From GPT-4o to GPT-5: What's Next for Multimodal AI?

8/12/2025

Okay, let's talk about the next big leap in AI. Everyone was blown away by GPT-4o & its ability to handle voice, see the world, & chat in real-time. It felt like a scene out of a sci-fi movie. But the tech world never sleeps, right? The moment something incredible drops, the next question is always: "What's next?" & "How do we get there?"

So, the big question on my mind, & probably yours, is how does a future model, let's call it GPT-5 for the sake of conversation, not only meet but exceed what GPT-4o can do? It's not just about being a little faster or a little smarter. It's about overcoming the current hurdles & unlocking fundamentally new ways for AI to help us.

I've been digging into this, looking at what makes GPT-4o tick, where it still stumbles, & what the road ahead looks like. Here’s my deep dive on what it would take to get GPT-5 to match & surpass GPT-4o's incredible multimodal performance.

First, Let's Appreciate the Game-Changer: What Made GPT-4o So Special?

Before we look forward, we HAVE to understand the ground we're standing on. GPT-4o wasn't just another update; it was a fundamental shift in how AI models are built. The "o" stands for "omni," & for once, the marketing hype was pretty much on point.

The biggest deal with GPT-4o is that it’s a single, unified model. Before it, a task like having a voice conversation with ChatGPT was actually a clunky, multi-step process. You'd have one model for speech-to-text (transcribing your words), another model (like GPT-4) to figure out a response, & a third text-to-speech model to talk back to you. This pipeline worked, but it was slow & lost a TON of information along the way. It couldn't hear your tone of voice, the laughter in the background, or tell if multiple people were talking.

GPT-4o changed that entirely by being trained end-to-end on text, vision, & audio. This means ONE neural network processes everything. The result? It can respond to audio in as little as 232 milliseconds, which is basically human reaction time in a conversation. It can pick up on emotion, interrupt you (politely!), & even sing or laugh. It can look at a picture of a menu in another language & not only translate it but also tell you about the dishes. That's the magic of true multimodality.

This unified approach is what makes the interactions feel so fluid & natural. It's not just faking it; it's processing the world in a more holistic way, much like we do.

The Hurdles: Where Does GPT-4o Still Fall Short?

Even with all its magic, GPT-4o has its limits. Honestly, recognizing these limitations is the first step to figuring out what GPT-5 needs to solve.

Shallow Understanding & Reasoning Gaps: GPT-4o is amazing at pattern recognition, but it doesn't truly understand things in the way a human does. It can describe the objects in an image, but it can struggle with complex spatial reasoning or cause-and-effect. For example, it might identify a ball, a bat, & a person, but fail to infer that the person is about to hit the ball. This deeper, contextual reasoning is a major hurdle.
Real-Time Video is Still a Challenge: While GPT-4o can process images & audio in real-time, continuous, real-time video understanding is a different beast altogether. It involves tracking objects, understanding actions over time, & processing an immense amount of data without missing a beat. The current implementation is more like a series of very fast snapshots rather than a continuous stream of consciousness.
Context is Fragile: Like its predecessors, GPT-4o can lose the thread in long conversations. It has a limited memory window, & while it's gotten much better, it can still forget key details you mentioned earlier, leading to repetitive or inconsistent interactions. For an AI to be a true partner, it needs a persistent, long-term memory.
The "Hallucination" Problem: AI models sometimes just... make stuff up. They state things with utter confidence that are completely wrong. This is a massive problem, especially for mission-critical tasks. While better, GPT-4o isn't immune to this. Reducing hallucinations to near-zero is one of the biggest challenges for the entire field.
Bias & Safety Guardrails: AI is trained on human data, & human data is full of biases. OpenAI has put a lot of work into safety filters & guardrails, but these can sometimes be clunky or overly restrictive. The audio modalities also introduce new risks, like the potential for voice impersonation, that need even more sophisticated safety systems. The balancing act between being helpful & being harmless is incredibly delicate.

The Roadmap to GPT-5: What Needs to Happen Next?

So, how do we get from the impressive-but-flawed GPT-4o to a next-generation model that truly feels like a leap forward? It's not just one thing; it's about pushing the frontier on multiple fronts.

1. Architectures for Deeper Reasoning

The next step isn't just scaling up the current model; it's likely about new architectures. We're talking about models that have a more sophisticated internal world model.

