Rippling's 2,100 scored runs experiment vs. the Stripe OpenRouter $7B deal

Rippling ran a benchmark that included testing 15 AI models on real payroll work with 2,100 scored runs per model and pass/fail grading with unfinished = fail. Results: - 7 untuned models came in at 88.5%-89.5% - Z.ai's GLM 5.2 (open-source): 88.7% for $621 total - Anthropic's Opus 4.6 (only prompt-tuned model): 91.0% for $1,453 This was structured payroll work, i.e. API calls with strict rules and validation, and the winner Opus 4.6 failed 9% of the scored runs on this harness. Rippling built pass/fail grading and their own spend console around it. Weeks later, Stripe acquired OpenRouter for $7B. OpenRouter helps developers pick between AI models based on cost, latency, provider rate limits, region/compliance, or fit for the task. They process ~100 trillion tokens a month across 8M developers and earn ~5% commission on inference spend, about $140M ARR right now. The question is how much of actual agent inference is heavy on thinking and reasoning vs. rather simple structured output work? Or more directly, do we need smart routing work in the future, or is a simple role-based fixed setup sufficient?