GPT-6 Astra vs Claude Opus 5: Is 2x the Price Worth It?

OpenAI launched GPT-6 Astra on 3 September 2026. Anthropic launched Claude Opus 5 on 24 July. The obvious question is which one is better, but that framing hides the more useful one: Astra lists at exactly double Opus 5's rate, and Anthropic's own documentation tells most developers to default to Opus 5 rather than its pricier Fable tier. So the real question is whether the new top-of-market model earns a 2x premium over the sensible default.

Every figure below comes from OpenAI's and Anthropic's own documentation, the GPT-6 Astra system card, or an independent evaluator. Where a benchmark compares a vendor against its own predecessor rather than against its rival, it is labelled as such — that distinction matters more here than you would expect.

What is the actual difference between GPT-6 Astra and Claude Opus 5?

Both models are 1M-class context, 128K max output, frontier-tier reasoning models. The headline spec sheet is closer than the launch coverage suggests.

Two things stand out. Astra's context window is marginally larger than Opus 5's, so the often-repeated claim that OpenAI hasn't published Astra's limits is simply wrong — they are in the API model reference. And Anthropic charges no long-context premium at all: its pricing page states plainly that a 900k-token request is billed at the same per-token rate as a 9k-token request. OpenAI's does not work that way, which becomes the single biggest cost divergence between the two.

What do the benchmarks actually say?

Here is the uncomfortable truth about launch-day benchmark tables: almost none of them compare Astra to Opus 5. OpenAI's published figures compare Astra to GPT-5.6 Sol, its own previous model. They are real numbers, but they tell you how much OpenAI improved, not how Astra stacks up against Anthropic.

OpenAI-published, measured against GPT-5.6 Sol (not against Opus 5):

The Terminal-Bench jump is the most striking result on that list — a 20-point gain on long-horizon terminal work. OpenAI also reports Astra completing OSWorld tasks in roughly 47% less time per task than Sol.

A caution before you carry any of those figures into a cross-vendor spreadsheet. OSWorld 2.0 is reported in more than one scoring mode and the spread is enormous — Anthropic publishes Claude Fable 5.1 at 77.9% under partial scoring and 41.7% under strict scoring: same model, same benchmark, 36 points apart. Astra's 72.6% is a partial score on the offline set. Comparing a partial number against a strict one produces a dramatic and completely wrong result, and vendors also assemble task sets that are not directly comparable between releases. Unless you can confirm both sides used the same convention, OSWorld numbers should not be tabled against each other at all.

For an actual same-harness cross-vendor comparison, there is currently one useful source. Artificial Analysis runs an Intelligence Index composed of nine evaluations under a consistent methodology, and it has both models:

Artificial Analysis's own summary is blunt: Claude Opus 5 is more intelligent than GPT-6 Astra, and roughly half the per-token cost. On the composite, the newer and pricier model comes in two points behind.

The other independent evaluator worth checking is Vals AI, which runs SWE-bench Verified through a deliberately minimal bash-tool-only harness so models are compared like for like. Opus 5 leads that board at 97.0%. Astra does not appear on it — Vals has archived the benchmark as saturated. So no same-harness SWE-bench number for Astra exists, and anyone presenting one should be asked where it came from.

Is GPT-6 Astra worth twice the price of Claude Opus 5?

The 2x gap is unusually consistent. It holds at list price, on cached reads, and on batch — there is no tier where Astra quietly becomes competitive. Above 272K input tokens it gets considerably worse, because OpenAI applies 2x input and 1.5x output to the full request, while Anthropic applies no surcharge at all.

That last row is the one to internalise. Feed either model a 500,000-token codebase and Astra costs nearly four times as much, because the whole request reprices once you cross 272K. If your workload is genuinely long-context — whole-repository reasoning, large document sets, long agent trajectories that accumulate history — the gap is not 2x, it is closer to 4x.

