Using Eval Gates to Decide When a Cheap Open-Weight Model Is Good Enough for an Agent Task

A practical guide to building eval gates that let you swap in cheap open-weight models like DeepSeek, Kimi, and GLM for agent tasks only when task-level accuracy, tool-call reliability, and cost-per-success metrics clear a defined threshold, set against the AI token economy's first deflationary shock and widening Western-versus-Chinese inference pricing gaps.