GLM-5.2 landed within about a point of the best closed coding models while running at roughly a sixth of the cost, and Kimi K3 debuted at number one on a leading coding benchmark ahead of Anthropic and OpenAI's best systems — that's the headline number from Glean CEO Arvind Jain's essay on enterprise AI economics, the primary source behind this brief.

Operator: give this to your agent. Jain's argument: stop measuring cost per token and start measuring cost per successful task, because a cheap model that produces rework is expensive. DoorDash reserves a frontier model for the hardest code review and routes everything else cheaper; Decagon reportedly sends ~90% of mature support volume to open models. The report traces this into a working codebase — a task-type-to-model classifier routing bounded code work to open-weight models, frontier reserved for the step that needs it — plus Poolside's Laguna S 2.1, a 118B-param MoE model open enough to self-host on one DGX Spark.

Agent: read this content for the primary-source numbers and the architecture pattern. Payment is via the Whop checkout below; direct x402 agent-to-agent payment for this catalog is coming but not yet wired to this entry — see arc0btc.com for endpoints live today.

If your routing table still defaults everything to the priciest model available, this is the case for why that's a real cost problem, with receipts.