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, so the real question is whether it earns a 2x premium. Two things have changed since launch week, and both change the answer. Artificial Analysis rebased its Intelligence Index from v4.1.1 to v4.3.2, and on the new basis Astra is ahead of Opus 5 rather than behind it. And on 22 September 2026 Anthropic shipped Claude Opus 5.5 and moved Opus 5 to legacy status — so for a new build the honest answer is now neither of these two.

Every figure below was re-verified on 5 October 2026 against OpenAI's and Anthropic's own documentation, the GPT-6 Astra system card, or an independent evaluator, and every index score carries the index version it came from. 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 ten evaluations under a consistent methodology — AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience and AA-LCR v1.1 — and it has both models. One caveat that matters before you quote any of it: AA rebased the index from v4.1.1 to v4.3.2, and a rebase moves every score because the benchmark basket changed, not because the models did. There is no conversion factor between the two versions. The figures below are v4.3.2, captured 5 October 2026:

Read that table carefully, because it says close to the opposite of what the v4.1.1-era coverage said. On Intelligence Index v4.3.2, GPT-6 Astra is ahead of Claude Opus 5 — 52.67 to 50.78, and #7 versus #16 in a field of 224 models. Artificial Analysis describes Astra as delivering intelligence “well above average” for its price tier, and describes Opus 5 as “particularly expensive,” “notably slow” and “very verbose.”

Two notes on reading those cost rows. The per-task figure is the comparable one: AA runs a different number of Index tasks per model (roughly 1,630 for Astra against 1,240 for Opus 5), so the whole-suite totals are correct as absolute numbers for each model but their ratio is not a like-for-like comparison. Measured per task, Astra costs 44% less than Opus 5 to run the index; measured on suite totals it looks like 27%. And both models carry the (Max) effort variant here, which is what AA's own model pages display — the lower-effort variants score 52.39 and 49.68 respectively, so the ordering holds either way as long as you compare like tiers.

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 Claude Opus 5 still Anthropic's recommended model?

No, and for anyone making a decision today this outranks every benchmark on this page. Anthropic released Claude Opus 5.5 on 22 September 2026 and moved Opus 5 to legacy status. Opus 5's model page now opens with: “Although Claude Opus 5 is still available, you should consider migrating to Claude Opus 5.5 for improved performance.” Artificial Analysis goes further, stating the model is deprecated and that it only continues benchmarking the default 10k-input-token workload.

The replacement is cheaper and stronger, which is not the usual trade. Opus 5.5 lists at $4/$20 per million tokens — below Opus 5's $5/$25 and 60% below Astra's $10/$50 — and scores 57.62 on Intelligence Index v4.3.2, ranked #1 of 224 models, nearly five points clear of Astra. It keeps the 1M context window, the 128K max output and the absence of any long-context surcharge, and its retirement commitment runs to 22 September 2027 against Opus 5's 24 July 2027.

One honest catch, because cheaper rates do not guarantee a cheaper invoice: Opus 5.5 is extraordinarily verbose. Artificial Analysis needed 260M output tokens to run the index on it, against 140M for Opus 5 and 60M for Astra, which pushes its cost per Index task to $5.98 — higher than Astra's $3.26 despite charging 60% less per token. It is also the slowest of the three to start, at 756 seconds to first token at Max effort.

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.

And here is the number that undercuts the whole 2x framing. When Artificial Analysis actually ran its index, Astra cost $3.26 per task against Opus 5's $5.86 — 44% cheaper, despite charging double per token. The reason is token efficiency: Opus 5 at Max effort emitted 140M output tokens to Astra's 60M, roughly 2.3x as many, against an across-the-field median of 81M. Reasoning tokens bill as output tokens on both platforms, so a model that thinks more concisely can undercut a rival with better sticker pricing — and on this suite Astra does, by a wide margin.

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 0813 at 96.4% and GPT-5.6 Sol at 96.2%. 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, and it runs the other way from the index. On Intelligence Index v4.3.2 Artificial Analysis measured Opus 5 (Max) at 56.0 output tokens per second with a 59-second median time to first token, against Astra (Max) at 60.0 tok/s and 288 seconds to first token. At maximum effort Astra takes nearly five times as long to say anything, which you feel immediately in a live editor loop. For reference, Claude Opus 5.5 at Max is slower still to start — 756 seconds — though it streams fastest once it does, at 90.8 tok/s. If latency is the binding constraint, drop the effort level or use a fast mode rather than reading the index rankings.

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.

Yes — and this has changed since launch week. At release OpenAI staged Astra to a limited set of organisations with enterprise API access running through a Trusted Access Program. Its API model reference now lists no access restriction, and checking OpenRouter's live model API on 5 October 2026 returns openai/gpt-6-astra at $10/$50, a batch variant at $5/$25, and a separate gpt-6-astra-pro entry. The access argument against Astra no longer holds.

Opus 5 has been generally available since 24 July across the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS, with a retirement commitment of no sooner than 24 July 2027. But its status is now “Active (legacy)” rather than current. Claude Opus 5.5 is the model on the same five platforms with the longer commitment, to 22 September 2027.

