GPT-5 Reasoning Effort Levels Explained

8/12/2025

Here's the thing about AI: it’s moving FAST. One minute we're all figuring out how to prompt GPT-4, & the next, there's a whole new beast on the scene. Say hello to GPT-5. But this time, it's not just about being "better" or "smarter." It's about control. OpenAI just handed developers & businesses a set of dials to tune the model's very thought process.

I'm talking about the new "reasoning effort levels." Honestly, it’s one of the biggest leaps forward for practical AI use that I’ve seen in a while. It's like going from a car that only has an "on" & "off" switch to one with a gas pedal, clutch, & a full gearbox. You can now tell the AI how hard to think.

This is a game-changer for anyone from a solo developer tinkering on a weekend project to a massive enterprise looking to automate complex workflows. So, let's break down exactly what these reasoning levels are, what they do, & most importantly, when you should use each one.

So, What Exactly IS "Reasoning Effort"?

Before we dive into the different levels, let's get on the same page. In the context of GPT-5, "reasoning effort" is a new parameter developers can control in the API. Think of it as a dial for the model's "thinking time."

When you send a prompt to GPT-5, it doesn't just spit out the first thing that comes to mind. It performs a series of internal steps, generating what are called "reasoning tokens." These are hidden tokens that aren't part of the final answer you see, but they represent the model's internal monologue or chain of thought as it works through the problem. The more complex the problem, the more reasoning it might need.

The

reasoning_effort

parameter lets you directly influence how much of this internal work the model does. It's a direct trade-off between speed & quality.

More effort = deeper thinking, better accuracy on tough problems, but slower response times & higher costs (because those reasoning tokens count towards your output token usage).
Less effort = faster responses, lower costs, but potentially less accurate or nuanced answers for complex prompts.

This gives you granular control. You no longer have to use a sledgehammer to crack a nut. You can pick the right tool for the job, every single time.

The Four Levels of Thinking: A Complete Breakdown

GPT-5 offers four distinct reasoning effort levels:

minimal

low

medium

, &

high

. Each one is tuned for different types of tasks. Let's get into what each one feels like & where it shines.

1. Minimal Effort: The Need for Speed

This is the newest addition to the lineup & it's all about one thing: raw speed. When you set the effort to

minimal

, you're telling GPT-5 to do the least amount of thinking possible to get you an answer. It's designed to be quick & cheap, making it perfect for high-volume, low-complexity tasks.

Characteristics:

Fastest possible responses: Seriously, it's quick.
Lowest API cost: Uses the fewest reasoning tokens, so you save money.
Surface-level understanding: Don't expect it to unravel complex paradoxes. It's looking for the most direct path to an answer.

When to Use
1minimal
Effort:

Simple Autocomplete: Think finishing a sentence or a line of code where the context is super clear.
Basic Q&A: Answering simple, factual questions that can be found in a provided text.
Text Classification: Simple sentiment analysis (is this review positive or negative?) or categorizing articles.
High-Frequency Chatbots: For a bot that answers hundreds of basic questions a minute,
1minimal
effort is your best friend.

Honestly, don't underestimate this level. For a ton of everyday tasks, you don't need the AI to ponder the mysteries of the universe. You just need a fast, accurate-enough answer.

Minimal

delivers that.

2. Low Effort: The Smart & Snappy Default

Think of

low

effort as the new baseline for many standard tasks. It's a step up from

minimal

, engaging in a bit more thought, but still heavily prioritizing efficiency. It adds a little more nuance & understanding without bogging things down.

Characteristics:

Very fast, but with more context: It's better at picking up on subtleties in your prompt than
1minimal
.
Cost-effective: Still very light on the reasoning tokens.
Good for straightforward tasks: It's reliable for things that require a bit of understanding but not deep, creative problem-solving.

When to Use
1low
Effort:

Standard Customer Support: This is the sweet spot for many customer service interactions. The AI can understand the user's problem, pull the right information from a knowledge base, & provide a helpful answer without delay.
Content Summarization: Quickly summarizing an article, email thread, or report.
Data Extraction: Pulling specific information like names, dates, or company names from a block of text.
Generating Social Media Posts: Creating quick, engaging posts based on a link or a short brief.

This is where tools built on top of GPT-5 will likely spend a lot of their time. For instance, businesses looking to provide instant customer support can find immense value here. This is exactly the kind of thing we're passionate about at Arsturn. A business can use our platform to build a no-code AI chatbot trained on its own website data & documents. By operating at a

low

reasoning level, the Arsturn-powered bot can answer the vast majority of customer questions instantly & accurately, 24/7. It's about providing immediate value without the high cost or latency of more intense reasoning.

3. Medium Effort: The Balanced All-Rounder

This is the default setting for a reason.

Medium

effort is the jack-of-all-trades, offering a solid balance between performance & speed. It’s powerful enough to handle most of the tasks you'd throw at a premium AI model, from writing to coding.

