OpenAI's GPT-5.6 Sol does the same work with 54% fewer output tokens. Here is why token efficiency is now the single MLOps cost lever that matters most.
Key takeaways
- On August 25, 2026, OpenAI published The Full Stack Behind Abundant Intelligence, framing efficiency as a full-stack result across models, inference, silicon, and its agentic harness.
- On the third-party Artificial Analysis Coding Agent Index, OpenAI reports GPT-5.6 Sol set a new high of 80, about 2.8 points above Claude Fable 5, while using 54% fewer output tokens.
- Sol also finished tasks in 57% less time than the next-highest model, and on the Intelligence Index came within a point of Fable 5 at 61% less time and roughly half the cost.
- The MLOps lesson: as quality converges, token efficiency, tokens per task, is what separates a cheap production system from an expensive one.
- Van Data Team's recommendation: measure tokens per task, compare models on efficiency at your real workload, and keep your stack portable so you can adopt gains as they land.
📖 Read the full guide on Van Data Team → Token Efficiency: OpenAI's Full-Stack Bet
