Have you actually used 256K/1M context for messy workflow inputs?
Most long-context talk still sounds like a chat demo. The uglier test is whether a model can hold a PRD, logs, docs, tests, repo slices, prior outputs, and contradictory notes from earlier runs in one working context without everything turning brittle. That is why Ling-2.6-1T is interesting to me. The official docs say it supports up to 1M native context, while the official API currently exposes 256K. The public materials also keep pairing that with fast thinking and lower token overhead. If that matters in practice, the win is not "it can chat forever." The win is fewer chunk / summarize / stitch passes, less context loss between steps, and less prompt glue holding the workflow together.
Have you tried a long-context model on work like this? PRD + repo + tests, long incident logs, or multi-run agent state with conflicting notes. Where did it actually help you, and where did it still make you clean the mess by hand?