From Pattern Matching to Causal Understanding: GPT-5 needs to move beyond just correlating words & pixels. It needs to build an internal model of how the world works. This means understanding cause & effect, physics, & human intentions. For example, if it sees a glass teetering on the edge of a table, it should be able to predict the consequence (it falling & shattering) without having seen that exact scenario a million times in its training data. This requires a shift from autoregressive prediction (what word comes next) to more structured reasoning.
Agentic Capabilities: This is a big one. The future of AI is likely "agentic," meaning the AI can take actions to achieve a goal. Imagine telling your AI, "Plan a weekend trip to San Diego for me." An agentic GPT-5 wouldn't just give you a list of flights & hotels; it would be able to browse websites, compare prices, check your calendar for availability, & even book the trip for you, asking for your confirmation at key steps. This requires the model to have tools, plan multi-step tasks, & learn from the results of its actions. Some speculative articles suggest this is a key focus for future models.

2. Mastering True Real-Time Interaction

To surpass GPT-4o, the feeling of real-time interaction needs to be flawless, especially with video.

Continuous Video Processing: GPT-5 will need to be able to process a live video feed as a continuous stream of information, not just a series of frames. This unlocks massive potential. A doctor could show a live feed of a patient's tremor to an AI for analysis, or a mechanic could point their camera at an engine & get real-time diagnostic help. This requires huge computational efficiency improvements.
Proactive Assistance: A truly advanced multimodal AI shouldn't always wait for a prompt. If it's your assistant & it's "seeing" your screen, it might proactively suggest, "Hey, I noticed you've been struggling with that spreadsheet formula for a few minutes. Want some help?" This moves the AI from a reactive tool to a proactive partner.

3. Building a Robust & Persistent Memory

The context window problem needs a more permanent solution.

Long-Term Memory & Personalization: GPT-5 needs a way to build a long-term memory of your preferences, past conversations, & key facts about you (with your permission, of course). This is how it goes from being a generic tool to being your AI. It would remember that you have a dog, that you're allergic to peanuts, or that you prefer a certain coding style. This would make every interaction radically more efficient & personalized.
Dynamic Resource Allocation: Some reports on future models speculate about dynamic resource allocation. This means the AI would use just a little bit of power for a simple question but could ramp up its "thinking" for a complex problem, dedicating more computational resources to reasoning, planning, or searching. This is way more efficient than using a sledgehammer for every nut.

4. The Business & Practical Application Layer

Here's the thing: all this amazing tech is only as good as its application. And this is where things get really interesting for businesses & everyday users. As these models become more capable, they will become the backbone of the next generation of digital experiences.

For example, think about customer service. Right now, many chatbots are... frustrating. They're glorified FAQ pages. But an AI with GPT-5-level capabilities could completely change the game. This is where a platform like Arsturn comes into the picture. Arsturn helps businesses build no-code AI chatbots that are trained on their own data. Imagine hooking an Arsturn-built bot, which already knows your entire product catalog & support documentation inside & out, to a GPT-5-level multimodal engine.

A customer could:

Show, not just tell: Instead of trying to describe a broken part on a product, a customer could just show it to the chatbot via their phone camera. The AI would visually identify the part, diagnose the issue, & pull up the exact replacement part to order.
Get guided help: The AI could walk them through a complex setup process in real-time, watching what they're doing & providing audio instructions, pausing when they get interrupted, & answering questions along the way.
Have natural conversations: The frustration of talking to a robot would disappear. The AI could understand tone, detect frustration, & respond with genuine empathy, providing instant, 24/7 support that feels human.

This isn't just about customer support. For lead generation, a platform like Arsturn allows businesses to create conversational AI that engages website visitors. With a future AI, this engagement could become deeply personalized. The AI could look at a business's website, understand its brand voice from the text & visual style, & automatically adapt its conversational style to match, creating a seamless brand experience that boosts conversions.

The Final Piece: Trust & Reliability

Finally, none of this matters if we can't trust it. For GPT-5 to truly surpass GPT-4o, it has to be reliable. This means:

Drastically Reduced Hallucinations: We need to be able to trust the AI's answers, especially in professional settings. This might involve new techniques where the AI learns to say "I don't know" or automatically fact-checks its own statements against reliable sources before presenting them.
Transparent & Controllable AI: Users need more control. We need to understand why the AI gave a certain answer & have the ability to correct it. Fine-tuning models on specific, high-quality data, like what a business does using a tool like Arsturn, is a step in this direction. It makes the AI an expert in a narrow domain, which inherently makes it more reliable for those tasks.

So, the path from GPT-4o to a hypothetical GPT-5 isn't just about more data & bigger computers. It's a journey toward deeper reasoning, more seamless interaction, persistent memory, & most importantly, trust. GPT-4o showed us a glimpse of what's possible when AI can see, hear, & speak. The next generation will be about what happens when it can truly understand, remember, & act.

Hope this was helpful & gives you a good sense of where things are likely headed. It's going to be a wild ride. Let me know what you think.