GPT-5 vs. Claude Sonnet 4: Comparing AI Reasoning Models

8/12/2025

Here’s the thing about the AI world right now: it’s moving so fast that keeping up feels like a full-time job. Just when you get comfortable with one model, a new one drops that completely changes the game. The latest heavyweight bout everyone’s talking about is between OpenAI’s GPT-5 & Anthropic’s Claude 4 series, specifically their approaches to “thinking” & reasoning.

There's a lot of chatter online, with some people swearing by Claude Sonnet 4's new "thinking mode" & others convinced GPT-5's reasoning is untouchable. Honestly, it's not as simple as one being flat-out better than the other. They're both powerhouses, but they think in fundamentally different ways. It’s like comparing a Formula 1 car to a world-class rally car—both are incredible machines, but built for different tracks.

So, let's cut through the noise. I've been digging into the benchmarks, the developer reports, & the early user feedback to really get under the hood of these two models. We're going to break down how each one approaches complex problems, where they shine, & where they stumble. This isn't about crowning a winner, but about understanding the very different philosophies driving the next generation of AI.

The New Kid on the Block: GPT-5's Adaptive Reasoning

First up is GPT-5. OpenAI didn't just release a bigger, faster version of GPT-4. They changed the entire engine. The biggest headline feature is something called Adaptive Reasoning.

For years, we’ve had to choose our models. Do you want the fast, cheap one for simple tasks or the powerful, slow one for heavy lifting? GPT-5 gets rid of that choice. It's not one model; it’s a whole system that decides for you how much "thinking" a prompt requires.

Here’s how it works:

Rapid Response Mode: If you ask a simple question, GPT-5 defaults to this mode. It’s optimized for speed & delivers quick, high-quality answers with minimal delay. Think of it as the system’s gut instinct.
Deep Reasoning Mode: When you throw a complex, multi-step problem at it, GPT-5 automatically kicks into a deeper mode. It engages in what OpenAI calls "internally simulated reasoning chains," dynamically figuring out how much "thinking depth" is needed. It essentially takes a moment to ponder, run through different scenarios, & formulate a more robust answer.

You don't toggle a switch; it just happens. The result is a conversation that feels way more fluid & natural. The model adapts to you, not the other way around.

How Good Is It, REALLY?

Pretty damn good, turns out.

On the coding front, it's showing major gains. On the SWE-bench Verified benchmark, which uses real-world Python tasks, GPT-5 scores a 74.9%, a big jump from previous models. It also uses fewer tokens & tool calls to get there, making it more efficient. Users who have tested it side-by-side with Claude 4 for coding report that GPT-5 is stronger at handling changes across multiple files & understanding project-wide dependencies.

But the most interesting part is how it thinks. One analysis used a tool called Infranodus to visualize the "knowledge graphs" of different models. The results were telling. When given a complex question, Claude 4 tended to focus heavily on the main topic, kind of getting biased by the initial prompt. GPT-5's thinking, on the other hand, was much more diverse. It explored more related concepts & subtopics, creating a richer, more comprehensive response. It’s like asking for directions & getting not just the route, but also suggestions for cool stuff to see along the way.

This adaptive approach also makes GPT-5 a beast at reasoning benchmarks. It's setting new records on tough tests like Humanity's Last Exam & Aider Polyglot. Even when it stumbles on a simple arithmetic problem, its process is what's impressive—it might decide to write & run a quick program to solve it, which is a smart way to ensure accuracy.

Claude Sonnet 4's Approach: Transparency & Control

Now, let's talk about Claude. Anthropic has been building a reputation for creating safe, reliable, & very capable models. The Claude 4 family continues this, with Sonnet 4 being the balanced all-rounder—perfect for enterprise workloads where you need a mix of speed & smarts.

Anthropic’s philosophy seems to be less about automatic magic & more about giving the user control & transparency. This is reflected in their latest features.

Instead of an automatic switch, the newer Claude 3.7 Sonnet introduced an "extended thinking mode." This is a toggle you can activate when you know you're dealing with a complex task that needs more detailed analysis. It’s a deliberate choice. You’re telling the model, "Okay, take your time on this one."

Even more unique is a feature some have called a “hybrid reasoning model.” This lets you see Claude's reasoning process in real time, almost like it's thinking out loud. You can watch it break down a problem step-by-step, which is HUGE for building trust & for catching potential errors before they derail the final output. This transparency is something you don't really get with other models.

Where Sonnet Shines

While GPT-5 might be winning on some reasoning benchmarks, Claude Sonnet 4 holds its own with a different set of strengths. In head-to-head comparisons for coding tasks, Sonnet 4 is often faster & more direct. It makes assumptions to get the job done quickly, which can be exactly what you need. If you're a developer who wants a quick, targeted code edit without a lot of extra chatter, Sonnet is often the preferred choice.

