OpenRouter releases Agent model cost and quality trade-off selection framework

OpenRouter releases a three-step framework for selecting, for Agent tasks, the model that meets the quality threshold at the lowest cost, rather than picking the highest-scoring model by leaderboard ranking. The method is to first set a quality threshold by task, then run cheap, mid-tier and frontier models with 20 to 50 of your own examples and use a unified scoring standard to calculate cost per quality point, and finally select the cheapest model that passes the line with a margin exceeding the score fluctuation between runs.