TECH Signal 496
Fable's cost and data policies push AI coders to route tasks to cheaper models
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The release of Fable, with its high cost and strict data retention requirements, is driving a shift where AI coding tool users route different tasks to different models rather than sending everything through the most capable one.
This mirrors the end of Moore's Law in hardware, when raw performance improvements stopped coming for free, developers had to start optimizing. AI tool users now need to invest in harnesses and context strategies to get good results from cheaper models, rather than waiting for the next model to paper over inefficiencies.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
Fable's pricing and data retention requirements have made it uneconomical to route all coding tasks through the most capable model.
GLM 5.2 costs roughly 1/9th of Fable and is sufficient for most rote coding tasks when given good context.
The shift is likely durable because improvements in inference costs benefit cheaper models too, and better harnesses make it easier to provide sufficient context to weaker models.
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