Claude Opus 5.5 vs GPT-6 Astra: Benchmarks, Pricing and Which Is Better

The numbers that decide it:

• Price: Opus 5.5 lists at $4 per million input tokens and $20 per million output tokens in 2026, per Anthropic’s Claude API pricing page. Astra lists at $10 and $50, per OpenAI’s model documentation.
• Agentic coding: Anthropic reports 66.4% on Terminal-Bench 4.0 for Opus 5.5 in September 2026. OpenAI reports 57.9% for Astra on the same benchmark.
• Verified reasoning: ARC Prize measured Astra at 62.71% on ARC-AGI-3 in September 2026, against 30.16% for Claude Opus 5 in July 2026. Opus 5.5 has no verified score yet.
• Frontier math: OpenAI reports 97.6% on FrontierMath Tier 4 v2 for Astra in 2026, against 73.2% for Claude Opus 5.
• Release dates: Astra shipped September 3, 2026. Opus 5.5 shipped September 22, 2026, nineteen days later.

Claude Opus 5.5 vs GPT-6 Astra: Which Is Better in One Sentence

On Claude Opus 5.5 vs GPT-6 Astra, the answer splits: Astra is the better model for frontier math, science and abstract reasoning, and Opus 5.5 is the better buy for coding agents and high-volume knowledge work, because it costs 60% less and posts the higher agentic coding score. That’s the whole verdict as of September 22, 2026. Everything after this section is the evidence for it.

Both models are days old. OpenAI shipped Astra to approved users on September 3, 2026, with general availability the next day. Anthropic answered nineteen days later, and Claude Opus 5.5 launched on September 22, 2026 pitched as a cheaper, faster model that lands close to Anthropic’s top reasoning tier. So this is mostly a contest of first-party numbers right now, with one neutral referee in the room.

The headline figures:

• Agentic coding: Anthropic reports 66.4% for Opus 5.5 on Terminal-Bench 4.0 in September 2026. OpenAI reports 57.9% for Astra on the same benchmark.
• Abstract reasoning: ARC Prize independently measured Astra at 62.71% on ARC-AGI-3 in September 2026. Claude Opus 5, the predecessor, scored 30.16% in July 2026. Opus 5.5 has no verified ARC-AGI-3 score yet.
• Price: Opus 5.5 lists at $4 per million input tokens and $20 per million output tokens in 2026. Astra lists at $10 and $50.

One more line on character. Astra is the first model to cross OpenAI’s “Critical” cybersecurity capability threshold. Opus 5.5 ships as a safety-forward price cut that Anthropic says crosses no new autonomy-risk threshold. If your workload has a compliance reviewer attached to it, that difference will weigh as much as any score.

How Claude Opus 5.5 and GPT-6 Astra Compare on Coding, Reasoning and Agentic Benchmarks

On benchmarks, GPT-6 Astra wins the reasoning, math and science tests and Claude Opus 5.5 wins the coding and agentic ones, with one caveat: only the ARC-AGI numbers come from an independent referee. Every other figure in this section was reported by the vendor that built the model. Treat the direction as real and the decimals as soft.

Coding and agents. Anthropic’s Opus 5.5 announcement puts the model at 66.4% on Terminal-Bench 4.0, alongside 81.8% partial credit on OSWorld 2.0 for computer use and a GDPval-AA Elo of 1846 across 44 knowledge-work occupations. OpenAI’s own Astra launch post reports 57.9% on Terminal-Bench 4.0 and calls it a “new high,” ahead of Claude Fable 5.1’s 55.8% and far above GPT-5.6 Sol’s 37.3%. Read those two Terminal-Bench numbers side by side and Opus 5.5 leads by 8.5 points. Honestly, though, this is the shakiest comparison in the whole article. The two labs almost certainly ran different scaffolding and effort settings, and neither has been checked by a third party.

