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Januari 2027 vs senaste Claude interna Model 2 
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• Model 2, which is somewhat more capable than Mythos 5. Our rough qualitative sense is that this model is a noticeable improvement on Mythos 5 for many tasks relevant to internal use but does not display a capability jump of the degree observed from Claude Opus 4.6 to Mythos Preview. We do not currently have plans to release this model externally, and have not run all of our typical suite of predeployment assessments, so we have somewhat lower confidence in our beliefs about its capabilities. We discuss this model’s applicability to each of our major RSP threat models in the following sections, though in Sections 3 and 4 our treatment is relatively brief.
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January 2027: Agent-2 Never Finishes Learning
With Agent-1’s help, OpenBrain is now post-training Agent-2. More than ever, the focus is on high-quality data. Copious amounts of synthetic data are produced, evaluated, and filtered for quality before being fed to Agent-2.² On top of this, they pay billions of dollars for human laborers to record themselves solving long-horizon tasks.⁴³ On top of all that, they train Agent-2 almost continuously using reinforcement learning on an ever-expanding suite of diverse difficult tasks: lots of video games, lots of coding challenges, lots of research tasks. Agent-2, more so than previous models, is effectively “online learning,” in that it’s built to never really finish training. Every day, the weights get updated to the latest version, trained on more data generated by the previous version the previous day.
With Agent-1’s help, OpenBrain is now post-training Agent-2. More than ever, the focus is on high-quality data. Copious amounts of synthetic data are produced, evaluated, and filtered for quality before being fed to Agent-2.² On top of this, they pay billions of dollars for human laborers to record themselves solving long-horizon tasks.⁴³ On top of all that, they train Agent-2 almost continuously using reinforcement learning on an ever-expanding suite of diverse difficult tasks: lots of video games, lots of coding challenges, lots of research tasks. Agent-2, more so than previous models, is effectively “online learning,” in that it’s built to never really finish training. Every day, the weights get updated to the latest version, trained on more data generated by the previous version the previous day.
