Adaptive Model Router Matches Task Complexity to AI Cost for Better Efficiency

A developer found that always using the most powerful AI model for coding tasks is often slower and more expensive than necessary, while defaulting to the cheapest model leads to costly retries. The key insight is that task uncertainty — not difficulty — should determine which model tier handles a given job. Frozen, deterministic tasks with closed rule sets suit lower-cost models, while ambiguous planning and high-consequence decisions warrant stronger ones. To formalize this, the developer built an open-source tool called Adaptive Model Router, which breaks work into typed stages and assigns each to an appropriate model tier. In regression testing across 14 cases, the skill-guided router scored 14/14 against a routing oracle, compared to 5/14 and 6/14 for unguided runs.