Still defaulting to your top-tier model for every simple task? LangChain just published how they cut costs on their own coding agent without losing quality. Title: How to Build a Model Router in the Harness URL: Facing ballooning monthly costs on its Open SWE coding agent, LangChain built a model router directly into the harness to balance cost and quality. Three highlights stand out. 📊 Starting from task analysis Analyzing historical traces showed 22% of requests were new features and 17% bug fixes, with wide variation in token consumption. That diversity in task complexity is exactly what makes selective routing worthwhile. 🎯 Three models on the Pareto frontier They picked a fast tier (GLM-5.3-Flash), a balanced tier (GPT-5.6 Sol), and a performance tier (GPT-6 Astra) based on cost-intelligence tradeoffs. Routing happens once at the start of each thread, and that model is used throughout. 💰 64% cost reduction with no quality loss In an A/B test across 973 threads, merged-PR rate was essentially the same — 29.2% with the router versus 27.3% always-premium (p=0.49) — while median cost per thread dropped from $2.61 to $0.94. A parallel test using only the cheapest model was pulled after just one day, underscoring the value of finding that sweet spot instead of going all-cheap or all-premium. #AIAgents# #CostOptimization#