The more I watch open-model discussion, the less I think “best overall” is the real question anymore. What seems more true now is that the field is separating into different kinds of usefulness. Some models are optimized to look brilliant in one turn. Some are better at long structured tasks. Some are better at tool use. Some are better at staying cheap enough to sit inside real workflows without turning every task into a cost problem. That is why Ling-2.6-1T is interesting to me less as a hype object and more as a signal. The pitch is not really “look how magical this chat feels.” It is much more about execution, structure, long task handling, and lower token waste. So I’m curious whether people here feel the same shift. Are we now looking at separate frontiers for raw reasoning, execution reliability, long-context organization, and cost per useful action? Because if that split is real, then a lot of leaderboard talk is going to look increasingly incomplete. submitted by /u/dahiparatha
Originally posted by u/dahiparatha on r/ArtificialInteligence