There is one honest counterweight, and it is the most interesting number on this page. When Artificial Analysis actually ran its full index, Astra cost $3,013 against Opus 5's $3,836 — about 21% cheaper, despite charging double. The reason is token efficiency: Opus 5 at max effort emitted 100M output tokens to Astra's 42M, roughly 2.4x as many. Reasoning tokens bill as output tokens on both platforms, and a model that thinks more concisely can undercut a rival with better sticker pricing.

So the correct statement is narrower than "Astra costs 2x". Astra's rates are 2x; your bill depends on how many tokens each model emits, and on one demanding suite Astra came out ahead. Two caveats. First, per-token prices are not neutral across vendors — Anthropic notes its Claude 4.7-and-later tokenizer produces roughly 30% more tokens for the same text than earlier Claude models. Second, Astra offers no documented way to turn reasoning off: its effort levels run low, medium, high, xhigh and max, with no none option. Claude Opus 5 does let you disable thinking, at effort high or below. On a workload that does not need deliberation, that is a cost lever Opus 5 has and Astra does not.

The sticker ratio and the invoice ratio are different numbers. Run your own eval and measure spend per completed task.

Which one is better for coding and agentic work?

On the evidence available today, they lead in different places.

Opus 5 holds the top independent coding result — 97.0% on Vals AI's SWE-bench Verified, ahead of DeepSeek V4 Pro at 96.4% and Kimi K3 at 93.4%. Anthropic describes it as built for complex agentic coding and enterprise work.

Astra looks stronger on computer use and long-horizon terminal tasks. Terminal-Bench 4.0 at 57.7% versus Sol's 37.3% is a large jump, and OSWorld 2.0 at 72.6% with roughly 47% less time per task points at genuinely better GUI-agent behaviour. Astra also scored 100% on OpenAI's ExploitBench and 88.0% of SRE-Bench tasks in a single attempt. Again: those comparisons are against Sol, not against Opus 5.

One workflow difference matters for interactive coding. Artificial Analysis measured Opus 5 at 58.7 output tokens per second with a 77-second time to first token at max effort — slow, and you feel it in a live editor loop. Both vendors sell a fast mode at 2x standard price if latency is the binding constraint.

Can you actually get access to GPT-6 Astra today?

This is the most underrated part of the comparison, because a model you cannot call is not really an option.

OpenAI describes Astra as rolling out to a limited set of organisations, becoming available to ChatGPT Plus, Pro, Business and Enterprise users over the following days, with enterprise API access running through a Trusted Access Program. As of today it is not listed on OpenRouter — we checked the live model API directly: Claude Opus 5 and its batch variant are both there, and no Astra entry exists.

Opus 5, by contrast, has been generally available since 24 July across the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and OpenRouter, with a published retirement commitment of no sooner than 24 July 2027. If you are choosing a model to build on this quarter, that difference in availability and lifecycle certainty is worth more than two points of benchmark index.

Where is each model genuinely weaker or unknown?

GPT-6 Astra. It is the first OpenAI model to meet the Critical threshold for cybersecurity capability under OpenAI's Preparedness Framework — the system card states Astra reaches Critical in cybersecurity and High in the biological and chemical category, while not reaching the High threshold for AI self-improvement. That drives real operational friction: staged access, stricter internal controls, and an external misalignment-monitoring system applied to all tool-using inference. The system card also states plainly that Astra's monitorability has decreased relative to GPT-5.6 Sol, with lower chain-of-thought and full-context monitorability across most token lengths. For teams whose safety case depends on reading the model's reasoning, that is a regression — and it is OpenAI saying so.

Claude Opus 5. It is slow and it is verbose. A 77-second time to first token and 2.4x Astra's output tokens on the same evaluation suite are real costs, both in latency and in spend. Anthropic's newer tokenizer inflating token counts by roughly 30% relative to earlier Claude models compounds this. And Opus 5 has no published equivalent to Astra's headline computer-use results, so if GUI automation is your core workload, the evidence for Opus 5 is thinner.

Unknown for both. There is no same-harness, cross-vendor coding benchmark that includes Astra yet. Treat any head-to-head SWE-bench table you see this week with suspicion.

Which model should you actually pick?

