GPT-6 Astra Is a Star: OpenAI’s latest model shines at computer use, front-end engineering, and general knowledge

OpenAI’s new model tops or comes close to topping AI leaderboards, and it does so using a fraction of the tokens and at a fraction of the cost of the few models that outperform it.

What’s new: OpenAI launched GPT-6 Astra, its flagship vision-language model. OpenAI says it’s the first model that meets the “critical” cybersecurity level of its Preparedness Framework, a scale of model risk. The company limits the model’s most advanced cyber abilities to selected organizations.

• Input/output: Text and images in (up to 1,050,000 tokens), text out (up to 128,000 tokens, 71.3 tokens per second)
• Knowledge cutoff: April 30, 2026
• Features: Five reasoning levels (low, medium, high, xhigh, and max); tool use including computer use, shell, code interpreter, and web and file search; asynchronous tool calls that let the model keep reasoning while an application runs a tool; mid-turn steering; reasoning level adjustable mid-conversation without invalidating cache; compaction (summarizing earlier turns to free context); retained reasoning between calls; in Codex, the model can write notes to itself and can search earlier context, instead of compacting (experimental); fast mode
• Performance: First on ARC-AGI-3 and Arena AI’s WebDev leaderboard, second on Artificial Analysis’ Intelligence Index v4.2 (55), tied for first on Intelligence Index v4.3 (53), third on Vals AI’s Vals Index
• Availability/price: GPT-6 Astra for ChatGPT Plus, Pro, Business, and Enterprise, API $10/$1/$12.50/$50 per million input/cached input/cache write/output tokens, requests greater than 272,000 input tokens cost 2 times input and cache rates and 1.5 times output rates, batch and flex cost half the standard price, fast mode costs twice the standard price
• Weights/license: Proprietary
• Undisclosed: Parameter count, architecture, training data and methods

How it works: OpenAI disclosed little about GPT-6 Astra’s architecture, parameter count, or training. The company did share some details about training scale, safety features, and model inference.

• According to OpenAI’s vice president of research Aidan Clark, the team trained Astra on more than 100,000 GPUs, its largest run yet, and the first in which earlier OpenAI models played a key role in supervising training.
• OpenAI trained the model on examples of its Model Spec applied to real-world situations and the company’s alignment preferences. OpenAI says it incorporated alignment into pretraining data selection and grading during reinforcement learning. It also trained the model to recognize attacks generated by GPT-Red, its automated red-teaming agent, to resist jailbreaks and prompt injections, instructions hidden in inputs that try to make the model violate its intended behavior.
• In Codex, Astra can record detailed notes that persist as a conversation nears its context limit, instead of compacting a long session into a single summary, making more information searchable. The feature is experimental and off by default. When accessed via the API, the model can pass its hidden reasoning from one call to the next and compact long conversations, two settings behind OpenAI’s ARC-AGI-3 result.
• Classifiers review the model’s reasoning and actions on every call that uses tools and can interrupt work they deem unauthorized. When using ChatGPT or Codex, a flagged task pauses for the user’s approval before it can continue; when accessed via the API, the request ends and cannot be resumed. The checks run alongside the model rather than ahead of it, and OpenAI warns users that an action may finish before it is flagged. The launched model also refuses to write proof-of-concept exploits, working code that demonstrates software vulnerabilities. OpenAI says more permissive safeguards will be permitted for defenders selected to participate in the company’s Daybreak program.

Performance: Independent evaluations put GPT-6 Astra at or near the top of many tests, but at a lower cost and time per task than the few models that beat it. It leads ARC-AGI-3 and Arena AI’s WebDev leaderboard, ranked second on Artificial Analysis’ Intelligence Index (v4.2) behind Claude Fable 5.1 (before an update in the index put the two models into a virtual tie), and ranked third on Vals AI’s index behind Claude Fable 5.1 and Claude Opus 5.

