Right-sizing intelligence: Glean saves 81% on token costs and is preferred 78% of the time versus Claude Cowork

This post details Glean's approach to optimizing AI chat latency and cost by implementing a Pareto frontier analysis to map cost and performance tradeoffs across various models. It introduces Glean's auto-routing mechanism, which matches tasks to models and their reasoning levels based on effort and capability required, using a specialist model (Glean Waldo) for reasoning level determination. The post also presents benchmark analysis showing Glean's 81% cost savings and 78% preference rate over Claude Cowork, attributing this to architectural decisions like a unified enterprise context and an efficient harness design that minimizes token usage. It highlights how Glean's preferred responses reduce 'botsitting' by providing more accurate, complete, and grounded outputs.