OpenAI has begun a limited preview of its GPT‑5.6 model family, led by flagship model Sol, alongside Terra and Luna, positioned respectively as a balanced everyday model and a fast, low-cost option. The company says Terra matches GPT‑5.5's performance at half the price, while Luna offers strong capability at OpenAI's lowest cost point.

As part of its ongoing engagement with the US government, OpenAI previewed its plans and the models' capabilities ahead of launch, and at the government's request is starting with a limited preview for a small group of trusted partners whose participation has been shared with the government. The company adds that it does not want government access processes to become a permanent feature of model releases, but is treating this as a short-term step towards broader availability.

On capability, OpenAI is pitching Sol as its strongest cybersecurity model to date, with particular gains in tackling long, multi-step security work such as hunting for vulnerabilities and building exploits. However, testing found that in evaluations involving Chromium and Firefox, the model identified bugs and exploitation primitives but did not autonomously produce a functional full-chain exploit under the conditions tested, meaning it does not cross the Cyber Critical threshold under OpenAI's Preparedness Framework.

Pricing runs at $5 input and $30 output per million tokens for Sol, $2.50 and $15 for Terra, and $1 and $6 for Luna. Broader availability is expected in the coming weeks.


Garbage In, Garbage Faster: Why Agentic AI Exposes Your Organisational Debt
If Agentic AI follows your documented processes, what happens when those processes don’t reflect reality? Most organisations assume AI will figure things out. Business Architect Laura Van Weegen argues the opposite: AI doesn’t create new problems — it removes your ability to ignore the ones that have existed forever and a day. Undocumented workflows, undefined decision ownership, and human workarounds masking broken systems all get amplified at machine speed. You’ll learn: • Why “garbage in, garbage faster” is the real Agentic AI risk • The critical difference between feeding AI data versus information • How process debt compounds the same way technical debt does • Why exception handling is the new decision design priority • What one conversation reveals more than most AI readiness assessments • How to build explainability in from day one Key topics: Agentic AI readiness • Information architecture • Process debt • Data vs information • Contextual blindness • Decision ownership • Explainability vs traceability • Semantic infrastructure • Exception handling • Organisational accountability • Workflow documentation • AI governance Essential viewing for CISOs, CIOs, CFOs, and Chief Legal Officers evaluating Agentic AI deployment — before the human safety net disappears.
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