entire request
Speed option	Fast mode $8 / $40 on first-party API, about 2.5x speed	Fast mode 2x applicable rates for about 2x speed
Where it tops this pair	SWE-bench Pro 89.9, Terminal-Bench 4.0 66.4, GDPval-AA v2.1 1846 Elo, HLE with tools 67.7	Terminal-Bench Science 64.6, FrontierMath Tier 4 97.6, ExploitBench 100, OSWorld about 47 percent less time per task
Shared strength	Adaptive thinking always on, effort controls depth	Reasoning effort low to max with notes across windows

Reasoning and coding: what each model finishes

Opus 5.5 leads the coding benchmarks that Anthropic publishes for this pair. It reports 89.9 on SWE-bench Pro against no published Astra figure, 66.4 on Terminal-Bench 4.0 against 57.9 for Astra, 54.4 on FrontierCode Main against 53.3 for Astra, and 57.8 on CursorBench 4.0 where Astra has no published score on that harness. Anthropic notes standard error of about 2.6 points on Terminal-Bench 4.0, so the gap exceeds noise, and Anthropic adds that Opus 5.5 achieves those scores at lower effort than the prior flagships.

Astra leads on scientific reasoning and abstract mathematics by clear margins on the shared tables. It scores 64.6 on Terminal-Bench Science 0.1 against 58.7 for Opus 5.5, 97.6 on FrontierMath Tier 4 against 87.8 for Fable 5.1 with Opus 5.5 not separately listed on that OpenAI table, and 96.0 on GPQA Diamond against no directly comparable published Opus 5.5 figure on the same harness, though Opus 5.5 reports 67.7 on Humanity's Last Exam with tools against 57.2 for Astra. The practical signal is that research code that mixes data analysis, simulation, and model fitting favors Astra, while repository patching and multi-file terminal work favors Opus 5.5.

On paper the two overlap on DeepSWE v1.1: Astra at 74.1 and Opus 5.5 at 74.2 on Anthropic's card for that family, effectively a tie within harness variation. Teams that measure coding quality by merge-ready diffs should run FrontierCode on their own stack, teams that measure by terminal completion should run Terminal-Bench, and both should report effort level alongside the score because effort shifts the result by several points.

Agentic tool use and computer use: throughput against task breadth

Astra was designed for agents that drive real software and it shows in the computer-use numbers OpenAI reports. Astra scores 72.6 on OSWorld 2.0 offline partial with about 40 minutes per task against 65.7 at about 75 minutes for GPT-5.6 Sol, and 92.7 on ScreenSpot-Pro grounding, with Anthropic reporting Opus 5.5 at 81.8 partial and 48.7 strict on OSWorld 2.0 under its own setup. The two OSWorld figures use different task releases, so time per task is the useful comparison.

Opus 5.5 counters on business workflow automation and knowledge-work breadth. It reports 40.0 on AutomationBench against 41.4 for Astra on Zapier's public leaderboard, essentially tied on that harness, and 1846 Elo on GDPval-AA v2.1 against 1542 for Astra at max effort, with AA-Briefcase at 1822 against 1569. Artificial Analysis places Opus 5.5 at 58 on the Intelligence Index against 53 for Astra, while Astra leads on the Coding Agent Index variant at 67.0 against no Opus 5.5 figure on that slice, which shows how index construction shifts the headline.

Long-horizon reliability favors careful setup on both sides. Astra notes that ARC-AGI-3 at 99.9 reflects a Responses API harness with retained reasoning between turns, and Opus 5.5 notes that its automation scores reflect production safeguards with fallback routing for a small share of tokens. Product teams gain from measuring cache-hit rate, output tokens per task, and retries to completion, since those three values decide the bill.

Context, pricing, and the surcharge that changes the math

Both models offer a 1M class window, but pricing and long-context cost diverge sharply. Opus 5.5 lists $4 per million input and $20 per million output, with cache reads at $0.20, while Astra lists $10 per million input and $50 per million output, with cached input at $1 and cache writes at $12.50. Anthropic states typical workloads cost about 40 percent less than Opus 5 and output arrives about 30 percent faster, and that reductions come from both lower list price and fewer tokens per task.

Astra adds a surcharge that matters for large-context designs. Prompts above 272K input tokens pay 2 times input and cache rates and 1.5 times output for the entire request. A 1M window is still available, but using it as a substitute for retrieval raises the effective rate to $20 per million input and $75 per million output for that request.

Opus 5.5 applies no surcharge up to 1M, and its cache-read price at 5 percent of input favors persistent agents that reread large prompts.

Measured cost per task illustrates the split. Anthropic reports Opus 5.5 matching Astra on Terminal-Bench 4.0 at roughly 40 percent of the cost and beating Astra on FrontierCode at roughly 20 percent of the cost per task under its test setup. OpenAI reports Astra beating Fable 5.1 on Terminal-Bench at about 63 percent lower estimated cost and on BenchCAD at about 86 percent lower, both figures tied to that vendor's harness.

