OpenAI's latest model, GPT-5.6 Sol, includes five distinct reasoning levels designed to match different task complexities. The tiers range from "Light" through "xhigh," with additional "Max" and "Ultra" modes that deploy multiple sub-agents working in parallel.
Vaibhav Srivastav, an OpenAI staffer, has published guidance on which reasoning level suits which workload. His core recommendation: start with the lowest reasoning tier and scale up only when necessary. This approach optimizes for both cost and speed, avoiding computational waste on simple tasks that don't require advanced reasoning.
The tiered structure reflects OpenAI's strategy to make reasoning-based models practical for production use. Lower tiers handle straightforward queries with minimal inference overhead. Mid-level tiers tackle problems requiring moderate analytical depth. The highest tiers activate when complex multi-step reasoning becomes essential. Max and Ultra modes represent the extreme end, spinning up parallel reasoning chains to attack hard problems from multiple angles simultaneously.
This design contrasts with earlier approaches where reasoning capability was all-or-nothing. Srivastav's framework lets developers control the reasoning depth per request, not per model deployment. A chatbot answering basic questions uses Light reasoning. A document analysis pipeline jumps to mid-tier when needed. A research assistant researching novel problems can access Max or Ultra for comprehensive analysis.
The practical payoff hinges on implementation details Srivastav doesn't fully specify here. How accurately does the model self-assess task complexity? Can developers override automatic tier selection? What's the latency gap between Light and Ultra? These answers determine whether the system delivers on its promise.
OpenAI's emphasis on matching reasoning to task complexity signals confidence in Sol's reasoning pipeline. Rather than hiding capability behind a uniform interface, the company exposes granularity. This puts burden on developers to