ChatGPT and LLM Models: Everything You Need to Know in 2025

TL;DR

In 2025, ChatGPT will remain the leading AI assistant. This blog explains why it stands apart from GPT, how LLM models really work, where they’re applied across industries, how it compares to Gemini, Claude, and LLaMA, and what you should consider when choosing the right model for your needs.

Introduction: Why ChatGPT Still Dominates AI Discussions in 2025

By 2025, ChatGPT isn’t just an AI chatbot. It’s the interface millions rely on for work, learning, and creativity. Whether you’re a startup founder deciding between open-source vs enterprise AI, a developer questioning if GPT-4o can write half your code, or an educator in Manila using AI to teach at scale, one truth holds: understanding ChatGPT and LLM Models is no longer optional.

So why does ChatGPT dominate the AI conversation? 

Three reasons stand out:

1. Accessibility – It turned AI from a research lab curiosity into a tool anyone can use.

2. Capability – What began as a text bot is now a multimodal assistant handling text, code, images, and even voice.

3. Adoption & Ecosystem – With the largest user base, enterprise integrations, and plugins, ChatGPT has become the default AI platform.

This article isn’t just theory. It’s your playbook for 2025: what ChatGPT is, how the LLMs behind it work, why they matter for your role, and what the future looks like. 

To start, let's clear up the most common point of confusion: the difference between the product you use and the technology that powers it.

Is ChatGPT an LLM?

Yes. ChatGPT is an application powered by an LLM model (Large Language Model) called GPT. GPT is the underlying engine; ChatGPT is the user-facing product built on top of it. Unlike some open source LLM alternatives that require heavy setup, ChatGPT delivers an immediate, polished experience.

What is LLM in ChatGPT?

It refers to the underlying Large Language Model (like GPT-4o) that powers ChatGPT’s conversational abilities.

What is the difference between GPT and LLM?

LLM is the category (like “car”). GPT is one instance within that category (like “Tesla Model S”).

Is ChatGPT an LLM or generative AI?

It’s both. ChatGPT is an application of an LLM that performs generative AI tasks, producing text, code, and even multimodal outputs.

By the end of this article, you’ll know exactly how LLM models like ChatGPT work, their strengths and limitations, and where the future of AI is headed. In comparison, experimenting with an open source LLM such as LLaMA or Mistral can give businesses flexibility, but ChatGPT’s usability makes it the dominant choice.

GPT vs ChatGPT

One of the most common misconceptions is treating GPT and ChatGPT as the same. GPT is the raw engine; ChatGPT is the refined car built around it. GPT offers flexibility, but ChatGPT provides out-of-the-box safety, usability, and ecosystem integration.

For individuals and businesses, this distinction matters. Those with in-house AI expertise may prefer experimenting with an open source LLM, while others adopt ChatGPT for immediate productivity.

Quick Analogy Recap:

• GPT = Tesla engine.

• ChatGPT = Tesla Model S.

Open source LLMs (like LLaMA or Mistral) = DIY kit cars, powerful but requiring skill to assemble.

Why the Distinction Matters in 2025

For businesses and startups:

• Using GPT directly → Maximum flexibility. You can fine-tune, integrate, and control outputs — but you need technical expertise.

• Using ChatGPT → Faster time-to-market. Out-of-the-box productivity, with APIs and plugins ready.

For individuals:

• GPT is invisible (you don’t interact with it directly).

• ChatGPT is what you actually use on your phone, laptop, or inside enterprise tools like Microsoft 365.

Founder’s Takeaway:
For a VC-backed startup or scaling SME, the nuance here matters. Choosing between raw GPT access vs ChatGPT depends on whether you want flexibility (custom GPT integrations) or speed to market (ChatGPT as-is). That’s where partners like Better Software help evaluate trade-offs and build the right foundation.

The Technology Behind ChatGPT

To truly grasp why ChatGPT is so revolutionary, we need to look under the hood. While it feels like a simple chat window, it’s powered by one of the most complex and powerful technologies in modern AI: the Large Language Model (LLM). It’s not just a chatbot; it’s an application built on a sophisticated reasoning engine. Let's break down what that engine is and how it works.

What is LLM (Large Language Model)? 