It's seen as an incredibly reliable model for production settings. There's a consistency to its output that businesses value. While GPT-5 might occasionally get verbose or over-explain, Sonnet tends to be more concise & focused.

The Claude 3 family, in general, has impressive multimodal capabilities, able to process images, charts, & PDFs alongside text. This makes it great for analyzing complex documents or reports where the information isn't just in paragraphs.

The Head-to-Head: Two Different Philosophies of 'Thinking'

So, when you put them side-by-side, the core difference isn't just about performance—it's about their entire approach.

Automatic vs. Manual:

GPT-5: Automatically adapts its reasoning depth based on your prompt. It's a "set it & forget it" system designed to feel seamless.
Claude Sonnet 4: Gives you the controls. You can toggle an "extended thinking mode" when you need it, giving you more deliberate power over the model's effort.

Thoroughness vs. Speed:

GPT-5: Tends to be more thorough, cautious, & willing to ask clarifying questions. It's great for complex debugging or tasks where you want every edge case covered. This, however, can make it slower.
Claude Sonnet 4: Is the speed demon. It's faster, more direct, & more decisive. It's built for rapid iteration & getting you an answer NOW.

Diversity vs. Focus:

GPT-5: Its thinking process is more diverse & explores more tangential ideas, leading to potentially richer & more creative outputs.
Claude Sonnet 4: Is more focused & can sometimes be biased by the initial prompt, sticking closer to the core subject without branching out as much.

Cost:

This is a big one. Early reports suggest GPT-5 is significantly cheaper than Claude Sonnet 4, sometimes costing two-thirds less for input & output tokens. For businesses running high-volume tasks, this could be a massive deciding factor.

Practical Applications: Where Does This Actually Matter?

This all sounds a bit abstract, so let's bring it down to earth. When would you use one over the other?

This is where the rubber meets the road for businesses trying to leverage AI. For example, in customer service, the type of chatbot you build depends entirely on the job it needs to do. This is a conversation we have with our users at Arsturn all the time. Arsturn is a platform that lets businesses create their own custom AI chatbots, trained on their specific data, with no code required. The goal is to provide instant, 24/7 support & engage with website visitors in a meaningful way.

Now, which of these new models would be better for an Arsturn-powered chatbot? It depends.

If you're an e-commerce store & your primary goal is answering common questions—"Where's my order?", "What's your return policy?"—you need speed & accuracy. You'd want a chatbot powered by an engine like Claude Sonnet 4. Its directness & rapid response time mean customers get their answers instantly without any fuss. The bot doesn’t need to explore the philosophical implications of return policies; it just needs to state it clearly & quickly.
On the other hand, imagine you're a SaaS company providing technical support for a complex software product. A customer might ask, "I'm trying to integrate your API with a custom-built legacy system, but I'm getting a persistent authentication error that seems to be tied to our firewall's packet inspection rules. How can I resolve this?" This isn't a simple lookup. It requires deep, multi-step reasoning.

For that kind of problem, you'd want a chatbot with an engine like GPT-5. Its ability to shift into a "Deep Reasoning Mode" would be invaluable. It could analyze the problem, consider multiple potential causes (API keys, firewall rules, legacy system quirks), & even suggest diagnostic steps. When you're trying to boost conversions or provide a truly personalized customer experience, this kind of deep problem-solving is what makes an AI feel genuinely helpful. Platforms like Arsturn help businesses build these no-code AI chatbots that can be trained on their own data, and the choice of the underlying AI's "thinking" style becomes a critical strategic decision.

The same logic applies elsewhere:

Creative Brainstorming: GPT-5's diverse thinking might be better for generating a wide range of ideas.
Quick Data Extraction: Claude Sonnet 4's speed would be ideal for quickly pulling specific information from a large document.
Complex Code Refactoring: GPT-5's ability to handle cross-file dependencies gives it the edge.
Generating a Fast First Draft: Sonnet's decisiveness could get you a solid draft faster.

So, is Claude Sonnet 4's Thinking Mode Superior?

Honestly, no. But neither is GPT-5's. The premise of the question is flawed because it assumes there's one "best" way to think.

The truth is, we're seeing a fascinating divergence in AI development. OpenAI is pushing for a highly capable, autonomous system that intelligently adapts to any task you throw at it. Anthropic is building an equally powerful system but with a focus on user control, transparency, & reliability.

GPT-5 feels like it's trying to be a true artificial general intelligence—a polymath that can reason, create, & problem-solve in a way that feels almost human. Claude Sonnet 4 feels more like the ultimate specialized tool—incredibly good at its job, fast, reliable, & designed to work with you, not just for you.

The "superior" model is the one that fits your specific needs, budget, & workflow. The real winner here is us, the users. For the first time, we have a meaningful choice not just in performance, but in the very philosophy of how AI should think. And that’s pretty cool.

Hope this was helpful in clearing things up. Let me know what you think