For Ling-2.6-1T, what would make the size feel justified first: quality per token, local serving reality, or long context stability?

The first question I have about Ling-2.6-1T is not “is the model card impressive?” It is whether the boring trade-off makes sense.

It is an open-sourced Ant/InclusionAI flagship with about 1T total params / 63B activated params, up to 1M native context, and 256K currently exposed through the official API. For a local-LLM crowd, I’d want one answer first: does the quality justify the active size, can the serving setup make sense, or does the long window stay stable enough deep into context?

Which one would you need answered before caring about it?