A lot of “open-source release” discourse still ends at weights + a benchmark collage. What I care about more is what becomes benchmarkable once a model is actually public. Ling-2.6-1T going open on Hugging Face today is interesting to me less as announcement news and more as a new object to evaluate for long agent loops: task decomposition, tool-call precision, retry drift, context cleanliness, token burn per resolved step, and intervention frequency. Its stated positioning is pretty specific: precise instruct execution, low token overhead, agent/tool workflows, and long-context task handling.
So if you were evaluating it seriously as an execution-first open model, what would you measure first?
My shortlist would be:
drift across retries
tool-call error rate
schema compliance after long context growth
token cost per finished subtask
repo-level fix quality vs one-shot codegen quality
What would you add or remove?