When we define LLM, we’re talking about a Large Language Model trained on massive datasets of text, code, and symbolic data. Instead of being explicitly programmed for every task, an LLM learns patterns and generalizes. This is why modern LLM models can handle everything from natural conversations to image analysis.

• Large → Billions or even trillions of parameters.

• Language → Once text-only, now extended to images, audio, and beyond.

• Model → A mathematical engine predicting the most probable next word, token, or symbol.

Think of an LLM as an engineered intuition machine: it doesn’t memorize answers, it learns how to guess well across thousands of domains. In simple terms, LLM is essentially a predictive system that adapts to context across multiple domains.

And naturally, once you understand what an LLM is, the next question becomes:

How does LLM actually work?

An LLM works by learning patterns from huge datasets, not fixed rules. It encodes text into numbers, uses transformers to weigh context, and generates outputs by predicting sequences. This makes it versatile across tasks, from casual chat to medical analysis. 

Core Traits of LLM Models

1. Transformer Architecture

   • Introduced in 2017’s landmark paper Attention Is All You Need.

   • Uses self-attention: every word is weighed relative to others in a sentence.

   • Example: In “He went to the bank to withdraw cash,” the model knows bank = financial institution (not riverbank) because of “withdraw cash.”

2. Massive Datasets

   • Trained on books, research papers, websites, forums, and even code repositories. This diversity lets LLM models jump from Shakespearean poetry to Python debugging in seconds.

3. Billions to Trillions of Parameters

   • Parameters = the model’s “memory slots.” Each one encodes a fragment of knowledge.

   • GPT-3 (2020) → 175B parameters.

   • GPT-4 (2023) → estimated >1T parameters.

   • GPT-4o (2024) → multimodal with smarter compression, so more powerful at similar compute cost.

   • Analogy: More parameters = more “neurons,” like adding layers of intuition.

The bigger the parameter count (with proper training), the richer the model’s ability to represent nuance.

Now that we understand the engine, we can appreciate the final product. ChatGPT is the application layer built on top of OpenAI’s GPT models, making this powerful technology accessible to everyone.

Understanding ChatGPT

When ChatGPT launched on GPT-3 in 2020, the world was stunned at how human its text felt. GPT-4 in 2023 added deeper reasoning. Now, GPT-4o (2024) powers ChatGPT, the first truly multimodal LLM that blends text, image, and audio in a single interface. Unlike an open source LLM, which often requires setup and customization, GPT-4o is available out-of-the-box for anyone to use.

Current Capabilities

• Text → Essays, scripts, research reports.

• Code → Debugging, scaffolding, database queries.

• Images → Descriptions, charts, and edits.

• Audio/Voice → Conversational interactions.

• Reasoning → Chain-of-thought responses, planning, and strategy.

Real-World Impact Across Industries

This technological leap isn't just theoretical; it's actively reshaping industries. By 2025, LLM adoption is standard practice for gaining a competitive edge.

• Business Automation: According to a 2024 McKinsey report, 63% of US enterprises use LLMs like ChatGPT for tasks like customer support automation, dramatically reducing response times.

• Software Development: The 2024 GitHub Annual Report revealed that AI copilots, powered by LLM models, are now involved in writing nearly 46% of all new code, massively accelerating development cycles.

• Education: In regions like the Philippines, schools are deploying AI-powered tutors to provide personalized learning support, helping to supplement limited teacher capacity.

• Healthcare: Hospitals use variants of ChatGPT for summarizing patient records and assisting with triage, while others explore customizable open-source LLMs for more localized needs.

With such widespread use across industries, one common question often arises:

Which ChatGPT model is best for translation?
Given its advanced multimodal and reasoning capabilities, GPT-4o is the top choice for translation. It handles over 50 languages with near-human fluency, making it invaluable for global businesses. 

What Makes ChatGPT an LLM?

Most people think of ChatGPT as “just a chatbot.” Under the hood, it’s much more: it’s powered by one of the most advanced LLM models ever built — GPT-4o.

