Route Requests Between Models
Send each request to the cheapest model that can handle it, escalating only when needed.
/ quick answer
Classify request difficulty up front and route easy, medium and hard work to different models through a gateway. Send each request to the cheapest model that can handle it, escalating only when needed.
- 01Sample real traffic and label it easy / medium / hard by hand.
- 02Define the route: small model, mid model, reasoning model.
- 03Use rules or a cheap classifier to choose the route per request.
- 04Add an escalation path when the small model reports low confidence or fails validation.
- 05Put routing and fallbacks in an LLM gateway, not in app code.
- 06Track quality and cost per route weekly and rebalance thresholds.
/ frequently asked
What does the Route Requests Between Models workflow do?
Classify request difficulty up front and route easy, medium and hard work to different models through a gateway.
What problem does Route Requests Between Models solve?
Using a frontier reasoning model for every request is slow and expensive; using a small one for everything fails on hard cases.
How many steps does Route Requests Between Models take?
6 steps. It starts with sample real traffic and label it easy / medium / hard by hand. and ends with track quality and cost per route weekly and rebalance thresholds..
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →LLM Gateway
A proxy between your app and model providers that centralises keys, routing, fallbacks, logging and spend limits.
- →Reasoning Model
An LLM trained to spend extra compute thinking through a problem internally before answering, improving accuracy on math, code and planning.
- →Small Language Model (SLM)
A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
- →Agent Cost Control
Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
Related workflows
Turn this into a repeatable process.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
- →Cut LLM Inference Costs
A step-by-step method to reduce model spend without degrading output quality.
Related tool stacks
The tools that run it in production.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
Related prompts
Reusable prompts for this job.
- →Model Routing Classifier Prompt
A cheap-model prompt that labels request difficulty so it can be routed to the right model.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
Related use cases
How people apply it, and what came out.
- →SaaS Cuts AI Bill With Routing
Illustrative example: routing easy requests to a small model reduces spend while holding quality steady.
Comparisons & alternatives
Pick between the options.
- →Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.
- →OpenAI vs Gemini for Agent Building
Both ship strong models and SDKs — differences are in tool calling, context and pricing.
- →Claude vs GPT vs Gemini
The three frontier model families, side by side.