Related model update
GPT-5.6 makes reasoning effort visible to ordinary users
OpenAI’s current GPT-5.6 controls expose Medium, High and Extra High reasoning choices on eligible plans. That makes a technical evaluation setting into a product choice users can see.
Open the August 9 reportThe direct answer
Reasoning effort changes how much inference work the system is allowed to devote to solving a task.
For a difficult coding problem, mathematical proof, research synthesis or multi-step plan, additional inference computation can give the model more opportunity to explore alternatives, check intermediate steps, use tools and revise an approach before presenting the result.
That does not mean a higher setting always changes the underlying model. OpenAI’s current ChatGPT documentation, for example, says Medium, High and Extra High are all powered by GPT-5.6 Sol on eligible plans. The product is changing the reasoning level, not necessarily swapping Sol for a different family member.
Think of reasoning effort as a resource budget for solving the task, not an intelligence rating printed on the model.
What “more reasoning” means without pretending we can see proprietary internals
Inference is the stage when a trained model processes a request and produces an output. Reasoning models can use additional computation during that stage rather than treating every prompt as a one-pass response.
Public documentation does not justify simplistic claims such as “High always uses twice as many reasoning tokens as Medium.” Providers can change implementation details, allocate resources dynamically or use different serving strategies.
The safe description is functional: higher reasoning effort gives the system a larger opportunity to work through a problem before completing the answer. OpenAI’s GPT-5.6 system-card material explicitly reports performance across reasoning-effort levels because capability can change as that effort increases.
A reasoning setting and a model choice are different controls
Users often combine these ideas because both can change output quality. But they describe different layers.
Model choice
Selects a different trained model or model variant, potentially changing architecture, capability, speed, cost and tool behavior.
Reasoning effort
Changes the inference effort allocated while using a model that may remain the same.
This distinction becomes important when comparing results. “GPT-5.6 Sol” is not enough information if one run used Medium and another used Extra High or a maximum-effort API setting.
For a broader reproducibility guide, see Why the Same AI Model Name Can Mean Different Things.
A longer answer is not proof that the model reasoned more
Reasoning effort and final answer length are separate properties. A hard task may require substantial internal work and still end in three sentences. A simple writing request can produce a long essay with relatively little problem-solving complexity.
This matters because users sometimes evaluate “thinking” by watching how much text appears. That is unreliable. The visible response is the communication layer; the reasoning setting is a computation/control setting.
Do not use verbosity as a proxy for inference effort, and do not assume that a long answer is more correct because it looks more elaborate.
Where extra effort can be valuable
Difficult tasks create more room for additional computation to help
Coding
Tracing failures across several files, planning changes, testing assumptions and coordinating tools can benefit from more deliberate work.
Mathematics
Multi-step derivations and proofs may require checking intermediate conclusions rather than producing the first plausible answer.
Research synthesis
Reconciling conflicting sources, dates and technical claims can benefit from additional checking and comparison.
Planning
Tasks with constraints, dependencies and tradeoffs can require exploring several candidate plans before selecting one.
Tool use
Complex workflows may involve deciding which tool to call, interpreting results and changing course after new evidence appears.
Long-horizon analysis
Work that spans many intermediate decisions creates more opportunities for early mistakes to compound.
More effort can be wasteful on easy tasks
If the task is straightforward—reformat a date, translate a simple sentence, calculate a basic percentage or retrieve an obvious fact—there may be little useful search space to explore. Extra reasoning can add latency without improving the outcome.
There are also tasks where the limiting factor is missing information rather than reasoning. No amount of extra inference can recover a source the model was never given or verify a current fact without access to the relevant evidence.
And additional computation does not eliminate hallucination or reasoning error. A model can spend more effort defending a mistaken premise. Higher effort changes the opportunity for better work; it does not turn uncertainty into certainty.
Capability comes with latency and resource tradeoffs
Additional inference work consumes more computational resources and can increase response time. Providers may price or rate-limit higher-capability modes differently, although the exact product policy can vary.
The relationship is also subject to diminishing returns. Moving from a very low effort setting to a moderate one may produce a large improvement on a hard problem; spending substantially more may produce only a small additional gain.
That creates an optimization problem for product designers and users: use enough effort to solve the task reliably without allocating maximum resources to every trivial request.
A benchmark score without the reasoning setting can hide a major experimental variable
OpenAI’s GPT-5.6 evaluation material deliberately shows performance as reasoning effort changes rather than reducing every model to one immutable score. That is the right instinct for modern model evaluation.
If Model A is evaluated at a maximum reasoning setting with tools, while Model B is tested at a cheaper default setting without tools, the comparison is partly a comparison of configurations rather than purely of model capability.
A serious benchmark report should therefore identify the model version, reasoning level, tool access, agent scaffolding and test conditions. Otherwise readers cannot tell what produced the measured result.
More reasoning by one model is not the same as dividing work among several agents
OpenAI’s GPT-5.6 launch material distinguishes ordinary reasoning effort from its ultra mode, which coordinates subagents across parallel workstreams. That is a different mechanism.
One system may devote more inference effort to a single reasoning process. A multi-agent system can instead split a complex task into parallel subtasks, allow separate workers to investigate them and synthesize the results.
Both approaches can consume more compute. But they should not be described as the same thing, especially when reporting benchmarks or explaining product settings.
Sources