The economics of AI matter just as much as their capabilities. They are shaped by the architecture around the model, not only the model itself: how work is routed, how context is retrieved, and how each task is orchestrated.
In our benchmark, @Glean was 4x more cost-effective, averaging $0.45 per task versus $1.84 for Claude Cowork, a 75% reduction in cost. That gap came from making better decisions at every layer of the stack.
The best systems will not send every task to the most powerful model. With the right context, they can understand what each task requires and route it to the least expensive model capable of meeting that bar.
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We've been investing heavily in our harness and routing capabilities, and we put them to the test benchmarking Glean's token costs against Claude Cowork. The results were striking: @Glean is 4x more cost-effective, averaging $0.45 per task versus $1.84 for Claude Cowork.
That 4x advantage comes from two things compounding: 2.9x lower token volume and a 1.4x cheaper blended rate per million tokens. Here's how:
- Model family routing: Glean made use of Luna which is 10x cheaper than Claude Sonnet and widely capable. We’re able to strike the balance by routing between open and closed models.
- Model tier routing: In Glean, Opus was used 10x more (29% vs 2.8%) but surgically for the right things and balanced by other models.
- Better context: Glean’s harness and indexing capabilities result in fewer tokens consumed; Claude Cowork used 3x the tokens per query on average using 88.8M versus 29.8M in Glean.
More results coming out at Glean:GO! Hit me up if you're still looking for an invite.
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