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The Stack Underneath
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You've heard of ChatGPT. At this point, almost everyone has. But most people using it daily have no idea what's actually running underneath it, or why it can write an email, debug code, and explain a legal clause in the same breath.
That's what this article is about.
In this article, we will explore the transformative impact of LLMs, which has disrupted traditional technological norms.
A large language model is a type of AI trained on massive amounts of text data to understand and generate human language. Not just match keywords. Actually understand context, intent, and meaning.
The "large" part isn't marketing. These models are trained on billions of words, books, articles, code, and conversations until patterns in language become something the model can predict and produce.
OpenAI's GPT (generative pre-trained transformer) is the most recognised example. It's what powers ChatGPT, and quietly runs inside millions of other apps you probably use without knowing it.
Not sure which LLM fits your use case? We help businesses move from model selection to working production systems. Talk to us.
Let's take a look at what they can do and where businesses can use them.
Got a 40-page report that needs to become a 3-paragraph brief? LLMs handle that. They pull out the key information and cut everything else, without losing what actually matters.
This is what most people think of first. LLMs power chatbots and virtual assistants that don't just answer questions, they hold context across a conversation. Ask a follow-up, change your mind mid-thread, circle back. They keep up.
LLMs can read a piece of text and tell you how it feels, positive, negative, or somewhere in between. Useful for customer feedback, brand monitoring, and anywhere opinion data needs to be processed at scale.
You give it a prompt. It gives you a paragraph, a draft, an outline, a starting point. Writers use it to beat the blank page. Marketers use it to move faster. It won't replace good judgment, but it removes the friction of getting started.
Less talked about, but real. LLMs can run interactive, text-driven experiences where the story or scenario responds to what the user does. Training simulations, onboarding flows, narrative games, all viable.
Researchers use LLMs to scan literature, surface relevant findings, and draft hypotheses faster than any manual process allows. It doesn't replace domain expertise. It removes the grunt work so experts can focus on what actually requires their judgment.
Describe what you need in plain English. Get working code back. Not always perfect, but often good enough to be a useful starting point, and sometimes exactly right.
LLMs can process information from a huge range of sources and surface connections a human researcher might miss. Think of it as a tool that reads everything so you don't have to.
This is where LLMs get genuinely useful for specific industries. A general-purpose model can be fine-tuned on healthcare data, legal documents, or financial records, and start performing like a specialist. That's why you see LLM deployments in fields as different as fleet management and entertainment.
LLMs aren't one thing. They're several layers working in sequence, each handling a different part of the problem. Here's what each one does.
The Embedding Layer
Words mean nothing to a computer on their own. The embedding layer translates them into numbers that carry meaning. "King" and "queen" land close together. "King" and "carburetor" don't. That proximity is how the model starts to understand language rather than just store it.
The Feedforward Layers
Reading words is one thing. Understanding what someone wants is another. Feedforward layers handle the second part. They take the embedded input and work out the intent sitting behind it, not just the literal meaning of the words.
The Recurrent Layer
Sentences aren't a list of individual words. They're a sequence where order changes everything. The recurrent layer reads that sequence and maps the relationships between words across the whole input. That's how a model knows a pronoun in sentence three refers to a noun in sentence one.
The Attention Mechanism
Not every word in a prompt deserves equal weight. The attention mechanism figures out which parts actually matter for the task at hand and focuses there. That's what separates a model that gives you a sharp, relevant answer from one that gives you a technically correct but useless one.
All four layers run together. That's what makes the output feel like it understood you.
Three types of LLMs exist. They're not interchangeable, and picking the wrong one for your use case will cost you
These are the foundation. They do one thing: predict what word comes next, based on patterns from training data. No instructions, no conversation. Just next-word prediction at scale. Most other models are built on top of these.
You tell it what to do, it does it. Sentiment analysis, code from a description, content from a brief. The gap between this and a raw model is intent. A raw model completes text. This one tries to complete your task.
Built for conversation. Not just completing a sentence but responding to a person across multiple exchanges. Context carries over. That's the difference between a chatbot that forgets what you said two messages ago and one that doesn't.
Pick the wrong category for your use case and the output will read fine but miss the point entirely. That's a more common problem than people admit.
Where businesses are putting them to work:
Customer service: Repetitive queries get handled without a human in the loop. Staff deal with the things that actually need judgment.
Personalised learning: A student struggling with fractions gets different content than one who isn't. The model adjusts. A fixed curriculum doesn't.
Creative work: Music, poetry, concept generation. Not a replacement for creative thinking. A tool that gives it more raw material to work with.
A year ago, this list looked different. Some models on it don't exist anymore in their original form. Others have been quietly replaced by newer versions that outperform them on almost every benchmark. This is what's worth knowing right now. If you want a broader view before narrowing down, our AI platform comparison covers how these models sit within the wider AI tooling landscape.
GPT-5 is OpenAI's current flagship and the model most teams reach for when they don't want to think too hard about model selection. It performs strongly across coding, reasoning, and long-context tasks. Not always the best at any single thing, but rarely bad at anything either. That reliability is what makes it the default for teams running varied workloads.
Anthropic built Claude with a different priority than most labs. The focus was on judgment, not just output. Claude Opus and Sonnet handle long documents and multi-step reasoning more effectively than most models. Where it stands out is work that requires careful reading of complex material rather than fast generation of text. Legal documents, research synthesis, nuanced analysis. That's where Claude earns its place.
