GPT-6 Astra Benchmarks: Is It Better Than GPT-5.6?

The GPT-6 Astra benchmarks tell two different stories at the same time. OpenAI reports near-perfect results: 99.9% on ARC-AGI-3, 98% on FrontierMath Tier 4 and 100% on ExploitBench. Independent testing by Artificial Analysis is far more measured, putting Astra level with Claude Opus 5 on coding, tied with its own predecessor GPT-5.6 Sol on general intelligence, and roughly 75% more expensive per task. Whether Astra is better than GPT-5.6 depends almost entirely on what you plan to do with it.

If the model itself is new to you, our GPT-6 Astra explained guide covers the 3 September 2026 launch, the rollout and the specifications. This post is only about the numbers, and about whether they justify paying 2.5 times more per token.

What do OpenAI’s own GPT-6 Astra benchmarks claim?

OpenAI’s GPT-6 Astra announcement reports a near-clean sweep: 99.9% on ARC-AGI-3 with human parity on 96% of levels, 98% on FrontierMath Tier 4, and 100% on ExploitBench against 78.5% for GPT-5.6 Sol. It also claims computer use tasks complete 47% faster than the previous generation, and 0% circumvention attempts in alignment testing.

The ExploitBench result comes with a warning. OpenAI says Astra reached its “critical” cybersecurity capability threshold, meaning it can find previously unknown vulnerabilities without human guidance, and it ships with safeguards restricting it to defensive use. CNBC covered the cyber capability warnings in detail.

What do ARC-AGI-3, FrontierMath and ExploitBench actually measure?

These three tests measure very different things and none looks like a normal exam. ARC-AGI-3 tests learning inside interactive environments, FrontierMath Tier 4 tests research-grade mathematics, and ExploitBench tests how far a model climbs a real software exploitation ladder. A high score means something different in each case.

ARC-AGI-3 is the first interactive reasoning benchmark from the ARC Prize. Instead of static puzzles, it drops an agent into an unfamiliar environment with no instructions and measures how efficiently it works out the goal and adapts. 100% means matching human learning efficiency across all environments.

FrontierMath Tier 4 is the top tier of Epoch AI’s mathematics benchmark: research-level maths, made of unpublished problems written by working mathematicians, some of them open research questions. Answering these is not pattern-matching from a textbook.

ExploitBench scores cybersecurity agents on a four-rung capability ladder: reaching vulnerable code, triggering the bug, building exploit primitives, and achieving arbitrary code execution, tested against the Chromium V8 engine. A 100% score describes offensive capability, not safety.

What do independent GPT-6 Astra benchmarks show?

Artificial Analysis, which runs its own harnesses rather than reporting vendor figures, found a much narrower gap. Astra scores 67 on the Coding Agent Index and 61 on the Intelligence Index. The first ties Claude Opus 5. The second ties GPT-5.6 Sol exactly, and sits five points behind Claude Fable 5.1.

That is the tension at the centre of this launch. On benchmarks OpenAI selected, Astra is close to perfect. On a broad composite index run by an outside lab, general intelligence did not move.

Sources: OpenAI’s GPT-6 Astra announcement and Artificial Analysis’s independent benchmarking, as of 5 September 2026.

Is GPT-6 Astra better than GPT-5.6 at coding?

Coding is Astra’s clearest and least disputed win. Artificial Analysis puts it two points above GPT-5.6 Sol at max effort on the Coding Agent Index while costing about the same per task, because it is roughly 70% more token-efficient on coding work. Same money, better output.

The efficiency figure is the part to dwell on. Artificial Analysis reports Astra using about a third of the tokens GPT-5.6 Sol needs at max effort, and about a fifth of what Claude Opus 5 uses at xhigh effort, for comparable results. In an agentic loop running for hours, token count determines your bill and your latency.

OpenAI’s framing matches this: production-quality code needing fewer iterations. If your workload is agentic coding, the price rise per token is largely cancelled out by the drop in tokens consumed.

How good is GPT-6 Astra at reasoning and maths?

On OpenAI’s chosen maths and reasoning tests, exceptional. On broad independent composites, flat. Astra reports 98% on FrontierMath Tier 4 and 99.9% on ARC-AGI-3, yet scores 61 on the Artificial Analysis Intelligence Index, identical to GPT-5.6 Sol. It did gain 6 points on Humanity’s Last Exam.

There is a plausible explanation. Astra was trained on OpenAI’s largest run to date, using over 100,000 GPUs, and much of that capability shows up in long-horizon agentic work rather than single-turn answering. The ~80 Elo gain on AA-Briefcase, which simulates multi-week knowledge work, supports that reading.

That gap between saturated maths benchmarks and a flat general index sits at the heart of the argument over whether Astra is a step change at all, which we examine in our piece on whether GPT-6 Astra is AGI.