Why ChatGPT Qualifies as an LLM

ChatGPT is powered by GPT-4o, which ticks every box: transformer-based architecture, internet-scale data, and billions of parameters. Unlike an open source LLM where developers must fine-tune for usability, ChatGPT arrives ready for adoption, making it the most mainstream example of a modern LLM model.

• Transformer-based architecture → enabling long-context reasoning.

• Trained on internet-scale data → making it versatile across domains.

• Billions of parameters → capturing subtle relationships between words, images, and concepts.

This combo lets it model patterns and relationships—not just generate fluent text—making it a textbook example of a modern LLM productized for everyday use. These technical foundations translate directly into the practical capabilities that millions of users rely on daily.]

What this enables in practice

1. Zero-/few-shot learning. Adapts to new tasks from instructions or a single example—no retraining required.

2. Conversational memory. Maintains and uses prior context, improving tutoring, support, and complex workflows.

3. Code generation & debugging. Scaffolds features, writes tests/SQL, and explains errors in natural language.

4. Step-by-step reasoning. Works through problems methodically for planning, math, and strategy when prompted appropriately.

5. Multimodal I/O. Understands text, images, and audio in one session—e.g., describe a chart, extract data, then continue by voice.

Bottom line: ChatGPT is an LLM-powered, multimodal reasoning assistant—the GPT engine plus the product layer (UI, safety, integrations) that makes advanced capabilities usable at scale.

To see what this means in practice, it helps to compare a generalist tool like ChatGPT with highly specialized, domain-specific LLMs.

Case Example: Finance vs Healthcare

• In finance, BloombergGPT (a domain LLM) can parse market filings with superhuman speed.

• In healthcare, Google's MedPaLM has been shown to reduce hospital documentation time by ~40%.

While these specialist models outperform in their niche, ChatGPT-4o demonstrates remarkable versatility. It can handle both of these distinct tasks with strong general performance, making it the default choice for a wide range of applications where flexibility is key.

Breaking Down GPT

So far we’ve talked about GPT as the “engine,” but what exactly makes it tick? Let’s unpack the acronym itself:

• Generative → GPT doesn’t just analyze; it creates. It can generate text, code, summaries, images (through multimodal extensions), and more.

• Pre-trained → Before you ever ask it a question, GPT has already absorbed patterns from massive datasets — books, research papers, websites, and even code repositories.

• Transformer → The underlying architecture that gives it context-sensitivity, powered by the breakthrough self-attention mechanism.

While understanding LLM, these same principles apply: generative ability, pre-training, and transformer architecture together make modern LLM models powerful and adaptable across domains.

And at this point, the most straightforward question people raise is:

What is the full form of GPT?

GPT stands for Generative Pre-trained Transformer. It generates new content, is pre-trained on massive datasets before fine-tuning, and uses the transformer architecture with self-attention to understand context and produce fluent, adaptable outputs.

How Transformers Work

Traditional models like RNNs read text one word at a time, often “forgetting” earlier context. Transformers flipped this by using self-attention, every word is compared with every other word to understand relationships.

• Example:

  • Ask a rule-based bot: “Explain photosynthesis for a 5th grader.” → It likely fails or gives textbook jargon.

  • Ask GPT-4o: It tailors the explanation to a child’s level, because it has learned both the concept and how to match tone.

Analogy: Imagine reading a paragraph while highlighting important words that shape meaning. That’s what transformers do at scale, billions of times per second. This is also why the transformers are at the core of what differentiates them from older architectures.

Why Pre-training Matters

Instead of starting from scratch for every task, GPT comes with a “world model” already in place. Fine-tuning or prompting just adapts it to the task at hand. For businesses, this is the reason LLM models are so efficient, they arrive with broad knowledge that can be customized for niche applications.

Example: A hospital using GPT doesn’t need to retrain it on human language; they only fine-tune it with medical records for clinical reasoning.

This naturally brings up the next question about their inner workings:

Why is LLM important in AI?

LLMs are important because they provide the foundation for modern AI applications. They enable natural conversations, reasoning, and content generation across domains, making AI usable in education, business, healthcare, and software development at global scale.

Bottom line: GPT’s power comes from this trio: Generative output, Pre-training on massive data, and Transformer architecture. Together, they explain why GPT and other LLM models are so adaptable,  from writing poetry to analyzing X-rays.

