A developer I know spent an afternoon convinced GPT-5.5 had gotten worse. Every response to his formatting scripts came back bloated, second-guessing itself, wandering into edge cases that did not exist. Then he checked his config. He had pinned every call to the highest reasoning level, figuring more thinking meant better output. For a job that was mostly reformatting JSON, that was exactly backwards.
OpenAI gives GPT-5.5 five reasoning effort levels. The instinct for most people is to crank it to the top and wonder why the results feel off. The setting is a task-matching dial, not a quality slider. Open it all the way for a simple task and you get verbose, over-thought replies. Set it too low for a hard problem and you get shallow answers that skim the surface.
The five levels at a glance
| Level | Best for | Typical tasks | Speed | Cost |
|---|---|---|---|---|
| none | Lookup, format conversion | Translation, summary, formatting | Fastest | Lowest |
| low | Light analysis, everyday writing | Email, copy, simple scripts | Fast | Low |
| medium | Standard analysis, moderate complexity | Data analysis, code review, strategy notes | Medium | Medium |
| high | Deep reasoning, complex problems | System architecture, algorithm design, strategic planning | Slow | High |
| xhigh | Extreme complexity, multi-layer reasoning | Formal proofs, multi-constraint optimization, new-domain research | Slowest | Highest |
The sweet spot for most professional work sits at medium. That is not a compromise choice. It is where the model performs best when a task needs judgment but not an exhaustive chain of logic.
A 30-second decision tree
You do not need a flowchart tool for this. Three questions get you there.
First, does the task need reasoning at all? If you are asking the model to translate, convert a format, look up a definition, or summarize existing content, the answer is no. Use none. The model does not need to think, it just needs to run a known transformation.
Second, how many reasoning steps are involved? A single step, like a direct classification or one-shot analysis, calls for low. Two or three steps, like comparing options, weighing tradeoffs, or synthesizing multiple sources, calls for medium. Four or more, like recursive reasoning, multi-angle verification, or building a proof, calls for high or xhigh. A quick way to count: if you can explain the reasoning in one sentence, that is one step. If it takes a paragraph with a “but” and an “on the other hand,” that is two or three. If you would reach for a whiteboard and start drawing arrows, you are at four or more.
Third, is there a clear right answer? Open-ended work like creative writing, brainstorming, or loose advice does better one level down from wherever you landed. Tasks with a definite answer, like math problems, debugging, logic proofs, or fact-checking, can hold steady or move one level up. The reason is simple: tasks with a correct answer benefit from exhaustive reasoning because the model can check its own work. Open-ended tasks that get too much reasoning just spiral into self-doubt.
What this looks like in practice
Say you are writing a follow-up email to nudge a client toward responding to a quote, without sounding pushy. The tone and word choice matter, but the logical structure is simple. That is one step, so use low. Push it to medium and you get an email that reads like it went through a committee review.
Reviewing a pull request is different. Checking 200 lines of TypeScript for correctness and style means understanding intent, spotting potential bugs, and judging naming and architecture. That is two or three steps, so use medium. Go to high and the model starts questioning design choices that were fine all along.
Designing a microservice architecture for a SaaS with a million daily active users is another level entirely. Service boundaries, data consistency, latency budgets, team structure, deployment strategy, all of it interlocks. That is four or more steps, so use high.
Then there is the rare case that actually needs the top setting. Formally proving the optimal substructure property of a dynamic programming algorithm demands mathematical induction, proof by contradiction, and boundary verification, with strict logic at every step. That is where xhigh earns its cost.
Stop paying the reasoning tax on simple jobs
Each step up in reasoning level roughly doubles API cost and adds noticeable latency. If 80 percent of your calls are doing format conversion and simple generation, dropping them from medium to none or low can cut your bill by more than half.
In practice, batch jobs like data cleaning and format conversion belong at none. User-facing chat replies fit low, unless deep analysis is the whole point of your product. Internal tools and report generation land at medium. Core business logic and security audits justify high. Reserve xhigh for exploratory research during development, not production traffic.
Three common mistakes
The first is assuming xhigh is always more accurate. It is not. On simple tasks, the extra reasoning introduces over-thinking that drifts away from the best answer, because the model starts weighing edge cases that never existed.
The second is reaching for high reasoning on creative tasks. The opposite works better. Creative writing lands best somewhere between low and medium. High reasoning makes creative output conservative and formulaic, because the model self-censors its bolder ideas during the reasoning process.
The third is picking one level and never touching it again. The smart move is to adjust dynamically. Within a single workflow, use none for data preprocessing, medium for analysis, and high for the final decision. There is no reason to bind every stage to the same setting.
A checklist for developers
Set different reasoning_effort values for different API calls in your code rather than defaulting everything to one level. Validate your choices with A/B tests: run the same prompt at medium and high, then compare output quality against cost. Write a team baseline document for level selection so nobody is guessing. Monitor token consumption to find calls that are wasting reasoning budget. And for latency-sensitive scenarios like real-time chat, hard cap the level below medium.
The goal is not to think as much as possible. It is to think exactly as much as the task requires.
Related reading
- GPT-5.5 Reasoning Effort: A Developer’s Guide to Choosing the Right Level
- Cursor vs Copilot vs Claude Code vs Windsurf: Which AI Coding Tool Is Right for You in 2026?
- AI Image Generation Tools in 2026: Midjourney vs GPT Image vs Ideogram vs Flux
- Wispr Flow vs Superwhisper vs MacWhisper: Picking the Right AI Dictation Tool in 2026
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