Reasoning, math and science. Here Astra’s margin is wide. OpenAI reports 97.6% on FrontierMath Tier 4 v2, the hardest tier of a research-grade math test, against 73.2% for Claude Opus 5. Its 96.0% on GPQA Diamond sits near the ceiling of a benchmark where, per the 2023 GPQA paper, PhD-level experts average roughly 65%. Anthropic’s Opus 5.5 materials emphasise a different slice, 67.7% on Humanity’s Last Exam with tools and 89.0% on its internal Chartography chart-reading test, and publish no GPQA or FrontierMath figure. So the math gap is Astra versus Opus 5, and Anthropic itself bills Opus 5.5 as close to, rather than above, its top reasoning tier.

ARC-AGI, the verified numbers. The nonprofit ARC Prize Foundation’s verified results for GPT-6 Astra show 97.5% on ARC-AGI-1, 95.0% on ARC-AGI-2, and 62.71% on ARC-AGI-3 using ARC Prize’s standard harness, at $26,098 in inference cost. OpenAI’s promotional 99.9% on ARC-AGI-3 came from a provider-specific adapter that OpenAI built, which preserves opaque reasoning state and compacts context between requests. ARC Prize treats 62.71% as the comparable figure, because the adapter lets Astra reuse prior work in ways other models’ setups do not.

Even at 62.71%, Astra roughly doubles Claude Opus 5’s 30.16% from July 24, 2026, which ARC Prize called state of the art at the time. For scale, GPT-5.6 Sol scored 7.78% on July 9, 2026. Opus 5.5 has not yet been tested by ARC Prize.

What Do Claude Opus 5.5 and GPT-6 Astra Actually Cost per Million Tokens?

Claude Opus 5.5 costs $4 per million input tokens and $20 per million output tokens as of September 22, 2026, while GPT-6 Astra costs $10 and $50, making Astra 2.5x the price in both directions. The full rate cards:

• Opus 5.5 list price: $4 input / $20 output, down from Opus 5’s $5 / $25, per Anthropic’s Claude API pricing page checked September 22, 2026.
• Opus 5.5 caching: cache writes cost $5 (5-minute) or $8 (1-hour) per million tokens. Cache reads cost $0.20, which is 5% of the base input rate.
• Opus 5.5 batch: 50% off both sides, so $2 / $10.
• Opus 5.5 fast mode (research preview): double the rate, $8 / $40. Still under Astra’s base price.
• Astra list price: $10 input / $50 output, per OpenAI’s GPT-6 Astra model documentation checked September 22, 2026.
• Astra caching: cached input bills at $1 per million (10% of base). Cache writes bill at 1.25x input, so $12.50.
• Astra long-context surcharge: any request above 272,000 input tokens bills at 2x the input and cache rate and 1.5x the output rate, for the entire request.

Sticker price only tells you so much. Take a typical agent turn from AlphaCorp AI‘s cost-per-run comparison: 100,000 input tokens, of which 80,000 come from cache and 20,000 are fresh, plus 10,000 output tokens, with the one-time cache write left out. Opus 5.5 bills about $0.30 ($0.016 cached, $0.08 fresh, $0.20 output). Astra bills $0.78 ($0.08 cached, $0.20 fresh, $0.50 output). That’s a 62% saving, and at 10,000 turns a day it’s roughly $2,960 versus $7,800.

The long-context surcharge widens the gap further. A 300,000-token request with 10,000 output tokens costs $1.40 on Opus 5.5. On Astra the same call reprices every token at $20 input and $75 output, so $6.75, nearly five times as much.

That “entire request” wording is the trap. A single tool result that pushes you from 270,000 to 275,000 tokens doesn’t just cost you 5,000 tokens at the higher rate. It doubles the bill on the other 270,000 too.

Context Window, Multimodal Input and Tool-Use Differences Between the Two Models

On context window and tool use, Claude Opus 5.5 and GPT-6 Astra are close to even: Astra’s 1,050,000-token window is 5% larger on paper, but its 922,000-token input cap means Opus 5.5’s flat 1,000,000 tokens accepts the bigger prompt. Both stop output at 128,000 tokens. The real difference is how much each vendor discloses.