Characteristics:

Great quality: This is where the AI really starts to "think." The answers are more comprehensive, creative, & well-structured.
Reasonable speed: It's not as instantaneous as
1minimal
or
1low
, but it's still very responsive.
The goldilocks zone: For most creative & professional tasks, this is the right place to start.

When to Use
1medium
Effort:

Content Creation: Writing blog posts, marketing emails, scripts, or reports.
Code Generation: Writing functional code snippets, translating code between languages, or explaining what a block of code does.
Detailed Analysis: Analyzing customer feedback, brainstorming ideas, or creating a business plan.
Complex Instruction Following: Any task that involves multiple steps, constraints, or a specific format.

When you're asking GPT-5 to be a creative partner or a junior developer,

medium

is your go-to. It has enough horsepower to produce high-quality work without the longer wait times of the highest effort level.

4. High Effort: The Heavy-Duty Thinker

When you have a truly hard problem—something that requires deep analysis, multi-step reasoning, or genuine ingenuity—you bring in the big guns. That's

high

effort. Setting the dial to

high

tells GPT-5 to take all the time it needs, use as many reasoning tokens as necessary, & explore different angles before giving you an answer.

Characteristics:

Maximum quality & accuracy: This is the best performance you can get out of the model. It excels at tasks where accuracy is CRITICAL.
Slower & more expensive: This performance comes at the cost of speed & money. The model generates significantly more reasoning tokens, which can add up.
Problem-solving powerhouse: It's designed to tackle novel problems & complex, layered instructions.

When to Use
1high
Effort:

Complex Architectural Decisions: Asking the AI to design a software architecture or a database schema.
Debugging Difficult Code: When you have a mysterious bug in a large codebase,
1high
effort can help trace the logic & find the root cause.
Scientific & Academic Research: Formulating hypotheses, analyzing complex data sets, or exploring connections in scientific literature.
Strategic Planning: Developing a multi-faceted business or marketing strategy that considers many variables.

You wouldn't use a race car to go to the grocery store, & you wouldn't use

high

effort for a simple question. This is your specialist tool. The performance metrics bear this out. OpenAI's own data shows that for a difficult visual reasoning benchmark called CharXiv Reasoning, cranking up the reasoning effort provides a significant boost in accuracy. Similarly, on SWE-bench, which involves fixing real-world GitHub issues, GPT-5's impressive 74.9% score was achieved using higher reasoning efforts. This proves that for the really tough stuff, more thinking time translates directly to better results.

It's Not Just About Effort: Verbosity, Models, & Context

While reasoning effort is the star of the show, it doesn't act alone. GPT-5 introduced a few other controls that work with it. There's a new

verbosity

parameter (

low

medium

high

) that controls how chatty the model is. You could ask for

high

effort reasoning but

low

verbosity output to get a deeply considered but concise answer. Pretty cool, right?

Plus, GPT-5 comes in different sizes: a full version, a

Mini

version, & a

Nano

version, each with its own pricing. You can combine these models with different reasoning levels to find the absolute perfect balance of cost & performance for your specific needs.

And let's not forget the massive context window: 400,000 tokens. This means you can feed the model entire codebases or massive documents, & it can maintain context throughout, something previous models struggled with.

How Businesses Can Leverage This Today

This level of control is HUGE for businesses. It moves AI from a powerful but blunt instrument to a precise, tunable engine.

Imagine a sophisticated customer journey. A customer first interacts with a chatbot on your website. For their initial, simple question ("Where is my order?"), the bot uses

minimal

effort for an instant response. This is where a platform like Arsturn comes in. It helps businesses build these no-code AI chatbots, trained on their own data, that can handle the frontline of customer engagement efficiently.

But let's say the customer's problem is more complex ("My order arrived with a damaged part, but it's part of a larger custom installation. What are my options for a replacement that fits the existing setup?"). The system can then escalate this to a

medium

or even

high

reasoning effort. The AI could analyze the order details, cross-reference them with installation manuals & inventory, & lay out a clear, step-by-step solution.

This is how you build truly meaningful connections with your audience. By using a conversational AI platform like Arsturn, you can create personalized chatbot experiences that adapt to the user's needs, using the right amount of AI firepower for each specific situation to boost conversions & provide unparalleled support.

Tying It All Together

So there you have it. The new reasoning effort levels in GPT-5 are a massive leap forward in making AI more practical, efficient, & controllable.

Minimal: For when you need speed above all else.
Low: For smart, quick, everyday tasks.
Medium: Your go-to for high-quality creative & professional work.
High: Your specialist for the toughest, most complex problems.

By understanding these levels & when to use them, you can get way more out of the model while also managing your costs effectively. It's about working smarter, not just having a smarter AI.

Hope this was helpful & gave you a clear picture of how to approach GPT-5's new capabilities. Let me know what you think, & what cool things you plan to build with it!