Model Routing

Model routing is the practice of automatically directing incoming requests or tasks to the most appropriate AI model based on task type, required quality, cost constraints, and latency requirements. In modern AI agent stacks, there is no longer a single model at the center — instead, an ensemble of frontier models, open-source alternatives, and specialized systems work in concert, with model routing determining which model handles which request. Typical routing strategies include: task-based routing (complex reasoning tasks go to powerful frontier models such as Claude Opus or GPT-5.5, while simpler classification or summarization tasks go to smaller, cheaper models), cost-based routing (requests below a complexity threshold are automatically redirected to lower-cost open-source models such as DeepSeek V4 or Llama 4), latency-aware routing (time-sensitive requests are sent to models with the lowest response-time profile), and fallback routing (when a primary model fails or is overloaded, a backup model automatically takes over without interrupting the workflow). In AI agent architectures like OpenClaw, model routing is a critical infrastructure component: it creates the flexibility to optimally balance performance and cost across different models while maintaining provider independence.