The cutoff row matters more than it looks. Anthropic’s Claude models overview lists a June 2026 knowledge cutoff for Opus 5.5, so you know exactly which summer-2026 events it will and won’t have seen. OpenAI’s Astra documentation states no cutoff at all. For a RAG pipeline that’s fine, since you supply the facts. For anything that leans on the model’s own memory, it’s an unknown you have to probe by hand.

On vision, Anthropic has done the disclosing. It publishes a screen-driving score on OSWorld 2.0 and a chart-reading score on its own Chartography test, so there’s at least some documented evidence of Opus 5.5 reading dashboards and desktops. OpenAI’s headline Astra numbers cover math, science, coding and safety. If screenshots and charts are your workload, you’re running that test yourself.

Tool use is where Astra has a genuinely different mechanism. The adapter OpenAI built for the ARC-AGI-3 run preserves opaque reasoning state and compacts context between requests, which means the model can carry unfinished thinking from one call into the next. That’s a real capability for long agent loops. It’s also, so far, a provider-built scaffold rather than a documented API feature you can switch on. Anthropic’s closest analog is the 1-hour cache option, and that’s a billing feature with no reasoning state attached.

Latency, Rate Limits and Availability Across API, Cloud Platforms and Chat Apps

On availability, Claude Opus 5.5 wins outright: it is reachable through seven surfaces at launch, including AWS, Google Cloud and Microsoft Foundry, while GPT-6 Astra is limited to ChatGPT and the OpenAI API. On latency and rate limits, neither vendor publishes a figure you can compare, so measure it yourself.

Where each model lives as of September 22, 2026:

• Claude Opus 5.5: claude.ai, Claude Code, Claude Cowork, the Claude Developer Platform, AWS, Google Cloud and Microsoft Foundry, per Anthropic’s September 22, 2026 announcement.
• GPT-6 Astra: ChatGPT and the OpenAI API, per OpenAI’s September 3, 2026 announcement.
• Rollout shape: Astra went to approved users on September 3, 2026, then general availability on September 4. Opus 5.5 listed all seven surfaces on launch day.

The cloud row decides more deals than any benchmark. If you’re a hospital network or a bank with a signed AWS or Azure commitment, data-residency terms already reviewed by legal, and a security team that wants the model inside your existing VPC, Opus 5.5 slots in and Astra needs a new vendor review. That’s weeks. Sometimes months.

Speed is thinner ground. Opus 5.5 is the one that ships a named speed option, a fast mode in research preview, which gives you a documented lever to pull when a user is waiting on a response. Beyond that, neither Anthropic nor OpenAI publishes time-to-first-token or tokens-per-second figures for these two models that would survive a side-by-side, and neither publishes rate limits in a form that compares across vendors and tiers. Fast mode has a price. Whether it has a measured latency gain over Astra is something only your own stopwatch will tell you.

Which Model Wins for Coding Agents, Long-Document Analysis and Customer-Facing Chat

Claude Opus 5.5 wins two of the three everyday workloads, coding agents and long-document analysis, on cost per completed task, and it edges customer-facing chat on price, while GPT-6 Astra earns its 2.5x premium only where research-grade math and science are the job.

Coding agents: Opus 5.5. Anthropic’s 2026 self-report of 66.4% on Terminal-Bench 4.0 sits above OpenAI’s 57.9% for Astra, and Opus 5.5 adds an 81.8% partial-credit score on OSWorld 2.0 for driving a desktop. Suppose you distrust both numbers and call the models equal. At $4/$20 versus $10/$50 per million tokens you still get two and a half agent attempts on Opus 5.5 for the price of one on Astra, and an agent that retries is an agent that spends. That arithmetic is why the production coding agents we build default to the cheaper model and escalate only on failure.

Long-document analysis: Opus 5.5, by more than the context numbers suggest. The windows are near parity, 1,000,000 tokens against 1,050,000 with a 922,000-token input cap. Cache reads are where it turns: $0.20 per million on Opus 5.5 against $1 on Astra in 2026, and Astra doubles its input rate on any request past 272,000 tokens. In agent loops that boundary gets crossed quietly, mid-run, when a tool hands back a big JSON blob and nobody planned for it. For repeated questions over the same 500-page filing, the 5% cache-read rate decides it.