Types of LLMs in 2025

LLM models are not one-size-fits-all. By 2025, they fall into four main categories, each serving different business and research needs. It’s important to remember that the term covers a broad spectrum of architectures and use cases.

1. Language Representation Models

Models like BERT and RoBERTa excel at understanding text but are limited in generating it. They power search engines, recommendation systems, and classification tasks where comprehension matters more than creativity. Think of them as expert “readers,” not “writers.”

2. Zero-shot & Few-shot Models

These adapt to new tasks with little or no retraining. For example, GPT-4o can generate SQL queries after seeing one example. This flexibility makes LLM models invaluable for enterprises that want efficiency without costly data-labeling pipelines.

3. Multimodal LLMs

The frontier in 2025. GPT-4o and Google Gemini process text, images, and audio seamlessly. They enable workflows like describing an X-ray, summarizing it in natural language, and suggesting follow-up steps, all in one system. When we define LLM in today’s context, multimodality has become a core expectation rather than an experimental feature.

4. Domain-Specific LLMs

Specialized models fine-tuned for industries. MedPaLM helps doctors reduce paperwork by 40%. BloombergGPT parses financial filings for investors. LegalBERT reviews contracts. These LLM models trade general versatility for deep accuracy in high-stakes domains where precision matters most.

After looking at the main categories, the simplest way to illustrate them is with concrete examples:

What is an example of an LLM?
Examples of LLMs include GPT-4o (used in ChatGPT), Google Gemini, Anthropic Claude 3, and open-source LLaMA 3/4. Each processes language at scale, with some also handling multimodal inputs like images or audio for broader applications.

Beyond just examples, readers often want to know what makes these models so useful in practice:

What are the advantages of using LLM?
LLMs provide adaptability across tasks, from coding to translation, without retraining. They enable natural conversations, reduce development costs, and accelerate workflows. Multimodal LLMs add further advantages, processing text, images, and audio together for richer, context-aware decision-making in businesses and education.

ChatGPT vs Other LLMs

By 2025, the LLM field will be crowded. Google, Anthropic, Meta, and open-source communities all offer powerful alternatives. But ChatGPT remains the most widely adopted. Here’s why.

What Makes ChatGPT Stand Out

• Conversational Fine-Tuning: Reinforcement Learning from Human Feedback (RLHF) makes ChatGPT’s tone and reasoning more natural than raw models.

• Mass Adoption: With hundreds of millions of users, it’s the “default AI” for work, education, and personal use.

• Plugin Ecosystem: Third-party extensions make it adaptable to custom workflows.

• Multimodality: GPT-4o integrates text, images, and audio, unlike many single-mode competitors.

Competitors in 2025

• Google Gemini: Strong in reasoning and multimodal tasks, with tighter Google ecosystem integration.

• Anthropic Claude 3: Safer and more cautious, often preferred in compliance-heavy industries.

• Meta LLaMA 4 & Mistral: Open-source LLMs giving enterprises control, but requiring technical expertise.

Quick Pros/Cons Snapshot

Given this crowded field, people often start with the most basic question:

Which AI LLM is best?
In 2025, ChatGPT (GPT-4o) is the most widely adopted LLM due to its multimodal power and ecosystem. However, “best” depends on needs — Gemini excels in Google integration, Claude in safety, and LLaMA in open-source flexibility.

Of course, many also frame it differently, asking whether anyone has truly surpassed ChatGPT:

Is there a better LLM than ChatGPT?
“Better” depends on context. For consumer use and scale, ChatGPT leads. In compliance-heavy industries, Claude is favored. For customization and cost control, LLaMA 4 or Mistral may outperform ChatGPT, though they require technical expertise to deploy.

Naturally, one of the most frequent comparisons readers make is with Google’s flagship model:

Is Google Gemini better than ChatGPT?
Gemini matches ChatGPT in multimodal reasoning and integrates seamlessly with Google products, making it strong for enterprise productivity. ChatGPT, however, has broader adoption, plugins, and community support.

In regulated sectors, another common comparison comes up:

Is Claude better than ChatGPT?
Claude is often seen as “safer,” with cautious outputs and longer context handling. ChatGPT, however, is more versatile, creative, and widely supported. For regulated industries Claude may be better, but for general productivity and adoption, ChatGPT dominates.

