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Context

Most AI products send every request to one model, because picking a model per request without losing quality is hard. The cheapest model that does the job changes from one request to the next, and only your own requests show which one it is. Off-the-shelf routers rely on classifiers trained on generic data, and teams compare models on public benchmarks, which measure someone else’s tasks. Neither tells you how a model will perform on your requests, so switching to a model that looks better on paper can still change how your agent behaves.

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How Adaptive Routing works

Weflayr routes on how models perform on your own requests:
  1. It benchmarks candidate models on your own past calls, against the model you use today.
  2. It routes each new call based on how each candidate performed on similar calls, following your cost and quality objective.
Because it learns your use case’s instructions, tools and recurring patterns, the router focuses on the details of each request that actually decide which model is best. You review every model’s results on your own calls before any production traffic moves.
Offline, a sample of calls is benchmarked on dozens of models and judged against the current model. Online, the router encodes each new call, finds the most similar benchmark calls and ranks the models.

Benchmarking

How candidate models are replayed on your calls and judged against your current model.

Routing

How the router picks the model for each new call.

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Results

On 1,177 test calls over 11 use cases, the Best quality objective beats Claude Opus 5.5 on quality (+20 points) at 71% lower cost, and Same quality beats it slightly (+5 points) at 89% lower cost, ahead of OpenRouter’s Auto Router and of the published routers of LLMRouterBench.

How Weflayr's Adaptive Routing outperforms leading LLM routers on quality and cost

The full setup, datasets and results.