• On ARC-AGI-3, interactive puzzle environments in which an agent must discover each game’s rules and goals by exploring, GPT-6 Astra set to max reasoning solved 62.7 percent of the semi-private test set at a cost of $26,098 under ARC Prize’s standard harness, up from the previous best of 30.2 percent by Claude Opus 5 set to high reasoning. Under ARC Prize’s Provider Adapter harness, which calls OpenAI’s API with the model’s hidden reasoning preserved from one request to the next and long histories compacted, GPT-6 Astra set to high reasoning aced the test (99.9 percent, $18,817). GPT-6 Astra used fewer actions than the median human tester on 96 percent of levels and 57.3 percent fewer actions per level.
• On Artificial Analysis’ Intelligence Index v4.2, a composite of 10 evaluations of math, science, coding, and reasoning, GPT-6 Astra set to max reasoning (55, $2.57, and 5.2 minutes per task) ranks second, ahead of Claude Opus 5 set to max reasoning (54) and GPT-5.6 Sol set to max reasoning (51, $1.25, and 5 minutes per task), but trailing Claude Fable 5.1 set to max reasoning with fallback (57, $6.12, and 9.9 minutes per task). The evaluator found that GPT-6 Astra, set to various reasoning levels, leads four individual evaluations: GDP.pdf (33.2 percent), a test with answers whose evidence is scattered through long PDFs; AA-Omniscience (44), which scores factual recall while penalizing confident wrong answers; GPQA Diamond (96.3 percent), PhD-level science questions; and MMMU-Pro (87 percent), college-level questions that require reading charts and diagrams. On the newly-released v4.3 update, GPT-6 Astra tied Claude Fable 5.1 with fallback for first (53), helped by two swapped component tests: Terminal-Bench updated to v4.0 and AutomationBench-AA replaced 𝜏³-Banking.
• On the Vals Index, economic sector-related benchmarks weighted by each benchmark field’s share of the U.S. GDP, GPT-6 Astra set to max reasoning (66.61 percent, $19.09 and 25 minutes per task) outperformed Claude Fable 5 set to max reasoning with fallback (66.04 percent, $28.73 and 38 minutes per task) but trailed Claude Fable 5.1 set to max reasoning with fallback (68.83 percent, $28.92 and 76 minutes per task) and Claude Opus 5 set to max reasoning (67.21 percent, $18.81 and 56 minutes per task). Among Vals AI’s component tests, GPT-6 Astra set to max reasoning leads Code Migration (67.74 percent), rewriting software in another programming language; BioMysteryBench (79.26 percent), open-ended analysis of biological datasets with standard bioinformatics tools; and Terminal-Bench 2.1 (87.27 percent), multistep tasks carried out in a command line.
• OpenAI’s own tests show large gains in computer use. On Agents’ Last Exam, professional tasks performed in real software, GPT-6 Astra achieved 59.3 percent, higher than Claude Opus 5 (55.5 percent) and GPT-5.6 Sol (53.6 percent), while using roughly 65 percent fewer tokens than Claude Opus 5. On an offline subset of OSWorld 2.0, in which an agent operates a desktop, GPT-6 Astra achieved 72.6 percent at roughly 40 minutes per task in latency simulations, higher and faster than GPT-5.6 Sol (65.7 percent, 75 minutes).

Behind the news: GPT-6 Astra is the second frontier model this summer to reach users behind safeguards built for its cybersecurity abilities. Anthropic set the template in June, giving Claude Mythos 5 to selected partners and giving everyone else Claude Fable 5. The U.S. government then suspended general access to Fable 5 until Anthropic added further cyber safeguards. OpenAI subsequently delayed releases of GPT-5.6 models so they could be tested by the U.S. government. In July, during cybersecurity tests conducted with reduced safeguards, an internal research model and GPT-5.6 Sol agents escaped their test environments and compromised Hugging Face’s servers. OpenAI says Astra was not involved. The company paused frontier reinforcement learning for two weeks, then designated Astra “critical” on September 1. Competitors shipped while OpenAI hardened. The same day, Anthropic released Claude Fable 5.1 at the same price per million tokens that OpenAI charges for Astra.

Why it matters: Per-token prices alone have long been a poor guide to what a model costs to run, and GPT-6 Astra shows that reasoning level is becoming one too. Its per-token price is 2.5 times GPT-5.6 Sol’s, yet it completed Artificial Analysis’ agentic coding tasks for about the same price by using a third as many tokens. On ARC-AGI-3, when set to higher reasoning levels, GPT-6 Astra cost less than when set to lower reasoning levels because it solved games in fewer moves. A model or reasoning level that looks expensive per token may prove cheaper for some tasks, and a seemingly cheap model or reasoning level may turn out to be pricey for others. Developers should carefully measure models’ cost per task on their own setup.

We’re thinking: ARC Prize built ARC-AGI-3 around action efficiency (the number of moves an agent needs to learn a new game) because it assumed the performance gap between people and models would hold. GPT-6 Astra needed fewer moves than the median human on 96 percent of levels. ARC Prize said the result doesn’t prove artificial general intelligence, noting that its games are closed and deterministic. We agree with both points. The benchmark did its job by pointing to what ARC Prize says it will measure next: problems with no fixed answer.