GPT-6 Astra vs Claude Opus 5: Benchmarks, Cost and Real Tests

Pick between these two on sticker price and you will pick wrong. GPT-6 Astra lists at twice Claude Opus 5 per token, so the obvious read is that Astra is the premium tier and Opus 5 is the value play. Then you run the same jobs through both and the bill comes out almost level, because the cheaper model talks more. Per-token price and per-task price are different numbers, and the gap between them is where the real decision lives.

This comparison puts GPT-6 Astra vs Claude Opus 5 on specs, published benchmarks, and three tasks we ran through both models plus Claude Sonnet 5 as the value reference. It covers what each model costs in practice, how fast it answers, whether the code it writes actually passes a test suite, and which one to reach for by workload. Every figure we measured came from running gpt-6-astra, claude-opus-5, and claude-sonnet-5 through the same OpenRouter endpoint in September 2026, with costs normalized to each vendor’s standard list price.

The short version: on ordinary work the two flagships are hard to tell apart on quality, close on cost, and both beaten on value by Sonnet 5. Astra earns its premium on a narrow band of tasks, and outside that band it is the expensive habit.

GPT-6 Astra vs Claude Opus 5 at a glance

Both are single dense reasoning models with a million-token class context window and adjustable thinking effort. The specs that move a buying decision are the price per token and the knowledge cutoff. Sonnet 5 sits in the table because it is the cheaper Claude most teams should compare against before paying for either flagship.

The one line to take from this table: Astra’s $50 output rate, straight off OpenAI’s rate card, is 2x Opus 5 and 5x Sonnet 5 on the tokens that dominate any reasoning workload. That multiplier is the number the rest of this guide tests. Full specs for each model live in our GPT-6 Astra teardown and the Claude Opus 5 deep dive.

What the benchmarks claim, and where they disagree

OpenAI published Astra against Opus 5 on several boards. Read these as the vendor’s own numbers, because they are. The pattern is consistent: Astra is ahead on agentic, computer-use, and math work, and the margins on frontier math and the ARC adapter harness are large.

Now the honest part. That ARC-AGI-3 row is where the two vendors openly contradict each other. OpenAI’s table shows Astra at 99.9% and Opus 5 at 30.2%. Anthropic’s own Opus 5 launch claimed an ARC-AGI-3 result three times the next best model. Both cannot be the leader, and the difference is the harness each side ran. Treat any single ARC number as a marketing artifact until someone reproduces it on a fixed harness.

One aggregate keeps the whole thing grounded. On the Artificial Analysis Intelligence Index, a broad cross-domain average, Astra scores 61.2, and OpenAI’s own comparison table places it behind Opus 5 and Claude Fable 5.1 on that same aggregate. So Astra leads the specialized boards it was tuned for and trails on general intelligence. Anthropic, for its part, leads Opus 5 with agentic coding and adds one caveat worth repeating: on the OSS-Fuzz security evaluations Opus 5 finds vulnerabilities well but stays weak at writing working exploits, which is the opposite of Astra’s headline cyber result. The takeaway is not that one model wins. It is that the winner changes with the task, so the only benchmark that matters is your own.

How we tested both

We handed all three models the same three prompts through OpenRouter and measured latency, token counts, and cost on each. The debugging prompt ran at high reasoning effort, the coding prompt at medium, and the quick prompt at low, matching how each job would run in a real integration. Costs are computed from the measured token counts at each vendor’s current published list price, so they reflect what you would pay on the API today. One recent change is baked in: Anthropic cancelled the September increase for Sonnet 5, so its launch rate of $2 and $10 per million is now the permanent standard, and that is what we bill it at here.

The debugging test: Opus 5 dug deeper, Astra stayed tighter

The prompt was a real production symptom: a FastAPI service using httpx and asyncpg leaks file descriptors under load until it hits EMFILE. Name the three most likely root causes for that stack and give one exact Linux command to confirm each. No preamble.

All three named the same core culprits: an httpx.AsyncClient created per request and never closed, and asyncpg connections or pools that are never released. Astra was the most disciplined about scope, ending with an explicit note that the commands confirm socket accumulation, not the Python root cause, and that distinguishing a leaked client from a leaked streaming response needs code inspection. That kind of self-limiting answer is exactly what you want from a model doing production triage.