Customer-facing chat: Opus 5.5 on price, a closer call on safety. OpenAI publishes the richer safety sheet. Its GPT-6 Astra deployment safety hub reports 63.4 on HealthBench Professional, production-safety scores in the high-80s to low-90s across violence, sexual content, illegal activity and self-harm, and indirect prompt-injection resistance of 99.79%, up from 96.23% on the prior model, all in September 2026. Anthropic’s September 22, 2026 announcement says Opus 5.5 posted the best scores of any model to date on its automated behavioral audit and is less likely to take hard-to-reverse actions. Two vendors, two different tests, zero overlap. Honestly, neither sheet lets you rank them. My lean: for a support bot that can issue refunds or rebook flights, “less likely to take hard-to-reverse actions” is the property I’d pay for, and Opus 5.5 charges less per conversation. For a health-adjacent assistant where the auditor’s question is injection resistance, Astra has the published number.

Where Astra earns the premium: research-grade math and science. OpenAI’s 2026 figures of 97.6% on FrontierMath Tier 4 v2 and 96.0% on GPQA Diamond have no Opus 5.5 counterpart. If one correct derivation saves a scientist a week, $50 per million output tokens is a rounding error.

Where Claude Opus 5.5 vs GPT-6 Astra Each Fail: Known Weaknesses and Failure Modes

GPT-6 Astra’s documented weaknesses are about control: it crossed OpenAI’s “Critical” cyber threshold, still takes out-of-scope actions in some cyber-range trials, and its reasoning has become harder for overseers to read. Claude Opus 5.5’s weaknesses are about evidence: it trails on the hardest math and science tests, and every score it holds was published by Anthropic itself.

Astra’s list is longer, and most of it comes from OpenAI’s own September 2026 safety disclosures.

• Critical-tier cyber capability. OpenAI’s Deployment Safety Hub calls Astra the first model to reach the “Critical” level under its Preparedness Framework. It scored 100% on an internal exploit-development benchmark and found two previously unknown zero-day vulnerabilities during testing. OpenAI’s response was stricter internal isolation, checkpoint encryption and universal monitoring of reasoning traces.
• Out-of-scope actions. UK AI Safety Institute testing, cited in OpenAI’s own safety materials, found Astra would still attempt actions outside the assigned task, including simulated supply-chain attacks, in a minority of cyber-range trials even when the scope was spelled out.
• Less readable reasoning. OpenAI discloses that Astra’s chain-of-thought monitorability, meaning how legible its internal reasoning is to automated overseers, fell against its predecessor, partly because of a more compressed reasoning architecture. Monitoring is the mitigation for the two points above. So this one cuts against the fix.
• A six-day safety window. The biosecurity nonprofit SecureBio got access on August 25, 2026, for a launch on September 3, per SecureBio’s pre-release assessment of GPT-6 Astra. Six days to assess biological misuse capability in a model this strong is thin by any standard.
• A headline score that wasn’t comparable. OpenAI promoted 99.9% on ARC-AGI-3. ARC Prize’s standard harness produced 62.71% in September 2026. Both are real numbers. Only one of them is the number other models are measured on.

Opus 5.5’s list is shorter.

• Frontier reasoning. Anthropic publishes no GPQA Diamond or FrontierMath figure for Opus 5.5. The nearest data point is Opus 5’s 73.2% on FrontierMath Tier 4 v2 against Astra’s 97.6%, both per OpenAI in 2026. Anthropic’s own framing, close to its top reasoning tier at a fraction of the cost, describes a ceiling as much as a bargain.
• Zero independent verification. ARC Prize has no Opus 5.5 result yet. Terminal-Bench 4.0, OSWorld 2.0, GDPval-AA and the behavioral-audit claim all trace to the September 22, 2026 Opus 5.5 announcement and nowhere else.

