79% of the time, the model you asked for isn't the model you needed

Data from the Weave model router shows that fixed model choices for AI coding agents are frequently inefficient: across roughly 275,000 requests from 22 installations over 30 days, 79.2% were routed to a different model than originally requested. The router commonly directs routine tasks such as classification, title generation, and tool-output summarization to cheaper, faster models, while also downshifting some frontier-model requests when their task requirements do not justify higher capability. These decisions reduced routed-traffic costs by about 28% without requiring configuration changes or reportedly causing quality regressions, though routing added a median 329 milliseconds, or roughly 7%, to a typical 4.8-second upstream request. Users explicitly forced a model in 12.4% of requests, an override mechanism presented as necessary for retaining control when specific model preferences matter.

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