Opus 5 spent its extra 1,758 tokens on something concrete. It caught a third cause the compact answers underweighted: event-loop churn from asyncio.run() or new_event_loop() inside sync endpoints, which leaks an epoll descriptor plus its eventfd per call and stays invisible to ss because those are not sockets. It then added a triage step that buckets /proc/PID/fd by type to catch the case where none of the three match, including a note that a growing pile of deleted temp files means unclosed spooled uploads. That is the answer a senior engineer would write. Sonnet 5 reached the same epoll and eventfd point in a quarter of the words and an eighth of the cost, which is its whole pitch in one data point.

The coding test: all three passed, the bill did not

The coding prompt is where a comparison usually separates models, so we made it verifiable. We asked each model to implement a Go-style duration parser: parse_duration(s) returning total milliseconds truncated toward zero, handling hours through nanoseconds, decimals, a leading minus, concatenated segments like 2h45m30s, and raising ValueError on empty input, unknown units, or a bare number. Then we ran every implementation through the same thirteen-case suite, including the traps: 250us must floor to 0, 1500us to 1, and 1m30 must fail because the trailing number has no unit.

Every model passed all thirteen cases. On a well-specified task with clear edge cases, correctness was a wash across the two flagships and Sonnet 5 alike. What separated them was style and spend. Astra wrote the tightest implementation at 49 lines and $0.035. Opus 5 wrote the most defensive one at 92 lines and $0.046, spending its tokens on explicit validation branches. Sonnet 5 landed in the middle on length and came in cheapest at $0.012.

If your coding tasks look like this one, well-scoped with a clear spec, you are paying Astra’s premium for output that a model at a fifth of the price gets right too. The premium only starts to justify itself on the harder, longer, more ambiguous work the benchmarks point at, and that is the work you should be measuring on your own prompts before you commit.

The pricing story flips when you measure per task

Add a fast, low-effort prompt to the two above and total the run, and the sticker-price intuition breaks. Across all three tasks Opus 5 cost more than Astra, despite listing at half the price per token, because it emitted 2.1 times as many tokens to do the same work.

This is the number most launch coverage misses. A model’s list price tells you what a token costs, not what an answer costs, and the two diverge whenever one model reasons more verbosely than another. Opus 5 is the more thorough model on hard prompts, which is a feature, but it means the half-price sticker does not translate into a half-price invoice. On this battery it translated into a slightly higher one. Astra, by reasoning more compactly, partly buys back its own premium. Sonnet 5 ended the run at a sixth of either flagship’s cost and half the wall-clock, which is why it belongs in every comparison as the baseline the expensive models have to beat.

Does prompt caching close the price gap?

No. Both vendors discount cached input reads to roughly a tenth of the standard input rate, so caching helps each model by the same ratio and the 2x absolute gap between Astra and Opus 5 survives. Astra reads cached input at $1.00 against its $10.00 standard rate; Opus 5, per Anthropic’s rate card, reads at $0.50 against $5.00. Same discount shape, half the money on the Anthropic side.

The catch that matters more: output tokens are never cached. Caching only touches the prompt you send, so it pays off when you resend a long stable context with short answers, and does almost nothing on the output-heavy reasoning work where Astra’s $50 output rate does the damage. We measured Astra’s caching cutting a warm call by about 92% in the Astra launch testing, but that was a deliberately input-heavy call. On a normal reasoning turn, most of your bill is output, and no cache touches it. If your workload is a big fixed system prompt with terse replies, caching narrows the gap. If it is open-ended reasoning, it does not.

Which model should you actually run?

Start with Sonnet 5 and make the flagships prove they are worth the jump. On the ordinary coding, config, and triage work that fills most days, it matched both flagships on correctness at a sixth of the cost and the fastest response of the three. For a lot of teams the honest answer to GPT-6 Astra vs Claude Opus 5 is that neither is the right default, and Claude Sonnet 5 is.

Reach for Opus 5 when the task is genuinely hard: long autonomous agent runs, ambiguous debugging where an extra pass of reasoning earns its tokens, or complex multi-file coding. It was the most thorough model in our debugging test and its extra verbosity is buying real coverage, not filler. Because it lists at half Astra’s output rate, it is the cheaper flagship on any output-heavy job even when it writes more.

Reach for Astra when your work sits squarely on its strengths: computer-use and GUI agents, the hardest frontier math, and security evaluation, the boards where OpenAI’s own numbers show a real gap. Its compact reasoning also makes it the faster and sometimes cheaper choice on short, high-effort prompts. Outside that band you are paying twice the token price for output another model gets right. Whichever way you lean, run your own prompts through both before you commit, because the only comparison that predicts your invoice is the one on your workload. If you drive either model from the terminal, both drop into Claude Code the same way, and if you would rather keep the spend in-house, our open-source LLM comparison covers the self-hosted alternatives.