Yesterday’s discussion here made me think the real shift might be even bigger than “different vendors are optimizing for different things.” It may be that “useful intelligence” itself is no longer one target. A model optimized to look brilliant in one isolated interaction is not the same product as a model optimized to survive repeated execution inside a workflow. Once models start living inside systems, the evaluation changes. Cost discipline matters. Constraint-following matters. Tool reliability matters. Retry stability matters. Long-context structure matters. Raw capability still matters too, but it stops being the whole story. That’s why Ling-2.6-1T is interesting to me as a signal. Not because it proves anything by default, but because the positioning seems to ask a different question: what does a model need to be good at when it is embedded inside a larger operational loop, not just judged as a standalone conversational mind? So I’m curious whether people here feel the same shift. Are we now looking at multiple frontiers at once? One frontier for raw reasoning. One for workflow execution. One for controllability. One for cost-per-useful-action. One for “best substrate for agents.” If that split is real, then a single benchmark-driven leaderboard is going to miss more and more of what actually matters submitted by /u/nebulagala_xy
Originally posted by u/nebulagala_xy on r/ArtificialInteligence