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Model routing only pays off if the router's choices rest on independent measurements of quality, cost, and latency across real tasks. The article's benchmark data shows that no single model leads everywhere: the top five models each win on different tasks, and some rank poorly on specific domains. Readers should treat routers as a practical way to manage that spread, with evaluation quality as the main factor in whether they deliver.
Sources coveringVals AI Blog, Latent Space, arXiv

Routers Are a New AI Primitive

Vals AI Blog9 min read

Model routers need an independent, end-to-end evaluation of quality, cost, and latency on real work. We're building one.

Every few months, frontier models unlock harder problems and new domains. No company can afford to sit that out, but few can afford frontier prices on every request. Which model should handle each piece of work, and when does the performance of frontier models justify their cost? At Vals, we evaluate models from across the major labs on their ability to carry out complex work across domains such as finance, software engineering, law, and tax. The Vals Index brings these evaluations together to measure AI’s potential economic impact. From that vantage point, the frontier is jagged: five different models hold the top spots on the Index’s eight benchmarks, and no model ranks in the top three on all of them. Choosing only one model means compromising performance on some tasks. On real work, the frontier is jagged The five models that hold the top spots on the Index’s eight benchmarks, and their rank on each, out of 48 models. The five models that hold the top spots on the Index’s eight benchmarks, and their rank on each, out of…

Excerpt shown under the publisher's RSS terms · 179 words · full text and figures are on the publisher's site

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