For developers, the big question shifts toward open-source alternatives:

Is LLaMA 4 better than GPT-4?
LLaMA 4 offers open-source freedom, lower costs, and research flexibility. GPT-4o remains stronger for multimodal reasoning, enterprise readiness, and real-world adoption. LLaMA suits developers needing transparency and control; GPT-4 suits users prioritizing scale, performance, and minimal infrastructure setup.

Finally, at the highest level, readers ask the ultimate benchmark question:

Which is the most advanced LLM?
As of 2025, GPT-4o is the most advanced LLM in mainstream use, thanks to multimodal integration and reasoning power. Open-source challengers like LLaMA 4 and Mistral push innovation, but GPT-4o still leads in adoption and enterprise-grade performance.

Challenges of ChatGPT & LLMs

Even though ChatGPT (GPT-4o) dominates in 2025, LLM models are not flawless. To use them responsibly, you need to understand their biggest challenges and how to address them. When you try to define LLM in practice, it’s not just about capabilities but also about limitations and risks.

Bias and Fairness

LLM models inherit biases from their training data, which can skew outputs in hiring, recommendations, or education. Anyone asking “what is LLM doing in recruitment or education?” must consider the bias factor.

• Risk: Job AI favoring certain demographics.

• Mitigation: Use domain-tuned models, apply bias filters, and implement human review in critical decisions.

Hallucinations

LLMs sometimes produce convincing but false answers, especially in niche or fact-dense fields. This is why businesses that want to use LLM effectively must include safeguards against misinformation.

• Risk: A health chatbot suggesting incorrect treatments.

• Mitigation: Pair LLMs with Retrieval-Augmented Generation (RAG) so outputs are fact-checked against trusted sources.

This leads to a pressing concern that almost every AI practitioner raises:

What is the biggest problem with LLM?

The biggest problem with LLMs is hallucination, generating fluent but false or misleading answers. This, combined with bias and cost issues, makes trust and responsible deployment the core challenges in 2025.

Cost & Environmental Impact

Training GPT-4 cost over $100M and consumed energy equivalent to powering 120 U.S. homes for a year. Running models at scale also raises cloud costs. For leaders evaluating “what is LLM going to cost my organization?”, these concerns are critical.

• Mitigation: Use smaller models for lightweight tasks and optimize inference with quantization.

 (source: OpenAI estimates).

Ethical & Security Concerns

LLMs bring new risks in 2025:

• Copyright: Authors vs AI-generated content.

• Misinformation: Deepfakes in elections.

• Privacy: Enterprise data leaks through prompts.

• Mitigation: Governance frameworks, prompt monitoring, and closed-loop enterprise deployments are becoming standard. 

Given these risks, one of the biggest societal debates centers on jobs:

Can LLMs replace human jobs?
LLMs automate repetitive tasks like coding, writing, and customer support, reshaping roles in the process. They don’t fully replace humans but augment workers while eliminating some entry-level or routine functions.

Business & Real-World Applications

By 2025, LLMs are no longer experiments, they’re embedded in industries worldwide. From startups to Fortune 500s, companies rely on ChatGPT and its competitors to scale operations, cut costs, and unlock new value.

Key Industries

• Customer Support: 24/7 chat agents cut costs by 60%.

• Marketing: Personalized ad copy across 50+ languages.

• Software Development: AI copilots accelerating release cycles.

• Education: Tailored learning modules.

• Healthcare: Faster patient record analysis.

All of this naturally raises the question of what these models are actually built to do day-to-day:

What is an LLM mainly used for?

LLMs are mainly used for language-based tasks such as writing, translation, coding, summarization, and Q&A. With multimodal extensions, they also handle images and voice, powering real-world use cases like copilots in healthcare, finance, and education.

Why This Matters for Founders

For VC-backed startups, scaling SMEs, and non-technical founders, adopting LLM models is not just about trendiness, it’s about competitive survival.

This is where Better Software steps in:

• Engineering-first approach → scale-safe stacks.

• AI-ready delivery → embed LLMs into workflows.

