Select between two models
Use a learned router to choose between a stronger and a weaker model.
Model-selection framework
RouteLLM
Serve and evaluate learned routers that choose between a stronger and a weaker model using a configurable cost-quality threshold.
Sources reviewed . Documentation review; not a performance benchmark or runtime test.
What can you do with RouteLLM?
Tune the cost-quality tradeoff
Calibrate the routing threshold against representative queries and the model pair you intend to serve.
Evaluate before serving
Use the framework to assess routing behavior; it is a model-selection framework rather than a hosted gateway account.
Before you integrate
Calibrate thresholds on representative queries and evaluate the model pair you actually intend to deploy.
Test against your own workload
Compare successful responses, latency and the total bill, including retries and fallbacks. Verify streaming, tool calls and structured output on the routes you plan to use. These are evaluation steps, not claims that every feature is supported.
Check model-provider incidents →Pricing and model access
Model rates, platform fees, credits and eligibility can change. Check the publisher’s current terms before choosing a route. Our calculator estimates model token costs; it does not quote a negotiated RouteLLM bill.
Check RouteLLM documentation ↗Sources and corrections
Suggest an update →Compare related routing products
Selected by product type and shared routing capabilities.
TensorZero →
An open-source gateway with routing, observability and evaluation. Its official repository is archived; assess maintenance before adopting it.
LiteLLM →
Run a proxy or use a Python router to distribute requests across model deployments, with load balancing, retries, cooldowns and fallbacks.
Merge Gateway →
Use one inference API with model routing, budgets and data controls. Choose ordered failover, intelligent selection or a custom benchmark-weighted routing policy.