Why GPT-5 Feels Different & How Its New AI System Works

8/12/2025

So, you’ve been playing around with GPT-5, the shiny new model from OpenAI, & you've noticed something… a little weird. You ask it a question that you know GPT-4o would have understood instantly, picking up on all the unspoken cues & context. But GPT-5… it just sits there, waiting for you to spell things out.

It can feel like a step backward, right? You’re not imagining it. Turns out, there’s a pretty interesting reason for this change, & it has everything to do with a fundamental shift in how GPT-5 is designed. It's not that GPT-5 is less capable—in many ways, it's SIGNIFICANTLY more powerful. But to understand its behavior, we need to peek under the hood.

Here's the thing: you're not really talking to a single "GPT-5" anymore. You're interacting with a whole new kind of system.

The Big Change: GPT-5 Isn't One Model, It's a System with a "Router"

This is the absolute core of the issue. With GPT-4o, you were essentially interacting with a single, very smart, all-purpose model. It was trained to be a jack-of-all-trades, handling everything from casual conversation to complex coding problems.

GPT-5 is different. OpenAI has moved to what they call a "unified system." Think of it like a company's front office. When you walk in with a request, there's a receptionist—a "router"—who listens to what you need & then directs you to the right specialist.

This is pretty much how GPT-5 works. When you enter a prompt, a real-time router analyzes it & decides which internal model is best suited for the job. It asks itself questions like:

Is this a simple, casual chat question?
Does this require deep, multi-step reasoning?
Is the user asking me to perform a complex coding task?
Does the user seem to be in a hurry?

Based on its assessment, it sends your prompt to one of two main destinations: a "fast & smart" model for quick, everyday queries, or a "deeper reasoning" model for the heavy lifting. The goal is to give you a seamless experience without you having to manually switch between models.

So, what does this have to do with you having to spell everything out?

Well, if your prompt is even slightly ambiguous or lacks explicit detail, the router might play it safe. It might assume you want a quick, surface-level answer & send your query to the faster, less resource-intensive model. That model, while efficient, may not have the deep, nuanced understanding of implicit context that the full-blown reasoning model does. It’s optimized for speed, not for reading between the lines.

This is why a prompt that worked perfectly with GPT-4o might fall flat with GPT-5. GPT-4o was the deep reasoning model, all the time. With GPT-5, you're not always talking to that part of the system unless you make it clear that's what you need.

"Reasoning Effort": The New Control Knob You Didn't Know You Had

This brings us to the next big piece of the puzzle: a new feature in the API called "reasoning_effort." This parameter gives developers—& by extension, the systems built on top of the API—more control over how much "thinking" the model does before giving an answer.

The options typically range from "minimal" to "high."

Minimal: This setting is designed for speed. It tells the model to generate a response as fast as possible with very few "reasoning tokens" (the internal thoughts of the model).
Low/Medium: A balance between speed & thoughtfulness. This is often the default.
High: This is for when you need the model to go deep. It will spend more time & resources analyzing the problem, which is great for complex tasks but comes at the cost of speed & more token usage.

Here's the kicker: for many everyday interactions, the system is likely defaulting to a lower reasoning effort to keep things snappy. This is a deliberate design choice. OpenAI found that for many tasks, you can get great results with less "thinking" time, which makes the whole experience faster & more efficient.

But it also means that if you're asking something that requires inferring a lot of context, the model might not be giving itself enough "time to think" to connect the dots. It's waiting for you to provide the explicit instructions that will either a) make the answer obvious even for the fast model, or b) signal to the router that this query needs to be escalated to the deep reasoning model with a higher reasoning effort.

This is a trade-off. In its quest for efficiency & precision, GPT-5 sometimes sacrifices that uncanny, almost psychic understanding that made GPT-4o feel so magical.

Is GPT-5 Actually Smarter? Yes, Much Smarter.

