OpenAI employee Vaibhav Srivastav has clarified when each of GPT-5.6 Sol's five reasoning levels is appropriate for different task complexities. The breakdown, shared on X (formerly Twitter), gives users a practical framework for choosing the right tier without excessive trial and error.
Reasoning levels explained
Srivastav described five primary levels: Light, Low, Medium, High, and xhigh. Light and Low are meant for quick, clear-cut tasks where the answer can be determined directly. Medium is suitable for tasks that require planning or analysis but not deep reasoning. High and xhigh handle complex, multi-step work or situations that need careful verification.
Two additional modes, Max and Ultra, work differently. Max allows the model to spend more time on a single problem, effectively increasing reasoning depth. Ultra goes further by deploying multiple sub-agents in parallel, each tackling a different part of a task simultaneously.
Higher reasoning levels consume more time and burn through more tokens, which can increase costs. Srivastav recommends starting with a low level and only scaling up when needed. He also noted that these levels do not map directly to GPT-5.5's tiers. Users switching from GPT-5.5 should start one level lower than they are used to.
Missing Pro tiers and interface concerns
The guidance comes amid broader questions about OpenAI's product direction. GPT-5.6 Sol's Pro tiers are still missing. They were previously leaked in a genomics benchmark paper, but have not been officially released. This leaves even ambitious users without a clear way to pick the optimal level without running their own benchmarks.
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OpenAI has stated a goal of making ChatGPT so simple that almost no interface is needed. The current multi-level system does not bring the company closer to that ideal. Critics argue that requiring users to manually select reasoning levels goes against the goal of simplicity.
Usage data collection potential
Despite the complexity, the level system may help OpenAI gather useful usage data. By observing which levels users choose for which tasks, the company can refine its understanding of how people interact with the model. This could inform future simplifications or automated level selection.
Srivastav's post reflects a practical approach for users willing to experiment. He suggested that the best strategy is to start at Light or Low, then move up if the output quality is insufficient. For tasks that require careful verification, using High or xhigh can prevent errors.
The lack of Pro tiers means that users who need the highest performance are left waiting. The leaked benchmark indicated that Pro levels offered significant gains on certain tasks, but OpenAI has not announced a release date.
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