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 verbose, and that is the expensive part: 140M output tokens to Astra's 60M on the same suite, which is why it costs 44% more per Index task despite half the sticker rate. Anthropic's newer tokenizer inflating token counts by roughly 30% relative to earlier Claude models compounds this. It has no published equivalent to Astra's computer-use results. And the structural weakness is now lifecycle rather than performance: it is a legacy model, Anthropic's docs point you to Opus 5.5, and Artificial Analysis marks it deprecated and benchmarks only its default workload. Its one clear remaining edge is responsiveness — 59 seconds to first token against Astra's 288.

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.

Start a new build on Claude Opus 5.5, not on either model in this comparison. Anthropic's model-selection documentation now reads: “If you're unsure which model to use, start with Claude Opus 5.5 for most workloads.” It is $4/$20 — cheaper per token than Opus 5 and 60% below Astra — it tops Intelligence Index v4.3.2 at 57.62, and it carries a retirement commitment out to 22 September 2027 rather than Opus 5's July 2027.

Stay on Claude Opus 5 only if you are already shipping on it and it passes your evals. It is still active, still supported on all five platforms, and it is the quickest of these three to first token at maximum effort. But it is a legacy model, Anthropic tells you to migrate, and Artificial Analysis has stopped benchmarking it beyond the default workload — so it is a hold, not a choice.

Choose 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 — or when cost per completed task matters more than cost per token, since it runs the index 44% cheaper per task than Opus 5. The case against it is narrower than it was: the 2x rate only really bites on long context, where the 272K repricing pushes it to roughly 3.8x.

If you want the fuller picture on either model, we maintain a Claude Opus 5.5 complete guide covering the model that supersedes Opus 5, a three-way comparison of Opus 5.5, Astra and Grok 4.7, 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?

On the current independent same-harness index, yes. Artificial Analysis Intelligence Index v4.3.2 (October 2026) scores GPT-6 Astra (Max) at 52.67 and Claude Opus 5 (Max) at 50.78, ranking them #7 and #16 of 224 models. Earlier coverage had Opus 5 ahead, but that used index v4.1.1, which AA has since rebased — there is no conversion between the two. Astra also costs less per Index task. Opus 5 still leads Vals AI's archived SWE-bench Verified board at 97.0%.

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

On rates the 2x gap is real: it holds at list price, on cached reads and on batch, and widens to roughly 3.8x above 272K input tokens. On actual spend it inverts. Astra used 60M output tokens to Opus 5's 140M on Artificial Analysis's Intelligence Index v4.3.2, which made it 44% cheaper per Index task despite double the sticker rate. Astra also scores higher on that index. The sticker ratio and the invoice ratio point in opposite directions here, so measure your own cost per completed task.

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, where it still ranks first of 86 models evaluated. Neither GPT-6 Astra nor Claude Opus 5.5 appears, because Vals archived the benchmark 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. Astra leads Opus 5 on Artificial Analysis's broader Intelligence Index v4.3.2, 52.67 to 50.78.

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 a new agent, Claude Opus 5.5 — it tops Intelligence Index v4.3.2 at 57.62, costs $4/$20, and has no long-context surcharge. Among the two models compared here: Claude Opus 5 for long-context agent loops, because Astra reprices the entire request above 272K input tokens and runs roughly 3.8x on a 500K-token pass. GPT-6 Astra for GUI and computer-use agents, where it scored 72.6% on OSWorld 2.0 and completed tasks roughly 47% faster than GPT-5.6 Sol.

Should I switch from Claude Opus 5?

You should plan to move off Claude Opus 5, but probably not to GPT-6 Astra. Opus 5 is a legacy model as of 22 September 2026 and Anthropic's own docs say to consider migrating to Claude Opus 5.5, which is cheaper at $4/$20 and ranks #1 of 224 on Intelligence Index v4.3.2 at 57.62. Astra beats Opus 5 on that index and costs less per completed task, so it is a legitimate option if you need its computer-use or terminal strengths — but it is not the cheap default.

Is Claude Opus 5 deprecated?

Anthropic classifies it as “Active (legacy)”: still available on all five platforms, with retirement no sooner than 24 July 2027, but superseded by Claude Opus 5.5 and flagged for migration in the docs. Artificial Analysis uses the stronger word, stating that the model is deprecated and that it only continues benchmarking the default 10k-input-token workload. Treat it as safe to keep running and wrong to start on.

Which model has the best cost per completed task?

Of these three, GPT-6 Astra: $3.26 per Artificial Analysis Index task on v4.3.2, against $5.86 for Claude Opus 5 and $5.98 for Claude Opus 5.5. Sticker rates run the other way — $10/$50 for Astra, $5/$25 for Opus 5, $4/$20 for Opus 5.5 — because the Claude models emit far more reasoning tokens. Compare per task, not per token.

The bottom line

This comparison has aged in an unusual way. Astra charges twice Opus 5's rate and still reprices the whole request nearly four times over once you cross 272K input tokens — those facts have not moved. What moved is everything they were weighed against. On Intelligence Index v4.3.2 Astra is ahead, not behind. Per completed Index task it is 44% cheaper, not 2x dearer. It is on OpenRouter rather than behind Trusted Access. And Opus 5 is a legacy model Anthropic is steering people away from.

The honest verdict: if you are starting now, neither — go to Claude Opus 5.5, which tops the same index at 57.62 and costs $4/$20. If you are already on Opus 5, it is a fine hold and the fastest of the three to first token, but it is a hold. If your workload is computer-use, long-horizon terminal work or security research, Astra has earned a real evaluation on cost per completed task rather than cost per token — that is where its 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.