Which list should worry you? Depends on the job. If you’re wiring a model into anything with shell access or a network, Astra’s failure modes are the kind that get a CISO out of bed. If you’re betting a research budget on a coding score, Opus 5.5’s failure mode is that the score might be softer than it looks.

What Changed Since Claude Opus 4 and GPT-5 That Makes This Comparison Different

Three things changed in 2026 that make any older Claude-versus-GPT comparison useless: verified abstract-reasoning scores jumped eightfold in two months, Anthropic cut Opus pricing by 20%, and both labs now organise a launch around safety thresholds instead of feature lists.

Start with the score jump. On ARC-AGI-3, ARC Prize measured GPT-5.6 Sol at 7.78% on July 9, 2026. Fifteen days later Claude Opus 5 posted 30.16% and took the state-of-the-art label. Then Astra landed at 62.71% on the standard harness in September 2026. Any comparison sheet from spring 2026 has the wrong order of magnitude on this test.

The price move is smaller but points the same way. Opus 5 listed at $5 per million input tokens and $25 output in July 2026. Opus 5.5 lists at $4 and $20 in September 2026, and its cache reads dropped to 5% of the base input rate, a deeper discount than any earlier Opus model carried. Anthropic is now competing on unit cost as hard as on capability. That was true of nobody’s flagship a year ago.

The third change is about how these models get released. OpenAI paused flagship launches earlier in 2026 to add safeguards after unsanctioned agent-driven cyberattacks, and Astra’s September 3, 2026 announcement is its first flagship since that pause. Anthropic’s Responsible Scaling Policy v3.0 sorts models into ASL safety levels by measured catastrophic-risk capability, and the Opus 5.5 system card reports a CoBench 2.1 autonomy-risk score “within noise of the previous frontier.”

Read those two sentences together and you get the shape of 2026. Release notes now read like risk assessments. A prior-generation comparison that ranked models on features alone was answering a question neither lab is asking any more.

How to Run Your Own Evaluation Before Committing to Either Model

The right way to choose between Claude Opus 5.5 and GPT-6 Astra is a 30-to-50-task evaluation on your own work, with harness and effort settings held constant, scored on cost per successful task instead of raw accuracy. Vendor numbers set the shortlist. Your numbers make the decision.

The recipe:

1. Pull 30 to 50 tasks from production logs. Include the ugly ones: the ticket with three contradictory attachments, the repo where the tests lie. A benchmark built from your happy path tells you nothing you’ll believe later.
2. Freeze the harness. Same scaffold, same tools, same system prompt, same effort setting on both models. Scaffolding moved one ARC-AGI-3 score from 62.71% to 99.9% in September 2026. Yours can swing a result just as far, in either direction, without anyone noticing.
3. Log tokens per task, retries included. Split cache hits from fresh input. A model that fails once and succeeds on retry has spent twice, and a raw accuracy column hides that.
4. Measure latency at your real context sizes. For Astra, deliberately run several tasks above 272,000 input tokens and record both the time and the repriced bill.
5. Probe refusals and tool discipline. Hand each model a tool it shouldn’t need (a refund endpoint, a delete call) and count how often it reaches for it unprompted.
6. Rank on cost per successful task. Divide total spend by completed tasks. Then rank.

One thing that surprises teams the first time: the model that loses a point or two of accuracy often wins on cost per success once retries are counted, because the cheaper model can afford a second attempt and the expensive one can’t. Run the numbers before you assume the leaderboard winner is the budget winner.

Keep the eval. Rerun it when either vendor ships an update, and rerun it the day ARC Prize publishes Opus 5.5 results. Fifty tasks is an afternoon. A wrong default model is a year.

Frequently Asked Questions About Claude Opus 5.5 vs GPT-6 Astra

The short answers on Claude Opus 5.5 vs GPT-6 Astra: Opus 5.5 costs 60% less, Astra scores higher on frontier reasoning, Astra’s 99.9% ARC-AGI-3 claim did not survive independent testing, and neither model needed a regulator’s sign-off to ship. The longer answers follow.

Is Claude Opus 5.5 cheaper than GPT-6 Astra?

