Reasoning.effort
Also written: Cognitive effort
A setting that controls how much computational effort the AI uses to think through a problem, balancing speed and accuracy.
Reasoning effort is a setting that lets you control how hard an AI model tries to solve a problem. When you use a higher effort setting, the AI spends more time and computational power thinking about your request. This can lead to more accurate and thorough answers, especially for complex questions.
However, using more effort also means the AI will take longer to respond and may cost more to run. A lower effort setting will be faster and cheaper, but the answers might be less detailed or accurate. It’s a trade-off between speed, cost, and the quality of the AI’s thinking.
Reasoning effort is a configurable parameter that adjusts the computational resources and depth of analysis allocated to an AI model's inference process. Increasing reasoning effort typically involves more complex computations, deeper search through potential solutions, or more extensive self-correction steps. This can improve the accuracy, coherence, and nuance of the generated output but comes at the cost of increased latency and computational expense.
Frontier AI labs are focusing on how much mental effort their models expend to complete tasks. This is driven by the need to balance performance with cost, especially for complex problems. Companies are exploring ways to control this effort, offering tiered performance or special modes for deeper processing.
- GPT-6.1 SolOpenAI · Model Documentation ·
- Better prompt caching for GPT-6OpenAI · OpenAI News ·
- GPT-Realtime-2 ModelOpenAI · Model Documentation ·
- GPT-5.6 Luna ModelOpenAI · Model Documentation ·
- GPT-6 Astra ModelOpenAI · Model Documentation ·
- GPT-5.6 Terra ModelOpenAI · Model Documentation ·
- Claude's extended thinkingAnthropic · Anthropic News ·
- OpenAI o3-miniOpenAI · OpenAI News ·
- Anthropic Claude
- Anthropic's Claude 3.7 Sonnet introduced an "extended thinking mode" for more in-depth processing on difficult tasks.
- OpenAI ChatGPT
- OpenAI's models like GPT-6.1 Sol and GPT-6 Astra are being developed with tiered performance options that relate to the effort and cost of complex tasks, while o3-mini boosts STEM reasoning.
- Temperature
- A parameter that controls randomness in text generation, affecting
- Top_p
- Another sampling parameter that limits the pool of possible next tokens,
- Model Size
- Refers to the number of parameters in a model, which is a fixed char
The concept of adjustable reasoning effort has emerged as a practical feature in large language models, allowing developers to tune inference behavior. It's often implemented as a set of discrete levels or a continuous parameter within the model's inference API.
When using ChatGPT or a similar AI assistant, try asking the same complex question twice. First, ask it to respond quickly. Then, ask it to take its time and provide a very thorough answer. Notice the difference in the length, detail, and accuracy of the responses.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- Setting tipGPT-6.1 Sol reasoning effort levelsSet the reasoning effort for GPT-6.1 Sol to control depth and cost. · 1 min
- Setting tipAdjust GPT-6 Reasoning Effort Without Breaking CacheChange reasoning effort between responses on GPT-6 models without invalidating cached context. · 5 min
- Cost tipAdjust reasoning effort for GPT-6 Astra cost and performanceGPT-6 Astra's reasoning.effort setting supports low, medium, high, xhigh, and max levels. · 5 min
- Setting tipConfigure Reasoning Effort for GPT-5.6 TerraAdjust Reasoning.effort setting for GPT-5.6 Terra to control its reasoning depth and cost. · 2 min
- Setting tipGPT-5.6 Luna Reasoning Effort SettingAdjust the reasoning effort in GPT-5.6 Luna to balance cost and quality for your task. · 5 min
Questions people ask
- Does higher reasoning effort always mean a better answer?
- Not necessarily. While higher effort can improve accuracy for complex tasks, it might be overkill for simple requests and could lead to slower, more expensive responses.
- How does reasoning effort affect AI costs?
- Higher reasoning effort requires more computational resources, which generally translates to higher costs per query, especially when using API services.
First seen on Subvolts Jan 31, 2025. Updated Oct 6, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.