GPT-6 Astra launch, capabilities, and post-launch quality issues

Astra's most viral scores came from stateful, tool-assisted evaluation runs, including a near-perfect ARC-AGI-3 result ^[6]. But on ExploitBench, a 'perfect' 100% score collapsed to 39% once the benchmark was rebuilt to exclude historical, previously-disclosed vulnerabilities ^[6]- a strong signal that some of that ceiling reflects memorized exploit data rather than genuinely novel discovery. OpenAI's own framing leaned hard into the opposite conclusion: President Greg Brockman said it was 'not unreasonable' to feel the industry has entered the AGI era ^[8]. One widely-watched developer review pushed back on that framing with a different measure entirely - an independent, non-benchmark-gamed intelligence index that placed Astra level with its own predecessor and behind a rival lab's model, despite Astra's benchmark sheet looking like a clean sweep.

Astra is the first OpenAI model to cross the company's Preparedness Framework 'Critical' cybersecurity threshold: with appropriate tools and access, it can find previously unknown security flaws and develop novel exploits across well-protected systems largely without step-by-step human guidance ^[1]. In OpenAI's own authorized-target safety test, Astra stayed within bounds 100% of the time without production safeguards, versus GPT-5.6 Sol overstepping 48% of the time ^[1]. But Greyhound Research's Sanchit Vir Gogia pushed back on the framing itself: 'Astra's capability did not change between 10 August ... and September 1 ... The testing changed. The model did not' ^[1]. He also flagged that OpenAI's own safety reporting shows decreased chain-of-thought monitorability compared with the predecessor model, meaning better behavior on paper has come with reduced ability to inspect why the model does what it does ^[1].

Independent of OpenAI's own claims, Cornell and Google DeepMind researcher Yoav Artzi ran early benchmark tests and concluded Astra 'does seem like a step change in spatial reasoning' ^[7]. On the StationeryBench desk-manipulation benchmark, Astra fully completed 7 of 100 tasks versus zero for Ai2's MolmoAct2, and posted a median progress score of 46/100 against MolmoAct2's 12/100 ^[7]. That's a genuine gap over the prior generation of models. Even so, Artzi's own REMAP testing found Astra still falls short of human performance on some of the same 3D reasoning scenarios ^[7]- a useful check on how far 'step change' actually goes.

Days after launch, users across ChatGPT and Codex reported that Astra's agentic output had visibly degraded - stopping tasks early, claiming work was done when it wasn't, or responding to stale context ^[4]. OpenAI's Codex product lead Tibo Sottiaux publicly attributed this to three distinct defects: legacy skills written for older models over-triggering and sometimes blocking Astra from checking its own work; a broken opt-in context-management experiment that made the model quit early or reply to stale messages, affecting an estimated 4,000 to 5,000 users before it was disabled; and inference engines serving tail traffic that were misconfigured and measurably degraded output quality ^[4]. The fix shipped as a full usage reset pushed out before midnight local time ^[4]. OpenAI's own engineering team walked through the same three causes in a public reset announcement, directly countering community speculation - visible in both Reddit threads and OpenAI staff replies - that the model had quietly been swapped for a cheaper, more compressed version to handle demand.

Astra rolled out first to a limited set of organizations on September 3, then expanded within days to all ChatGPT Plus, Pro, Business, and Enterprise plans, plus the API and AWS ^[1]. OpenAI's own announcement confirms it is live for Pro, Enterprise, and Business Premium users in ChatGPT Work and Codex ^[2]. Microsoft moved just as fast, making Astra generally available to all Foundry customers on Azure, priced at $10 / $1 / $50 per million input, cached-input, and output tokens for short-context requests in the Standard Global tier, rising to $20 / $2 / $75 for long context ^[3]. The model ships with a 1.05 million-token context window, a 128,000-token max output, and an April 30, 2026 knowledge cutoff ^[2].