Now, it's SUPER important to understand that these changes don't mean GPT-5 is a downgrade. In fact, on almost every measurable benchmark, it's a massive leap forward.

Coding & Math: The improvements here are staggering. On SWE-bench, a benchmark for real-world software engineering tasks, GPT-5 scores significantly higher than its predecessors. It's better at understanding complex codebases, fixing bugs, & even has a better grasp of aesthetics in front-end development. I've seen examples where GPT-4o would get a math problem completely wrong, while GPT-5 nails it on the first try.
Factuality & Reduced Hallucinations: One of OpenAI's big goals with GPT-5 was to make it more reliable. They claim it's 45% less likely to have a factual error compared to GPT-4o. It's also less prone to just making stuff up, which is a HUGE deal for anyone using AI for research or work.
Instruction Following: GPT-5 is much better at following complex, multi-step instructions & using tools. This makes it more "agent-ready," meaning it's better at carrying out tasks that require it to interact with other software or APIs.

So, the power is there. The "problem" is that it's now a more specialized & controlled power. It's less of an intuitive, creative muse & more of a precise, high-performance engine.

How This Affects Businesses & AI Integration

This shift has some pretty big implications for businesses using AI. While the "out-of-the-box" conversational experience might feel a bit different, the new architecture opens up a ton of possibilities for creating more tailored & effective AI solutions.

This is where conversational AI platforms come into the picture. For example, a platform like Arsturn helps businesses harness the raw power of models like GPT-5 & shape it to their specific needs. Instead of just relying on the default, generic behavior, Arsturn allows you to build no-code AI chatbots trained on your own data.

This means you can create a customer service bot that doesn't just have general knowledge but has a deep, ingrained understanding of your products, your policies, & your customers' common questions. By training it on your company's knowledge base, FAQs, & support logs, you're essentially providing all that "obvious context" upfront.

The bot then doesn't have to guess or rely on a generic model's interpretation. It has the specific, explicit information it needs to provide instant, accurate support 24/7. This is how you move from a general-purpose AI to a specialized business tool that can genuinely boost conversions, improve customer engagement, & provide personalized experiences at scale. The power of GPT-5 is there, but platforms like Arsturn provide the framework to focus that power precisely where your business needs it most.

So, How Should You Talk to GPT-5?

Getting the hang of GPT-5 requires a slight mental adjustment. Here are a few tips:

Be More Explicit: If you're not getting the depth you want, don't be afraid to spell things out. Instead of asking, "What are the main takeaways from this?", you might try, "Analyze this report & identify the three most critical strategic risks for a company in the manufacturing sector."
Use Trigger Phrases: Sometimes, you can nudge the router in the right direction. Phrases like "think hard about this" or "analyze this step-by-step" can signal to the system that you need the deeper reasoning model.
Provide Context Upfront: If you're starting a complex conversation, give it the background information it needs in the first prompt. Think of it as briefing a new team member.
Embrace the Specialization: For technical tasks, lean into GPT-5's strengths. Give it your code, your data, your complex problems. This is where it truly shines & outperforms GPT-4o by a country mile.

The Bottom Line

The feeling that GPT-5 requires you to spell out obvious context isn't just in your head—it's a direct consequence of its new, more complex architecture. By implementing a router & different levels of reasoning effort, OpenAI has made a deliberate trade-off: they've exchanged some of GPT-4o's free-flowing, intuitive feel for greater speed, efficiency, & precision.

GPT-5 is undeniably a more powerful & capable tool, especially for specialized, high-stakes tasks. But getting the most out of it means understanding that you're no longer talking to a single entity. You're talking to a system, & learning how to navigate that system is the key to unlocking its full potential.

It’s a different kind of intelligence. It might feel less like a mind-reader & more like a hyper-competent, literal-minded assistant. And honestly, for a lot of real-world applications, that might be exactly what we need.

Hope this was helpful & cleared things up a bit. Let me know what you think