Google's Gemini has been engineered to bridge productivity, search, and generative AI, weaving itself into many of Google's flagship products from search to Workspace and beyond. Its latest iterations are built with very long context windows, reportedly up to one million tokens, making them particularly strong at handling big blocks of text including entire reports, large codebases, or long academic papers all at once. If your team already runs on Google's ecosystem, this is the obvious starting point.
xAI's Grok 4 has moved well past its early reputation as the X-integrated chatbot. It now sits among the leading frontier models of 2026, with real-time web access built in as a core capability. Most models work from training data that's months old. Grok doesn't have that problem.
DeepSeek came to the spotlight during the "DeepSeek moment" in early 2025, when R1 demonstrated ChatGPT-level reasoning at significantly lower training costs. The latest release, DeepSeek V4, is designed for long-context reasoning, coding, and agentic workflows. The pricing is what gets developer teams interested. The output quality is what keeps them.
Meta's Llama 4 is the open-source option for teams where data privacy or customisation is non-negotiable. It delivers high control through open weights, enabling affordable self-hosting and flexible deployment across use cases. You need engineering capacity to run it properly. If you have that, it gives you control that no API-based model can match.
Qwen 3 doesn't show up in as many headlines as GPT or Claude. It balances reasoning ability, cost efficiency, and context support, with particular strength in multilingual summarisation, RAG pipelines, and long-context document handling. Teams working across languages or processing large volumes of documents are finding it does the job better than models with bigger marketing budgets.
There is no single best LLM. Anyone telling you otherwise is selling something. The right model is the one that fits what you are building, what your team can actually run, and what your budget can sustain without surprises.
When clients come to us trying to pick a model, the conversation almost never starts with the model. It starts with four questions. Get these right and the model selection becomes obvious.
Text, code, images, documents. Models are trained differently and perform differently across these. A model that handles marketing copy well may fall apart on structured technical data. Start with your data type, not the leaderboard rankings. We've seen teams pick a model based on a benchmark score and then rebuild three months later because it couldn't handle their actual inputs.
For marketing copy, ad generation, and social content, GPT-5 and Claude handle these well. For coding, data analysis, and technical documentation, DeepSeek and Qwen 3 are worth testing before you default to the obvious choice. For enterprise applications where safety and reliability matter above all else, Claude was built with that in mind. The task should drive the model selection. Not the other way around.
Open-source models give you control. They also give you the full maintenance burden. If your team doesn't have developers who can manage deployment, fine-tuning, and ongoing upkeep, an open-source model will cost more than a paid API subscription ever would. Be honest about your team's capacity before you go down that road. We've had clients insist on open-source for cost reasons and end up spending more on engineering time than they would have on API fees.
Images, audio, documents, video. If your use case involves any of these, you need a multimodal model. GPT-5, Gemini 3 Pro, and Claude all handle mixed inputs. If your work is text-only, multimodal capability is something you are paying for without using.
Most teams spend too long choosing the model and not enough time on how they are using it. Prompting, data quality, and workflow integration will affect your results more than the model name in your config file.
Picking a model is step one. Here's what actually comes after that.
Every major model, GPT-5, Claude, Gemini, DeepSeek, has a public API you can test before committing to anything. Sign up, get a key, and run your actual use case through it. Not a demo prompt. Your real inputs. That's the only test that tells you whether the model fits your work.
Most teams evaluate LLMs on clean, simple prompts and get good results. Then they go to production and hit the edge cases. Test on your messiest data, your most ambiguous queries, your longest documents. If the model handles those, it'll handle everything else.
The same model can produce very different outputs depending on how you instruct it. Before you conclude a model isn't working, check whether the problem is the model or the prompt. Most of the time it's the prompt.
Testing indefinitely is its own kind of procrastination. Pick a model, run it on a real workload for two weeks, and measure the output against a clear standard. That will tell you more than any benchmark comparison.
If you get through those steps and realise the scope is bigger than your team can handle alone, that's where we come in. As a generative AI development company, we help businesses move from model selection to working production systems. Without the six-month rebuilds.
LLMs are not all the same, and choosing one without a clear sense of your use case, your team's capacity, and your budget is how you end up rebuilding six months later.
The models covered here are the ones worth knowing in 2026. Some are better for reasoning, some for speed, some for teams that need full control over their infrastructure. None of them is the right answer for every situation. If you need help making that call, our AI development services cover everything from model selection to full deployment.
If you know what you're building, the right model becomes obvious. If you're still figuring that out, that's the problem worth solving first.
We work on that second part. As a generative AI development company, we help businesses figure out where AI actually fits in their workflows and build it properly from there. If that's a conversation worth having, we're easy to reach.
LLMs are referred to as large language models, providing AI programs with a capability to generate and understand languages. They are trained on an enormous amount of data that helps them detect patterns in the data to draw conclusions. Today they power chatbots, language translation, content creation, and code generation tools. The popular examples of LLMs are GPT (by OpenAI), BERT (by Google), and Claude (by Anthropic).
A chatbot is the interface. An LLM is what runs underneath it. ChatGPT is a chatbot built on GPT-5. The LLM handles the language understanding and generation. The chatbot handles the conversation wrapper around it. You can build a chatbot without an LLM, but the ones worth using today almost all have one inside.
It depends on what the business actually does. For document-heavy work like legal, compliance, or research, Claude handles long context and careful reasoning better than most. For coding and technical tasks, DeepSeek and GPT-5 are worth testing. For businesses already inside Google's ecosystem, Gemini integrates without friction. There is no single answer, but there is usually a clear frontrunner once you know the use case.
It depends on how you access it. API-based models like GPT-5 and Claude charge per token, which adds up at high volume but is negligible for low-volume testing. Open-source models like Llama 4 are free to use but require infrastructure to run, which has its own cost. For most businesses starting out, a paid API is cheaper in practice than self-hosting an open-source model.
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