Can tech companies learn to love cheaper AI models? 

if those same AI workloads can be handled by cheaper models without affecting quality, it would mean a massive shift in the economics of AI.

If those same jobs can be handled by cheaper models without affecting quality, it would mean a massive shift in the economics of AI. And critically, much of the savings would be coming out of the pockets of the big labs, dealing a financial blow to OpenAI and Anthropic just as they’re heading for their IPOs.  It’s a potentially seismic change in the industry, resting on one basic question: Are companies ready to switch to smaller models?  Initial tests suggest that, when the system is arranged right, cheaper models could sub in without any sacrifice in quality. In a recent test by the legal AI tool Harvey, the company was able to reduce inference costs by 3x without reducing quality. The test, performed in partnership with the inference platform Fireworks AI, combined Claude Opus and Fireworks’ GLM 5.1, and shifted to Opus for the most intensive tasks. The result was a significantly lower load in terms of server time and overall cost.