LLM Models Explained: How They Work & Which to Choose

What is an LLM Model, Actually?

Most of us have used this once in our lifetime without knowing. When we use an AI to write an email for us or debug a code, we are not using an app with pre-programmed answers. What we are actually doing is utilizing a statistical system that has read more in one second than any human has read in one hundred lifetimes and has learned at a very fundamental level how language works.

LLM means “Large Language Models”. Here, “large” means “a lot of data” and “a lot of parameters”. Parameters are numbers that are used to improve its ability to predict text. GPT-3 has 175 billion of them. The new frontier models have many times that number.

“Model” means “a mathematical function that has been trained”. Not an app and not a chatbot. The model is the thing underneath – the weight and architecture. Everything is built on top of that. This is obvious until it is not.

The difference between a model, a chatbot, and an API

GPT-5.2 is a model. ChatGPT is a product that uses this model. The OpenAI API is a way for developers to use this model directly, bypassing the ChatGPT layer.

It makes a difference. If you are using ChatGPT, you are also using OpenAI’s system prompt, safety checks, and UI. If a developer uses the raw API, he’s using the model. The very same model. Very different results depending on what’s wrapped around the model.

How LLMs Work?

“Artificial Intelligence doesn’t think like humans”.

The LLM works on the input given by you, and it divides the input into parts, which are referred to as tokens. Tokens are further converted into numbers, and these numbers undergo several layers of computation to generate the output. In this process, one token is considered at a time in an order on the basis of probability. The power of the LLM lies in the layers of the model, which are based on the transformer architecture. A Google paper – “Attention is all you need” by Vaswani et al. in 2017, presented this architecture.

The transformer works by performing this prediction task on a huge amount of text. Predict the next word and see if the word is correct or not, and adjust the parameters slightly. Do this process billions of times. Once this process is done, the model not only gets good at the prediction task but also understands the task.

What is a token?

Token size is three or four characters, or three-fourths of an average English word. For instance, the sentence “unbelievable” has three tokens, and “the cat sat” has four tokens, or so I think.

Tokenization is not standardized across platforms. The same prompt may have varying token counts on different platforms. A prompt may have 140 tokens on the GPT-4 platform, but it may have up to 180 tokens on the Claude or Gemini platform. It is good to know this when budgeting for the cost of the API.

What is Self-Attention and Why does this matter?

Self-attention is how the model is able to view all the words in the sentence and then view another word in the sentence as well. If we wish to understand it better, we can take an example. The model uses self-attention in order to understand that in the given sentence, i.e., ‘The bank was steep,’ the word bank is referring to the riverbank because of the usage of the word steep. This is done in all the layers of the transformer model, and a clear picture is created in each of these layers.

Training vs. Inference – What is the Difference?

The training phase is where the model is built, and this is a costly, time-consuming, and one-time or large update process. The cost estimate for GPT-3 is 4-12 million dollars, and this is for only GPT-3. The cost estimate for the 2026 frontier is much more substantial than this.

The inference phase is where you utilize your model, and a perfect example is sending a message to Claude or GPT-5, and this is the inference phase. This is a cheap and quick process, and from 2025 to 2026, the prices for the LLM API went down by 80% across the board, but it is important to note that the pricing model is complex and is worth understanding before you start building anything at scale.

The Major LLM Models Compared

Honestly, this is where most articles fail you. They list model names, copy a tagline from the official site, and call it a comparison. So here’s what the landscape actually looks like in March 2026.

There’s no longer a universal “best AI” — there’s the best AI for your use case.

Model	Best for	Context window	Type	API input cost /1M tokens
GPT-5.2OpenAI	General reasoning, writing, ecosystem integrations	Up to 1M tokens	Closed	~$1.75
Claude Opus 4.6Anthropic	Long documents, coding, nuanced instructions, safety	200K (1M beta)	Closed	~$5.00
Gemini 2.5 ProGoogle DeepMind	Long context, Google Workspace, multimodal	1M tokens	Closed	~$1.25
DeepSeek V3.2DeepSeek AI	Cost-sensitive workloads, open-source flexibility	128K tokens	Open (MIT)	~$0.28 (free self-hosted)
Llama 4 ScoutMeta	Ultra-long context, on-premise privacy	10M tokens*	Open weight	Free (self-hosted)
Grok 4.1xAI	High-volume, cost efficiency, large context	2M tokens	Closed	~$0.20
Mistral Small 3Mistral AI	Local inference, EU data residency	32K tokens	Open	Free (self-hosted)

One thing that table doesn’t show: reasoning models. OpenAI’s o3 series and Anthropic’s “extended thinking” modes on Opus 4.6 are designed for slow, deliberate multi-step problem solving — math, complex code, research tasks. They’re billed differently (reasoning tokens cost extra) and respond more slowly. For quick tasks, you don’t want them. For hard ones, they’re meaningfully better.

Open Source vs. Closed Source LLMs

This split has different implications in 2026 than it did two years ago.

The best open source LLMs in 2026 are – DeepSeek-V3.2, Llama 4 Scout, and Qwen 3. DeepSeek V3.2 has managed to do this for a training budget of around $5.6M – a sum that would have been considered impossible for a frontier-class model even eighteen months ago.

Closed models like OpenAI, Anthropic, and Google are still managed services – you call an API, and you get a result. You pay per token. Your inputs go to their servers. Open models give you actual weights – download, run, zero-per-token cost, and full data privacy.

Weirdly enough, the security calculus has now flipped for some teams. DeepSeek and Qwen are Chinese labs. This means compliance risk for US enterprises that work with regulated data. For those teams, the best solution is the Mistral Small 3 – a European open model that has Apache 2.0 licensing.