Why does the hallucination drop from 92% to 51% matter more than the headline scores?

Because hallucination rate decides whether you can use a model’s output without checking it. Artificial Analysis found Astra’s hallucination rate on its knowledge test fell from 92% to 51% versus GPT-5.6 Sol, alongside a 4-point accuracy gain. That nearly halves the volume of confident wrong answers.

A 99.9% score on a puzzle set does not change your working day. Cutting fabricated answers roughly in half does, because verification time is usually the real cost of using a model for research or analysis.

Two caveats keep this honest. 51% is still high in absolute terms: on this test, around half the answers the model should have declined are still fabrications. And Artificial Analysis notes that while Astra’s analytical quality improved on long-horizon work, its presentation quality went down.

Where did GPT-6 Astra go backwards?

Astra regressed on several benchmarks compared with GPT-5.6 Sol. The largest is an approximately 80 Elo point decline on GDPval-AA v2, which tests economically valuable professional tasks. Artificial Analysis also recorded modest declines on customer support (τ³-Banking), scientific coding (SciCode) and long-context reasoning (AA-LCR).

That decline is the mirror image of the AA-Briefcase gain. Astra got better at long, multi-step projects and worse at the discrete professional tasks that make up most day-to-day commercial work.

For anyone running high-volume customer support or scientific code, a newer and pricier model that scores lower is reason enough to stay put. Our breakdown of GPT-6 Astra use cases covers which workloads suit it.

Why does cost per task matter more than cost per token?

You pay for tokens but you buy outcomes. Astra costs $10 per million input tokens and $50 per million output tokens against $4 and $20 for GPT-5.6 Sol, a 2.5x increase per token. But Artificial Analysis measured it at around 75% more expensive per completed task, since it uses about 10% fewer output tokens.

Those two multipliers pull in different directions depending on the job. On coding, the token efficiency gain is large enough that cost per task comes out roughly level with GPT-5.6 Sol. On general reasoning, where Astra saves only about 10% of output tokens, you pay close to the full price increase for a score that did not move.

Anyone who has costed a software platform will recognise the pattern, because it is the same distinction that separates a sticker price from what you actually spend over a year. The reasoning is set out in our guide to LMS pricing models and total cost of ownership, and it transfers directly to model selection: per-unit rates matter far less than the volume of units a real workload consumes.

Prompt caching at $1 per million input tokens is the lever most teams underuse. With a 1,050,000-token context window and a 30-minute cache TTL, a long session that re-reads the same repository can shift much of its input spend to the cached rate.

Astra vs Claude: how do they compare?

On coding, Astra ties Claude Opus 5 at 67 and trails Claude Fable 5.1 at 70. On general intelligence, Astra’s 61 sits five points behind Fable 5.1. Astra wins clearly on token efficiency, using roughly a fifth of the tokens Claude Opus 5 needs at xhigh effort for comparable coding results.

That advantage translates into real money. Artificial Analysis puts Astra at less than half the cost of Claude Fable 5 for equivalent coding performance, a strong argument for Astra on high-volume agentic work even though it does not top the leaderboard.

The honest summary of astra vs claude as of 5 September 2026: Claude Fable 5.1 leads on raw scores in both indices, while Astra is cheaper to run for the same coding output.

Should you switch to GPT-6 Astra? A verdict by use case

It depends on the workload, and for some the answer is no. Agentic coding and long research projects justify the upgrade. High-volume customer support and everyday chat do not, on the evidence available as of 5 September 2026.

A verdict table is a starting position, not a decision. Scoring a model the way procurement teams score software gives you something defensible: weight the criteria that matter to you, test against them, and record the result. The method in our practical vendor evaluation checklist works unchanged here, and the prompts in our buyer guide to vendor questions are a useful reminder to ask about rate limits, data residency and deprecation dates before you migrate anything.

Computer use is where the independent picture is thinnest. OpenAI’s 47% speed claim has not been checked by an outside lab, and the capability raises real supervision questions, covered in our guide to OpenAI Astra computer use and its risks.

Conclusion

Run the comparison yourself before committing budget. Take five to ten real tasks from your workflow, run them through both GPT-6 Astra and GPT-5.6 Sol at the same reasoning effort, and record quality, total tokens consumed and wall-clock time for each. That gives you your own cost per task, the only figure that reflects your work rather than someone else’s benchmark suite.

Two settings will change your results, so fix them first. Set reasoning.effort deliberately rather than accepting the default, and enable prompt caching with prompt_cache_options.ttl set to 30m if your tasks reuse context. OpenAI’s model reference for GPT-6 Astra lists the current parameters, and note that temperature and top_p have been removed.

If Astra wins on your tasks, keep it. If it ties, the cheaper model is the better decision.