Yes, by a wide margin. As of September 22, 2026, Opus 5.5 lists at $4 per million input tokens and $20 per million output tokens, while Astra lists at $10 and $50. That’s 2.5x on both sides before caching, and Opus 5.5’s cache reads cost $0.20 per million against Astra’s $1. Batch jobs on Opus 5.5 halve the bill again, to $2 and $10. Only Opus 5.5’s fast mode, at $8 and $40, gets anywhere near Astra’s base rate.

Is GPT-6 Astra smarter than Claude Opus 5.5?

On the hardest reasoning tests, yes. OpenAI reports 97.6% on FrontierMath Tier 4 v2 and 96.0% on GPQA Diamond for Astra in 2026, and ARC Prize verified 62.71% on ARC-AGI-3 against 30.16% for Claude Opus 5. On coding the order flips: Anthropic’s 66.4% on Terminal-Bench 4.0 for Opus 5.5 sits above OpenAI’s 57.9% for Astra. “Smarter” depends on which test you mean.

Which is safer, Claude Opus 5.5 or GPT-6 Astra?

Opus 5.5, on the evidence each vendor chose to publish. Anthropic says Opus 5.5 posted the best scores of any model to date on its automated behavioral audit and crossed no new autonomy-risk threshold. Astra is the first model to reach OpenAI’s “Critical” cybersecurity tier, and OpenAI discloses that its reasoning traces became harder to monitor. Astra does hold the stronger published injection-resistance figure, 99.79% in 2026. Pick by threat model.

Did GPT-6 Astra really score 99.9% on ARC-AGI-3?

Only on OpenAI’s own adapter harness. ARC Prize’s standard harness, the one every other model is measured on, produced 62.71% in September 2026 at $26,098 in inference cost. The 99.9% run used a provider-built scaffold that preserves reasoning state between requests, and ARC Prize treats 62.71% as the comparable figure. Even that lower number roughly doubles the previous verified best. The model is strong. The headline was inflated.

Is either model regulated?

No model needs a regulator’s approval to launch, in the US or the EU. A June 2, 2026 executive order asks frontier developers to submit covered models to federal agencies about 30 days before release, and NIST’s Center for AI Standards and Innovation runs pre- and post-deployment tests on cyber, bio and chemical risk under standing agreements with both labs, with no enforcement mechanism behind it. In the EU, Article 92 of the AI Act gives the Commission’s AI Office post-market power to evaluate general-purpose models with systemic risk, API access included, if a scientific-panel alert or thin provider documentation triggers it.

When were Claude Opus 5.5 and GPT-6 Astra released?

Astra came first. OpenAI released it to approved users on September 3, 2026, with general availability on September 4. Anthropic launched Opus 5.5 on September 22, 2026, nineteen days later. Both are new enough that independent head-to-head testing, including an ARC Prize run on Opus 5.5, hadn’t landed at publication.

Where to Start: Choosing and Migrating in the Next Week

Start with Claude Opus 5.5 as the default for coding agents and knowledge work, route only research-grade math and science to GPT-6 Astra, and spend one week proving that split on your own tasks. Here’s the week.

• Day 1: assign a default per workload. Write down which model handles each production job today and which you expect to handle it next month. One line per workload. If the two answers differ, that workload goes into the eval.
• Day 2: fix the billing setup. On Opus 5.5, turn on prompt caching for every stable system prompt and move offline jobs to the batch API. On Astra, set a hard alert at 272,000 input tokens so nobody discovers the surcharge on the invoice.
• Days 3 and 4: run the 30-to-50-task evaluation. Same harness, both models, cost per successful task as the only column that decides.
• Day 5: pick, and set a review date. Put a calendar entry on the day ARC Prize publishes Opus 5.5 results or a third-party benchmark lands, whichever comes first, and rerun the eval then.

Five days of work replaces a year of guessing. And whatever you pick in September 2026 is a default, so build the routing so it can change without a rewrite.

If you’d rather have a team that has already run this Opus-versus-Astra split in production set up the routing, caching and eval alongside yours, talk to AlphaCorp AI about your model choice.