What Can You Actually Do With an LLM?

The short answer is: more than most people expect, with more caveats than most articles discuss.

Let’s define the use cases in four categories. Generation: write something. Reasoning: analyze or explain something. Coding: write or debug code. Retrieval: retrieve and synthesize information in documents. Most use cases involve at least two of these.

The ‘generate text’ use case gets all the glory. But for business, the important use cases are the ‘reasoning’ ones. Getting structured data out of unstructured documents. Classifying support requests. Building SQL queries from natural language questions. Stuff that used to require human labor is now done automatically.

But here’s the thing: the best approach in 2026 is to have different models for different tasks: one LLM for customer service, one for content generation, and one for code help. One model for all is not the way to go.

RAG 

Retrieval-Augmented Generation. Long name, simple idea.

Large Language Models have a knowledge cutoff. They don’t know what happened after their training date, and they definitely don’t know anything specific to your company, your documents, or your database. RAG addresses this by retrieving documents relevant to your query and passing those documents to the model alongside your query.

Ask it “What does our refund policy say?” and it retrieves your refund policy document, reads it, and answers it. This is how most serious enterprise LLMs are used. RAG is not optional infrastructure. It is the infrastructure.

The main idea is to use a model of retrieval. The LLM uses the augmented prompt to generate an answer that is factually correct and cites sources appropriately. The main tools: LangChain and LlamaIndex. Hugging Face is where you can find open-source models you will likely use as your foundation.

LLM Limitations You Should Know Before You Build

Most pieces, I think, discuss the limitations as part of the disclaimer section, which is something to get through before the “exciting potential” part. I think this is backwards. The limitations are where the real engineering decisions are.

The most important, I think, is the hallucination. The model is producing something that is not only correct but also fluent and confident. A lawyer, in 2023, submitted a legal brief with citations provided by ChatGPT. Several of the cases cited do not exist. The legal brief was excellent. The cases were fictional. While this has not gone away, it has simply been better managed by people who are aware of the problem.

This is not a bug. It’s not something to fix. It’s structural. The model is choosing the most statistically likely next token. The most likely token is not necessarily the correct token. You can mitigate the hallucinations with RAG, which grounds the model on real documents, or by asking the model to say “I don’t know” when it doesn’t know. You can’t eliminate them.

Also: context window limits. And in that space, 2026 has made a significant impact.

The models that most developers actually use day to day – Gemini 2.5 Pro, GPT-4.1, Claude Opus 4.6 – sit in the 200K-1M space. Llama 4 Scout claims to support 10M tokens but requires 8 x H100 GPUs for inference and has its limits at 1.4M tokens. The space between “supports” and “performs well at” is often the most significant figure to check.

And the training cutoff. All models have a cutoff date after which they know nothing. Some have web search tools. Most don’t.

The data privacy question

If you send text to a closed LLM API, that text goes out of your machine and onto someone else’s server. What happens to that text depends on who is running the server. ChatGPT Enterprise complies with a number of compliance standards, including SOC2, FedRAMP, and HIPAA. They can also comply with contractual terms around privacy, including no data retention by OpenAI.

Similarly, the Claude enterprise API complies with a number of compliance standards. However, consumer products like ChatGPT.com and Claude.ai have different terms than the enterprise API. The clean answer for “confidential” workloads: run a local open model. In 2026, data sovereignty is no longer a niche topic. The conversation has moved from “Can I run AI locally?” to “Which local model should I trust with my codebase?” DeepSeek V3.2 and Llama 4 are both local models that can be run locally using Ollama or LM Studio.

How to Choose the Right LLM for Your Use Case

Three questions. Answer them in order.

Question 1: Do you need a very large context window? A 1M token context window is enough to hold 3,000 pages of text. This is plenty for most legal contracts, annual reports, and technical documentation. The 2M token models, such as Gemini 3 Pro and Grok 4.1, can even process entire codebases in a single pass. If you’re processing long documents, the context window is your first filter. If you’re making a chatbot that will process short conversations, it doesn’t really matter.

Question 2: Do you need data privacy guarantees? If yes: open-source, self-hosted. DeepSeek V3.2 is available under the MIT license. Alternatively, consider Llama 4 under Meta’s open weight license. If you’re in the EU and need to comply with EU regulations: Mistral.

Question 3: How much is your cost tolerance? For most apps, you’ll find that models in the $0.10 – $3.00 range per million token cost will do the job. If you need to go cheap, TLDL’s Grok 4.1 is $0.20/M. Gemini 2.5 Flash is an even cheaper option at $0.30/M. If you need high quality and don’t mind the cost, Claude Opus 4.6 is your best bet at $5/M. GPT-5.2 is also an option.

Getting Started With LLMs as a Developer

Three paths. Very different tradeoffs.

Path 1 – Hosted API. Fastest to start up. Just sign up for OpenAI, Anthropic, or Google’s API, and you’re done. Get a key and make a Post request. Up and running in under an hour. Pay per token.

Path 2 – Hugging Face + open weights. Download a model from the model hub and run it using the transformers library. More work to set up. But useful for fine-tuning and other pipelines.

Path 3 – Run locally. The ecosystem has matured to the point that you can have models installed in one click. Ollama is basically the Spotify for local AI models – it runs in your terminal, handles downloading and serving models for you. LM Studio provides a full interface for those who don’t wish to touch the command line. Both support DeepSeek V3.2, Llama 4, and Mistral Small 3.

But unless data privacy is a hard constraint for you, start with Path 1. The frontier models are more powerful, and the setup is trivial. You can always switch to running yourself later if you have a better idea of what you need.

Frequently Asked Questions