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.