In the past few years, the pace of AI model development has been nothing short of extraordinary. Large Language Models (LLMs) have transformed how businesses interact with data, generate content, and even automate decision-making. But with the release of GPT-5, we’ve reached a turning point: a model that is not only an LLM but also natively multimodal.

At Burzcast, we work daily with AI-powered tools and architectures, integrating them into real-world, enterprise-grade solutions. Understanding the difference between GPT-4, GPT-4o, GPT-4.5, and GPT-5 is crucial for making informed decisions about which model best fits your business needs.

LLM vs. Multimodal: What’s the Difference?

Before we get into the details of GPT-5, let’s clarify two terms that are often used interchangeably but mean different things:

• Large Language Model (LLM): An AI model trained primarily to understand and generate text. LLMs can be incredibly sophisticated, reasoning across multiple steps and using massive context windows — but their core “native language” is still text.
• Multimodal Model: An AI model capable of processing and reasoning over multiple types of data — for example, text and images — within the same reasoning space. In future evolutions, this could expand to audio, video, and other structured data streams.

An LLM can be multimodal if it’s been trained to handle multiple input types directly, rather than relying on bolt-on components. That’s where GPT-5 changes the game.

How GPT-5 Differs Architecturally

The big shift is that GPT-5 is natively multimodal. That means:

• One unified token space for text and images, instead of separate encoders.
• Training from the start on both text and image data so the model learns how they interact.
• Single reasoning core that processes all modalities together without “switching modes.”

This is a departure from GPT-4’s approach, where multimodality was achieved by stitching together separate models — a text LLM plus a vision encoder — and merging their outputs in a “fusion layer” before reasoning.

• GPT-4: Text and images go through separate processing pipelines. They only meet at a later “fusion” step, which can limit cross-modal reasoning.
• GPT-5: Text and images are both converted into the same type of tokens from the beginning, letting the model reason about them in the same space without translation losses.

Why This Matters for Business

For enterprises, especially those working with data-rich, multi-format inputs, the native multimodal capability in GPT-5 unlocks new possibilities:

• Richer document analysis: Extract and cross-reference information from text, tables, and embedded diagrams in one pass.
• Advanced product support: Accept screenshots or diagrams alongside natural language queries for faster issue resolution.
• Enhanced creativity workflows: Combine text prompts with reference images for precise creative direction.
• Improved decision-making: Seamlessly integrate visual data into AI-driven reports and insights.

At Burzcast, we see GPT-5 as a foundational step toward AI systems that operate more like humans, perceiving and reasoning across different types of information simultaneously.

Looking Ahead

The journey from GPT-4 to GPT-5 is not just about more power — it’s about more integration. While GPT-4 introduced many to the concept of multimodal AI, GPT-5 delivers it natively, setting the stage for future models that might also unify audio, video, and real-time sensor data in the same reasoning core.

As we integrate GPT-5 into client solutions, our focus is on maximizing its multimodal strengths to deliver richer insights, streamline workflows, and open entirely new product categories.

At Burzcast, we don’t just use AI — we build with it.

If your organization is ready to explore the capabilities of GPT-5 in a secure, enterprise-ready environment, get in touch with us. Let’s create solutions that see the whole picture.