Tokenomics 101: Lowering AI TCO Through Strategic Model Allocation

The models an enterprise can route among today span roughly a 100x range in price and, at the top, a surprisingly narrow range in capability. Claude Opus 5 leads Artificial Analysis's intelligence index at 63. Nine models sit within ten points of it, at blended list prices from $0.99 to $20 per million tokens. Paying the flagship rate for every task means paying flagship prices for work a model at a fifth the cost does indistinguishably well. That, not vendor negotiation, is where AI TCO is won or lost.

This paper builds the allocation discipline. Classify each task by its token shape (how much goes in, how much comes out), its error cost, and how verifiable the output is. Pay for intelligence where decisions concentrate: research and planning, which read a lot and write a little. Use mid-tier models where execution follows a plan someone smarter already wrote. Anthropic's own engineering research backs the pattern: an Opus lead agent directing Sonnet subagents outperformed a single Opus agent by 90.2 percent on their research evaluation, and their docs tell customers to reserve the big model for architectural decisions. The same logic prices out across vendors, and the worked examples in Section 3 show tiering cutting a three-stage pipeline's cost 63 percent without touching the stages that need the expensive model.

Two more layers stack on top. Dynamic routing, through OpenRouter, Cursor, or a self-hosted router, automates the allocation per request; published benchmarks show 74 to 85 percent cost reductions at measured accuracy tradeoffs. And token-efficient engineering, prompt caching, batch processing, context editing, effort parameters, routinely halves the bill again. The paper ends with a 30-day action plan, including copy-paste subagent configs and routing setups. Every price and claim is cited; illustrative token counts are labeled as such.