Model Routing
Sending each request to the cheapest model that can handle it.
/ quick answer
A router classifies the task (simple/hard, short/long, code/prose) and dispatches to a small model (Haiku, GPT-4o mini) or a large one (Sonnet, GPT-4o). Cuts cost 5-10x with no visible quality loss. Sending each request to the cheapest model that can handle it.
What is Model Routing?
A router classifies the task (simple/hard, short/long, code/prose) and dispatches to a small model (Haiku, GPT-4o mini) or a large one (Sonnet, GPT-4o). Cuts cost 5-10x with no visible quality loss.
What is an example of Model Routing?
A support bot routes FAQ matches to a small model and only escalates ambiguous queries to Sonnet.
Why does Model Routing matter for AI and automation?
Sending each request to the cheapest model that can handle it. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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Related concepts
The vocabulary this page depends on.
- →AI Router
A layer that picks the cheapest capable model for each request, saving cost and latency.
- →Quantization
Shrinking a model by lowering weight precision.
- →Inference
Running a trained model to produce outputs.
- →MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
Related workflows
Turn this into a repeatable process.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
- →How to Start a Niche Website with AI
Pick a niche, validate demand, build the site, and publish ranking content using AI end-to-end.
- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →AI Cost Optimization Stack
This stack provides tools and services for monitoring, analyzing, and controlling the operational costs associated with AI agent deployment and LLM usage.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →Groq vs Together AI
Fast open-model inference: throughput vs breadth.
- →Chroma vs Qdrant vs Pinecone
Open-source local vs managed cloud vector databases.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.