AI labs have staked their reputations on model benchmarking — the supposedly neutral and accurate system of evaluations that are designed to measure whether one AI product is better than another. However, the methods behind these evaluations are complicated and still maturing, said Irene Solaiman, Hugging Face’s chief policy officer, during a discussion at TechCrunch […]
Read the original at TechCrunch AI: Embedded AI evaluators won’t be an easy solution, Hugging Face’s policy chief says
How OpenSmartRoute helps
For a team that routes through the router, a release like this is one catalogue entry. The new model competes on the next request against what the team already runs, on quality, cost and speed. If it answers well it earns more traffic; if it fails it gets less, with no code change in the app.
Any OpenAI-compatible provider works, as do open-weight models served locally. The catalogue at https://opensmartroute.ai lists models with their prices and live rankings, and the savings ledger shows what the routing saved against always using the most expensive option.