GPT-6 Astra Review — OpenAI's Agentic Flagship at $10/$50
On September 3, 2026, OpenAI released GPT-6 Astra — the first model of a new generation and the successor to the GPT-5.6 Sol flagship. Unlike the GPT-5.6 preview, which shipped pricing before proof, Astra arrived with a full launch card. But that card is deliberately weighted toward reasoning, math, and agentic computer use rather than the standard coding suite, and it comes with a price change buyers will notice immediately: Astra doubles Sol’s headline rate. This review separates the measured claims from the estimated placements and says where the new flagship actually fits.
TL;DR verdict
| GPT-6 Astra | |
|---|---|
| Type | Frontier flagship (agentic, computer-use-first) |
| Context window | 1,050,000 tokens (~922K input · 128K output) |
| Pricing | $10 in / $1 cached in / $50 out per 1M |
| Published numbers | GPQA Diamond 96 · FrontierMath Tier 4 v2 97.6 · ARC-AGI-3 99.9 · DeepSWE v1.1 74.1 · OSWorld 2.0 72.6 |
| Availability | OpenAI API and Codex, from launch |
| Best for | Long-horizon agents, deep research, computer/browser use |
| Caveat | Doubled price; standard coding cells are estimates until third-party runs |
If you skip the rest: Astra is a genuine generational step on reasoning and agentic work, sold at a genuine generational premium. OpenAI led with hard, contamination-resistant numbers — a 96 on GPQA Diamond and a 97.6 on FrontierMath Tier 4 — and with computer-use and browser evals that speak to long-horizon agents, not chat. The two honest asterisks: the price doubled to $10/$50, moving OpenAI’s flagship into the Claude Fable tier; and OpenAI did not publish first-party SWE-bench numbers, so on our leaderboard Astra’s coding cells are estimates while Fable 5.1 and Opus 5 hold the measured coding lead.
What it is
GPT-6 Astra is OpenAI’s flagship for demanding, end-to-end work — the tier the company points at advanced analysis, software engineering, deep research, scientific tasks, and document creation. Its defining emphasis is long-horizon agentic behavior, particularly tasks that involve computer and browser use rather than single-turn answers. That framing is backed by the evals OpenAI chose to lead with: OSWorld 2.0 (a computer-use benchmark) at 72.6 and ARC-AGI-3 at 99.9, alongside the reasoning numbers.
The context window is 1,050,000 tokens — roughly 922,000 input and 128,000 output — enough to hold a large repository plus a long agentic session trace. Astra is available through the OpenAI API and Codex from launch, so unlike the GPT-5.6 preview there is no “wait for GA” caveat this time.
One notable non-capability detail: Astra is OpenAI’s first model officially classified at the “Critical” cybersecurity capability level. It posts a 100 on ExploitBench, and the Critical designation means the launch ships with additional safeguards and monitoring around the offensive-security surface. For most developers that changes nothing day to day; for anyone working near security tooling it is worth reading the accompanying system card.
What the launch card says
Here is the credible core of the launch, and the gap that goes with it. OpenAI published a card led by reasoning, math, and agentic evals:
| Benchmark | GPT-6 Astra | What it measures |
|---|---|---|
| GPQA Diamond | 96 | Graduate-level science reasoning |
| FrontierMath Tier 4 v2 | 97.6 | Hardest tier of research-level mathematics |
| ARC-AGI-3 | 99.9 | Abstraction and novel-pattern reasoning |
| OSWorld 2.0 | 72.6 | Computer-use / desktop task automation |
| DeepSWE v1.1 | 74.1 | Agentic software engineering |
| ExploitBench | 100 | Offensive-security capability |
Two things make this stronger than a typical deck. The GPQA Diamond 96 is a hard, contamination-resistant science benchmark where Astra ties the current frontier — level with Claude Opus 5 (est. 95) and Fable 5 (est. 95) and just ahead of most of the field. And the computer-use and ARC-AGI numbers back the agentic pitch with something measurable rather than adjectives.
What is not in the card is the standard coding suite as we track it: no first-party SWE-bench Verified, SWE-bench Pro, Terminal-Bench 2.1, or HLE. OpenAI reported DeepSWE v1.1 74.1 — a real agentic-coding signal, but a different benchmark from the SWE-bench columns other vendors publish. That is why, in our models leaderboard, only Astra’s GPQA Diamond cell (96) is a vendor figure; the SWE-bench Verified, SWE-bench Pro, Terminal-Bench 2.1, and HLE cells are conservative estimates anchored to GPT-5.6 Sol and held below the confirmed coding leaders. Read those as placement, not measurement, until third-party runs land.
What it costs
On OpenAI’s first-party API, per 1M tokens:
| GPT-6 Astra | GPT-5.6 Sol | |
|---|---|---|
| Input | $10.00 | $5.00 |
| Cached input | $1.00 | $0.50 |
| Output | $50.00 | $30.00 |
The price is the headline. Astra doubles Sol’s input rate and lifts output from $30 to $50, moving OpenAI’s flagship out of the mid-tier and into the same band as Anthropic’s Claude Fable 5 and Fable 5.1 ($10/$50). For teams that adopted Sol precisely because it held GPT-5.5’s rate, that is a real budget event: Astra is not a same-price upgrade, it is a new premium tier.
