OpenAI Releases Reasoning Level Selection Guidelines for GPT-5.6 Sol

An engineer from OpenAI has officially demonstrated how to use the reasoning levels built into the latest model "GPT-5.6 Sol". The model features five tiers ranging from "Light" to "xhigh", plus higher-level modes called "Max" and "Ultra" that run multiple sub-agents in parallel. Users can select these according to the difficulty of their tasks.

The "reasoning level" of an AI model refers to a mechanism for adjusting how deeply the model should think before generating an answer. As the level increases, it becomes easier to handle complex problems; however, processing time and costs tend to increase. This flexibility in settings aligns with the direction OpenAI has been gradually implementing in reasoning models since the o1 series, and GPT-5.6 Sol can be seen as broadening these options further.

Regarding specific use cases, Vaibhav Srivastav from OpenAI recommends the approach of "starting with a lower level and raising it only when necessary". In other words, simple questions or routine tasks are adequately handled by lighter modes like "Light", while higher tiers should be reserved for tasks requiring advanced analysis or complex reasoning. The top-tier "Max" and "Ultra" modes operate through a mechanism where multiple sub-agents run in parallel, designed specifically for particularly challenging tasks.

The significance of this guidance extends beyond being a simple operation manual. The balance between cost of use and performance of AI models has become a critical practical concern for enterprises and developers. As raising the level improves accuracy while increasing API costs and response times, appropriate use directly impacts economic efficiency. OpenAI engineers providing official guidelines helps reduce the effort users would otherwise spend through trial and error searching for optimal solutions.

Furthermore, the existence of "Max" and "Ultra" modes that run multiple sub-agents in parallel demonstrates that GPT-5.6 Sol is designed not merely for simple conversational responses but for agent-like task processing. An AI agent refers to operations that go beyond answering one-off questions to autonomously execute multiple steps. This enrichment of functionality aligns with the broader movement toward integrating AI into business processes.

A key point to watch going forward is how widely the multi-tiered approach to reasoning levels becomes a standard design philosophy. The design allowing users to control the "depth of thinking" of a model represents a practical approach to achieving both cost management and performance assurance, with potential to spread across the industry. How OpenAI integrates this guidance into documentation and product UI also warrants continued attention.