LLM Router System

An LLM Router System is a dynamic model selection system that can support LLM routing tasks by directing user requests to appropriate LLM sub-models based on query complexity analysis.

• AKA: Model Router, LLM Routing System, Dynamic Model Selector, Request Router.
• Context:
  • It can typically analyze Request Complexitys through complexity assessment algorithms.
  • It can typically route Simple Requests through lightweight model selections to mini LLM models.
  • It can typically direct Complex Requests through capability matchings to thinking models.
  • It can typically optimize Resource Utilizations through cost-performance balancings.
  • It can typically maintain Routing Decision Logs through decision tracking systems.
  • ...
  • It can often employ Reinforcement Learnings through routing feedback loops.
  • It can often support Multi-Model Orchestrations through parallel routing strategys.
  • It can often enable Deterministic Routing Modes through model pinning configurations.
  • It can often implement Fallback Routings through error handling mechanisms.
  • ...
  • It can range from being a Simple LLM Router System to being a Complex LLM Router System, depending on its routing sophistication level.
  • It can range from being a Static LLM Router System to being a Adaptive LLM Router System, depending on its routing learning capability.
  • It can range from being a Binary LLM Router System to being a Multi-Path LLM Router System, depending on its routing branch count.
  • It can range from being a Cost-Optimized LLM Router System to being a Performance-Optimized LLM Router System, depending on its routing optimization priority.
  • It can range from being a Rule-Based LLM Router System to being a ML-Based LLM Router System, depending on its routing decision methodology.
  • ...
  • It can integrate with Token Counting Systems for cost estimation.
  • It can connect to Model Performance Monitors for routing optimization.
  • It can interface with Load Balancing Systems for resource distribution.
  • It can communicate with Caching Systems for response optimization.
  • It can synchronize with Monitoring Dashboards for routing visibility.
  • ...
• Example(s):
  • GPT-5 Router Systems, such as:
    • GPT-5 Main Router for GPT-5 model selection.
    • GPT-5 Thinking Router for GPT-5 reasoning mode selection.
  • Multi-Provider Router Systems, such as:
    • OpenRouter System for cross-provider model routing.
    • Martian Router for multi-model orchestration.
  • Enterprise Router Systems, such as:
    • Azure OpenAI Router for enterprise model management.
    • AWS Bedrock Router for cloud-based model routing.
  • Open-Source Router Systems, such as:
    • LiteLLM Router for lightweight model routing.
    • Langchain Router for framework-based routing.
  • ...
• Counter-Example(s):
  • Single Model System, which lacks dynamic model selection capability.
  • Static Model Pipeline, which uses fixed model sequences rather than dynamic routing.
  • Manual Model Selection Interface, which requires user model choices instead of automatic routing.
• See: Mixture of Experts Model, Cost-Optimized Time-Based AI Routing System, Model Selection Algorithm, Load Balancing System, OpenAI GPT-5 Language Model, Agentic Software System, Token Pricing Model, Request Complexity Analysis, Model Performance Benchmark.

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