• Trusted partner → no agency trauma, just solid foundations.

Why is LLM important in AI?

​​LLMs are important because they provide the foundation for modern AI applications. They enable natural conversations, reasoning, and content generation across domains, making AI usable in education, business, healthcare, and software development at global scale.

The Future of ChatGPT and LLMs

By 2025, LLMs are the backbone of modern AI. But what comes next? Over the next five years, expect rapid evolution that reshapes industries and daily life. 

Predictions for 2025–2030

• Open-source boom: MModels like LLaMA 4 and Mistral are democratizing AI. Open-source gives enterprises control, lowers costs, and accelerates experimentation. At the same time, businesses evaluating the “top rated LLM for chat” must weigh whether to prioritize open-source flexibility or proprietary performance.

• Enterprise copilots: By 2026, Gartner predicts 80% of enterprises will adopt AI copilots. These copilots won’t just suggest code, they’ll guide marketing campaigns, monitor financial risk, and even support medical diagnostics. ChatGPT APIs and domain-specific LLMs will become standard corporate infrastructure. For leaders asking “what is LLM going to look like inside my workflows?”, copilots are the answer.

• Towards AGI? While Artificial General Intelligence (AGI) remains distant, every step in LLM reasoning narrows the gap. GPT-4o’s multimodality is a preview: future models may blend text, image, voice, and video into unified intelligence. Choosing the “top rated LLM for chat” in this landscape will depend on balancing raw capability with ethical deployment. 

Looking at today’s adoption curve, the first question leaders ask is about demand:

Which LLM is most in demand? 

​​In 2025, GPT-4o (ChatGPT) is the most in-demand LLM due to its multimodal abilities, wide adoption, and enterprise integrations. Open-source options like LLaMA 4 and Mistral are rapidly gaining traction among developers and research-focused companies.

Among open-source challengers, one name consistently comes up at the top:

What is the most powerful open-source LLM?

As of 2025, LLaMA 4 is considered the most powerful open-source LLM, offering high performance with transparency and flexibility. Mistral also leads in efficiency, enabling enterprises and researchers to deploy advanced AI without proprietary restrictions.

Naturally, this leads to the strategic question every enterprise must weigh:

Is open-source LLM better than paid LLM? 

It depends on goals. Open-source LLMs provide control, transparency, and cost savings, ideal for customization. Paid LLMs like GPT-4o offer superior performance, safety, and enterprise readiness. Many organizations use a hybrid approach, open-source for experimentation, paid APIs for scale.

Conclusion

By 2025, ChatGPT has proven itself as one of the most widely adopted LLM models — not because it’s flawless, but because it combines usability, scale, and multimodal power in a way no other AI has matched. For startups and SMEs, the question isn’t “Should we adopt AI?” but rather “Which approach to LLM models best fits our strategy?”

Some enterprises prefer the safety and speed of proprietary systems like GPT-4o, while others experiment with an open source LLM such as LLaMA 4 or Mistral for cost savings and flexibility. For business leaders asking “what is LLM going to mean for my industry?” the answer lies in use cases from healthcare to education and customer support.

As competition intensifies, every company will evaluate the top-rated LLM for chat based on its balance of accuracy, adoption, and ecosystem support. The future isn’t about whether ChatGPT will dominate forever; it’s about how proprietary and open-source LLMs together will reshape every role, workflow, and industry between now and 2030.

Summary

This article explores ChatGPT and LLM models in 2025, breaking down what makes ChatGPT the most widely adopted AI assistant. It covers the evolution from GPT-3 to GPT-4o, explains how and why ChatGPT qualifies as one. You’ll learn the difference between GPT and ChatGPT, the architecture behind LLMs, and the rise of multimodal AI. The blog compares ChatGPT with rivals like Google Gemini, Meta LLaMA, Anthropic Claude, and Mistral, showing how enterprises weigh the benefits of a proprietary system versus an open sourceLLM. For readers still wondering “what is LLM really used for?”, the article highlights real-world applications in education, healthcare, coding, and business. It also addresses challenges like hallucinations, bias, and environmental cost, and looks ahead to trends such as enterprise copilots, the top rated LLM for chat, and the road toward AGI..