Technical Context
I love such field signals more than any glossy benchmarks. When someone writes that Kimi is more convenient for running tests, Sonnet 4.6 is more flexible, and some models just lock up at the word "cybersecurity", I immediately think not about leaderboards, but about real AI integration into the workflow.
And here the picture is quite down-to-earth. Kimi indeed performs well on structured security tasks: CTF, forensics, artifact analysis, step-by-step investigation. In my observations, it often tries to be more thorough than precise, which can be a plus in a test environment.
Sonnet 4.6 also makes sense to me. It isn't always the boldest in depth, but it usually maintains a steadier line of thought and falls apart less on long chains. When I need not a one-time magic but predictable behavior in AI automation for team processes, such stability is more valuable than a single wow result.
With Codex the story is different. It's interesting not because it "thinks better about security", but because it gets tasks to working code, integrations, and useful results faster. If extended confirmation and plugins are accessible, it fits well into engineering scenarios where the goal is not to argue with the model but to build a tool.
And the lockups at Anthropic and the strict safety locks in frontier models are now part of the architecture, not an accident. So choosing a model for cybersecurity must consider not only answer quality but whether it will survive the sheer fact of a security context without refusing.
What This Changes for Business and Automation
In short, those who separate model roles win. I would look at Kimi for research tests and labs, Sonnet 4.6 for more stable production processes, Codex for building utilities, internal agents, and fast security integrations.
Teams that try to put the entire perimeter on one model lose. In practice, you'll hit either safety locks, weak executability, or costly mistakes on long scenarios.
I see exactly these forks constantly in projects. At Nahornyi AI Lab we usually don't argue which model is "best", but assemble an AI solutions architecture for the specific risk, access rights, and task type, so that automation with AI doesn't break on the first sensitive prompt.
If your security team spends hours on manual analysis, triage, or internal tools, you can calmly break that down into workable blocks. And then, under those blocks at Nahornyi AI Lab together with Vadym Nahornyi, build an AI solution development without fancy tales about a universal model, but with a real result in production.