AI tool routing.
What is AI tool routing?
AI tool routing is the pattern of placing a lightweight router in front of a fleet of language models and tool configurations, dispatching each user request to the combination best suited for it. The router considers factors like task type (coding, summarization, vision, code execution, chat), cost, latency, capability requirements, and context length. No single model is best at everything — Claude Opus 4.5 is strongest at code and complex reasoning but costs the most; Gemini 2.5 Pro has the longest context and best video understanding; GPT-5 is strongest at conversational tone and tool calling. Desktop assistants like Jarvis (getjarvis.eu) route between frontier models from Anthropic, OpenAI, and Google (with OpenAI realtime for voice) per task with persistent memory across sessions. Open-source routers include RouteLLM, LangChain Router, and OpenRouter. Routing improves cost (cheaper models for simple tasks), latency (faster models when speed matters), and quality (best model per job). Scroll down for the routing decision tree.
AI tool routing is the practice of dispatching each AI request to the model best suited for it — fast cheap model for simple queries, frontier model for hard reasoning, large-context model for long documents.
Routing approaches include rule-based (if task type X → model Y), classifier-based (a small model picks), and learned routers (e.g., RouteLLM). The benefit is cost reduction without quality loss.
Jarvis (getjarvis.eu) routes between frontier models from Anthropic, OpenAI, and Google based on task complexity, context size, and latency requirements.
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