A blended model is a system that engages multiple underlying LLMs on each request and synthesizes the result dynamically, presented to the caller as a single model with one API surface. Unlike an aggregator catalog, the caller never selects an underlying model; unlike a simple router, the blend can combine work rather than only forwarding it.
Why it matters
Model selection is the largest unpriced cost in production LLM use: pay frontier rates on routine traffic and you lose more than any fee; send hard tasks to cheap models and failed runs bill you twice. A blended model moves that decision from a quarterly engineering argument to a per-request mechanism.
Measured: what the blend delivered
Pareto 26.9’s published benchmark scores are on the model card. Measured task costs and a composite score have not been published for this release.
Common questions
Is a blended model just a router?
No. A router forwards each request to one model from a menu. A blend can engage several models on a request and synthesize, and its output is not reducible to any single underlying model's answer.
How is a blended model priced?
Pareto bills at cost: what the underlying computation cost, with the business model living elsewhere. Aggregators, by contrast, fee the credits or mark up tokens on top of list prices.
How do I verify a blended model's quality claims?
Same-harness benchmarks with both sides published, then your own traffic. Anything less is marketing; this is why the evaluation harness is public.
Compare any two models
List rates and dated competitor measurements: prices and measured bills. Pareto 26.9 measured task costs are not published. Verbosity: Edition 2.