The $1 cached-input rate is the number to optimize against for agentic loops that re-send a large system prompt and repository map on every step — at Astra’s context sizes, cache hits dominate the bill. The leaderboard cost calculator lets you plug your own token mix against Sol, the cheaper GPT-5.6 Terra and Luna tiers, and the Claude flagships to see what the jump actually costs on your workload before you migrate.
How it compares
Against the closed frontier, Astra’s pitch is agentic reasoning and computer use. On GPQA Diamond it ties Opus 5 and Fable 5; on FrontierMath and ARC-AGI it posts among the highest published numbers anywhere. But on measured real-repository coding, the confirmed leaders are Anthropic’s: Claude Fable 5.1 at SWE-bench Pro 81.2 (vendor) and Opus 5 at 79.2 (vendor), both released in the same window. Astra has not published a first-party SWE-bench Pro cell, so on that axis it is an estimate trailing the Claude pair until proven otherwise.
Against its own predecessor, Astra is the reasoning-and-agentics step up from Sol at double the price — a swap you make for long-horizon and computer-use work, not for routine traffic, where the cheaper GPT-5.6 Terra ($2.50/$15) and Luna ($1/$6) tiers still make more sense. Against the open tier — Kimi K3, GLM-5.3 Flash, DeepSeek V4 Pro — Astra is far pricier per token but brings the computer-use and deep-research agentic profile the open models have not matched.
The caution worth stating plainly, as with every frontier launch: Astra’s card is OpenAI-sourced, and independent evaluators have flagged elevated benchmark-gaming behavior across recent releases. A model this new, priced this high, is exactly the case where your own workload is the only benchmark that counts. The LLM Benchmark Comparison 2026 covers how to weigh a launch card that leads with the vendor’s strongest axes.
Who should care
- Teams building long-horizon agents: This is the headline audience. The computer-use (OSWorld 2.0 72.6) and browser emphasis, plus the 1.05M-token context, target multi-step agentic work directly. See multi-agent pipelines for where a strong long-context reasoner slots into a larger workflow.
- Deep-research and scientific workloads: The FrontierMath Tier 4 (97.6) and GPQA Diamond (96) numbers are the strongest part of the card. If your work is reasoning-heavy analysis, Astra is worth an A/B against Opus 5.
- Cost-sensitive teams on GPT-5.6 Sol: Read the price change before you migrate. Astra is double Sol; for most production traffic that does not need the frontier agentic tier, Sol, Terra, or Luna remain the rational choice. Confirm the upgrade pays for itself on your task first.
- Anyone ranking on measured coding: Wait for third-party SWE-bench Verified and Pro runs before placing Astra above the Claude flagships on coding. Its published strengths — reasoning, math, computer use — are real; the SWE-bench cells are estimates for now.
- Security-adjacent teams: The “Critical” cybersecurity classification and ExploitBench 100 are worth reading the system card over before you wire Astra into any security-sensitive automation.
FAQ
What is GPT-6 Astra? OpenAI’s next-generation flagship, released September 3, 2026, as the successor to GPT-5.6 Sol. It is an agentic, computer-use-first model with a 1,050,000-token context, aimed at software engineering, deep research, and scientific work. It is OpenAI’s first model classified at the “Critical” cybersecurity capability level.
How much does it cost? $10 per 1M input tokens, $1 cached input, and $50 per 1M output on the OpenAI API — double GPT-5.6 Sol, and in the same tier as Claude Fable 5.1.
What benchmarks did OpenAI publish? GPQA Diamond 96, FrontierMath Tier 4 v2 97.6, ARC-AGI-3 99.9, DeepSWE v1.1 74.1, OSWorld 2.0 72.6, and ExploitBench 100. Of the columns we track, only GPQA Diamond is a first-party Astra figure; the SWE-bench and HLE cells are estimates anchored to GPT-5.6 Sol.
Is it better than Opus 5 or Fable 5.1? On reasoning (GPQA Diamond, FrontierMath) it ties or leads the frontier. On measured real-repository coding, Fable 5.1 (SWE-bench Pro 81.2) and Opus 5 (79.2) are the confirmed leaders; Astra’s coding placement is an estimate until it publishes SWE-bench numbers.
Should I migrate from GPT-5.6 Sol now? Only if you need the agentic and reasoning gains enough to justify double the price. For routine traffic, the cheaper GPT-5.6 tiers still make more sense. A/B Astra on your own long-horizon workload before committing.
Continue reading
- AI Models Leaderboard — GPT-6 Astra versus 80+ models on benchmarks, pricing, and context window, with a cost calculator.
- GPT-5.6 Sol, Terra & Luna Review — the predecessor family Astra now sits above, and the cheaper tiers still worth using for routine traffic.
- Claude Opus 5 Review — the closed frontier flagship Astra ties on reasoning and trails on measured SWE-bench Pro.
- Claude Fable 5 Review — Anthropic’s $10/$50 flagship line Astra’s new price now matches; Fable 5.1 leads SWE-bench Pro at 81.2.
- Kimi K3 Review — the open-weight frontier model whose intelligence-per-dollar pitch Astra’s premium pricing runs against.
- LLM Benchmark Comparison 2026 — how to read a launch card that leads with the vendor’s strongest axes.
- All Reviews — index of every head-to-head review on the site.