The numbers point in the same direction: token efficiency and cache use decide the invoice, so teams should price their own traces.

Safety, refusals, and how each model handles dual-use boundaries

Both models ship with expanded safeguards for biology, cybersecurity, and frontier AI development, and both restrict exploit creation in the generally available build. Astra is the first OpenAI model designated at the Critical cybersecurity threshold, with 100 on ExploitBench and 88.0 single-attempt on SRE-Bench when tested without production safeguards, and OpenAI routes advanced defensive work through Daybreak Blue for vetted testers. Opus 5.5 applies expanded biological safeguards shared with the prior Mythos lineage, and Anthropic reports it did not cross the next capability threshold for novel weapon synthesis.

Day-to-day behavior differs around refusals and scope discipline. Astra reports 91.5 percent refusal on cyber jailbreak attempts, about 60 percent fewer cyber false positives for Fable 5.1 lineage safeguards that Opus 5.5 extends, and about half as many higher-severity misalignment flags as GPT-5.6 Sol across 54,000 internal Codex tasks.

Opus 5.5 is also the most aligned Anthropic model on the 2,000-scenario behavioral audit Anthropic cites, though the audit is an internal measure. Both vendors note a monitorability tradeoff, with Astra's recurrent-depth reasoning harder to monitor than prior models and Opus 5.5 tying reasoning blocks to the originating conversation.

Operational controls are explicit and narrow. Astra refuses proof-of-concept exploit generation in the standard deployment and allows deeper access through configured verification programs.

Opus 5.5 routes cybersecurity tasks that trigger safeguards to fallback handling when safeguards intervene, which can lower some benchmark scores by design. Engineering teams should keep tool allowlists, tenant scope, and trajectory logging aligned to those boundaries.

Who should pick which

Pick Opus 5.5 when the workload is codebase-scale patching, terminal agents, IDE-assistant work, or professional documents and slides where GDPval-style quality rubrics matter and where cache-heavy persistent loops benefit from $0.20 cache reads. The model matches Fable-class scores on most reported benchmarks at 60 percent lower list price and shows the strongest published margins on SWE-bench Pro and Terminal-Bench in this pair. For high-volume execution, the per-task estimates from Anthropic point to materially lower spend at default effort.

Pick Astra when the workload is scientific research workflows, reconstruction tasks, or computer-use throughput where time per task carries weight, or when frontier mathematics and exploit-aware defenses are central to the use case. Astra's leads on Terminal-Bench Science, FrontierMath, BenchCAD at 95.9, and health-related research benchmarks are the widest margins in the shared tables, and its OSWorld latency reduction of about 47 percent matters for human-in-the-loop sessions. Budget planning should include the above-272K surcharge if the design relies on very large prompts.

When the workload mixes both patterns, routing is the practical answer. Use Opus 5.5 for patch and document paths, route research and heavy computer-use steps to Astra, and keep a cheaper model for short single-turn tasks so neither flagship bears work it does not improve. Measure Elo, completion rate, and cost per successful task on recorded traces from the product data, with vendor tables as background.

Building the product once across model rotations

Model leadership is rotating fast enough that a product integration tied to a single checkpoint ages quickly. We ship an embedded AI agent that lives inside the SaaS product, reasons over that product's schemas, records, and permissions, and performs multi-step work for end users without moving the work to an external chat.

The team keeps ownership of its API, data model, domain rules, tenant boundaries, and definition of a correct result, including which actions each model may attempt. Managed MCP infrastructure exposes selected product capabilities to external clients under governed access, so Astra, Opus 5.5, and fallback models connect without rebuilding a separate server per model family. A useful next step is a 20-minute demo scoped to one valuable workflow run in a sandbox of the product with both models routed side by side on recorded traces.

Sources

• Introducing Claude Opus 5.5, Anthropic, September 22, 2026. Accessed September 24, 2026.
• Claude Opus 5.5 overview and pricing, Anthropic Platform Docs. Accessed September 24, 2026.
• Claude Opus 5.5 System Card, Anthropic, September 22, 2026. Accessed September 24, 2026.
• Anthropic releases Opus 5.5 with lower prices and Fable-level performance, TechCrunch, September 22, 2026. Accessed September 24, 2026.
• Anthropic releases Claude Opus 5.5, beating Fable 5.1 on key benchmarks at 60 percent cheaper API price, VentureBeat, September 22, 2026. Accessed September 24, 2026.
• GPT-6 Astra: A new generation of intelligence, OpenAI. Accessed September 24, 2026.
• GPT-6 Astra Model, OpenAI API Docs. Accessed September 24, 2026.
• GPT-6 Astra System Card, OpenAI Deployment Safety Hub, September 3, 2026. Accessed September 24, 2026.
• GPT-6 Astra: Release Intelligence, Performance and Price, Artificial Analysis. Accessed September 24, 2026.
• GPT-6 Astra pricing and benchmarks summary, LLM Stats, September 3, 2026. Accessed September 24, 2026.