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The founder of EvalShift shares a model swap case where cost dropped 59% and latency improved 75%, yet agent behavior regressions like fake tool completions and 25% tool-selection divergence would still block the ship, arguing cost/latency alone can't certify a migration.
A user reports replacing American AI models with Chinese alternatives across reasoning, code generation, agent loops, bulk processing, and image/video generation, achieving 87% cost reduction with only 4% average quality drop and unchanged revenue.