OpenAI has introduced a structured reasoning framework for GPT-5.6 Sol, mapping five distinct levels of cognitive effort to the complexity of user tasks. The system, detailed by OpenAI employee Vaibhav Srivastav, is designed to help users match the model’s reasoning depth to the actual demands of their work, rather than defaulting to maximum capability for every query. This move represents a significant shift in how users interact with large language models, trading raw power for efficiency and cost control.
What Are the Five Reasoning Levels in GPT-5.6 Sol?
The five reasoning levels in GPT-5.6 Sol are named “Light,” “Low,” “Medium,” “High,” and “xhigh.” Each level corresponds to a specific type of task complexity and determines how much computational effort the model expends on reasoning. “Light” and “Low” are designed for quick, clear-cut tasks that require minimal deliberation. “Medium” is suited for tasks involving planning and analysis. “High” and “xhigh” are reserved for complex, multi-step work or scenarios that demand careful verification. Higher levels consume more time and tokens, making the choice of the appropriate level a practical decision for both performance and cost.
How the Reasoning Levels Map to Task Complexity
According to Srivastav, the mapping is straightforward: simple, unambiguous queries should use “Light” or “Low,” while tasks that require logical chains or strategic thinking benefit from “Medium.” For problems involving multiple interdependent steps, deep research, or rigorous fact-checking, “High” or “xhigh” are the appropriate choices. This tiered approach enables users to avoid overpaying for unnecessary reasoning power on trivial tasks while still having access to deep deliberation when needed. The levels do not correspond to the tiers in GPT-5.5, and users migrating from the previous version are advised to start one level lower than they are accustomed to.
Max and Ultra: Two Special-Purpose Reasoning Modes
Beyond the five standard levels, GPT-5.6 Sol introduces two additional modes that operate on different principles. “Max” allows the model to allocate more time to a single problem, effectively deepening its focus on one task. “Ultra” takes a parallel approach, deploying multiple sub-agents simultaneously, each handling a different part of a larger task. These modes are designed for scenarios where the standard levels are insufficient, but they come with higher token consumption and longer processing times. The distinction between scaling depth (Max) and scaling breadth (Ultra) gives users a flexible toolkit for tackling complex workflows.
The Missing Pro Tiers and the User Experience Gap
Despite the sophistication of the reasoning framework, GPT-5.6 Sol’s Pro tiers remain absent from the current release. These tiers, which were previously leaked in a genomics benchmark paper, are expected to offer additional capabilities but have not yet been made available. This gap complicates the user experience, as even experienced users may struggle to select the optimal level without running their own benchmarks. The setup may also serve OpenAI’s broader data collection goals, helping the company understand how users allocate reasoning effort across different tasks. Notably, the introduction of these levels adds complexity to the interface, moving further from OpenAI’s stated vision of a simplified, “almost no interface” future for ChatGPT.
Who Should Try This Now
Users currently on GPT-5.6 Sol should begin by evaluating their most common task types against the five reasoning levels. Start with “Low” for routine queries and only scale up to “Medium” or higher when the task genuinely requires multi-step reasoning or verification. For projects that involve parallel subtasks, “Ultra” offers a compelling option, but it should be tested against token budgets first. The practical takeaway is straightforward: don’t default to the highest level. Let the task dictate the reasoning effort, and treat the level selection as a cost-management lever as much as a capability toggle.
