Good question. Local LLMs are already “good enough” for most in-context work: short-to-medium writing, refactors, reasoning over what’s on screen, and multi-step agent plans where latency and privacy matter more than raw IQ.
I still fall back to remote models for very long-context tasks, heavier code synthesis, or when you want best possible reasoning over large codebases, the goal is to default local, then escalate only when it actually adds value.
I still fall back to remote models for very long-context tasks, heavier code synthesis, or when you want best possible reasoning over large codebases, the goal is to default local, then escalate only when it actually adds value.