The decision rule is simpler than the benchmark noise implies.

Default to Claude Opus 5. Anthropic's own model-selection documentation says: "If you're unsure which model to use, start with Claude Opus 5 for most workloads." That is a vendor steering you toward its cheaper tier when it would profit from selling you the $10/$50 one — an unusually reliable signal. It is half the rate, it leads the strongest independent coding benchmark, it scores higher on the only independent cross-vendor index that has both, it has no long-context surcharge, and you can actually call it today.

Evaluate GPT-6 Astra when your workload is computer-use or GUI automation, long-horizon terminal work, or security research — the three areas where its published gains are largest — and when you have Trusted Access. Then measure total cost per completed task, because Astra's token efficiency may offset its rates.

If you want the fuller picture on either model, we maintain a Claude Opus 5 launch guide, a complete guide to GPT-6 Astra, and the adjacent comparisons: GPT-6 Astra vs Claude Fable 5.1 for the premium-tier fight, Claude Fable 5.1 vs Opus 5 for choosing within Anthropic's lineup, and Claude Opus 5 vs GPT-5.6 for the previous generation's matchup.

FAQ

Is GPT-6 Astra better than Claude Opus 5?

Not on the only independent same-harness index that has run both. Artificial Analysis scores Claude Opus 5 at 63 and GPT-6 Astra at 61, ranking them #3 and #8 of 202 models. Astra shows clear advantages in computer use and long-horizon terminal tasks, but OpenAI measured those against GPT-5.6 Sol, its own previous model, not against Opus 5.

Is GPT-6 Astra worth twice the price of Opus 5?

For most workloads, no. The 2x gap holds at list price, on cached reads and on batch, and widens to roughly 3.8x above 272K input tokens. The one counterweight is token efficiency: Astra used 42M output tokens to Opus 5's 100M on Artificial Analysis's index, making it about 21% cheaper to run that suite despite double the rates.

Which is better for coding?

Claude Opus 5 holds the strongest independent result — 97.0% on Vals AI's SWE-bench Verified under a minimal bash-only harness. GPT-6 Astra has no same-harness score on that benchmark because Vals archived it as saturated. Astra scored 74.1% on DeepSWE v1.1 and 57.7% on Terminal-Bench 4.0, both measured against GPT-5.6 Sol rather than against Opus 5.

What is GPT-6 Astra's context window?

1,050,000 tokens, with a maximum of 922,000 input tokens and 128,000 output tokens, per OpenAI's API model reference. That is slightly larger than Claude Opus 5's 1,000,000-token window. Note that prompts above 272,000 input tokens are repriced at 2x input and 1.5x output for the entire request.

Which should I use for an agent?

For coding agents and long-context agent loops, Claude Opus 5 — it is half the rate, has no long-context surcharge, and leads the independent coding benchmark. For GUI and computer-use agents, GPT-6 Astra is the stronger candidate on OSWorld 2.0, where it scored 72.6% and completed tasks roughly 47% faster than GPT-5.6 Sol.

Should I switch from Claude Opus 5?

There is no strong case to switch today. Astra scores slightly lower on the independent cross-vendor index, costs twice as much, is on staged access through a Trusted Access Program, and is not yet on OpenRouter. Anthropic's own documentation still recommends starting with Opus 5 for most workloads. Re-evaluate when same-harness coding benchmarks including Astra are published.

The bottom line

GPT-6 Astra is a genuinely impressive model, and its computer-use and terminal-agent results are the most interesting things in the launch. But "impressive" and "worth double" are different claims. On the one independent index that has run both, Astra lands two points behind Claude Opus 5 while charging twice the rate — and nearly four times it once you cross 272K input tokens.

The honest verdict: keep Opus 5 as your default, benchmark Astra on your own workload rather than on either vendor's slide, and measure cost per completed task instead of cost per token — that is the one place where Astra's efficiency genuinely surprises. If you are weighing these models for a production build and want engineers who have already shipped on both, Codersera can extend your team with vetted developers who work with these APIs daily.