# Model capability is converging. Model economics are not.

> One real coding task cost roughly 80 times more with one frontier model than another, with negligible difference in the final result.

Published: 2026-07-11
Reading time: 3 min read
Canonical URL: https://aidanmarshall.ai/writing/model-economics-are-not-converging/
Source: LinkedIn — https://www.linkedin.com/feed/update/urn:li:activity:7481524040243978240/
Provenance: Personal observation
Provenance note: Adapted for aidanmarshall.ai from a post first published on LinkedIn. The 80x result describes one workload and is not a universal benchmark.

I spent roughly 80 times more on one coding task using GPT-5.6 Sol than I did using Grok 4.5 in Cursor, while the difference in the final result was negligible. This was one real-world task—not a universal benchmark. Cursor pricing, token usage, reasoning effort, caching, and model behavior can all affect the final cost.

Per-token pricing alone does not determine the economics. A model that reasons longer, produces more tokens, or invokes more agents can become substantially more expensive before the user sees a meaningfully better result.

A good harness can mitigate many remaining model differences through better context and task decomposition, planning and validation loops, automated tests and evaluations, model routing, and escalation to expensive models only when needed.

A practical router uses the least expensive model that reliably clears the quality bar, then escalates when the expected improvement justifies the added cost.

## Topics

Model routing, AI economics, AI-assisted development, LLM cost