Large Language Models
explained without the hype
A Large Language Model, or LLM, is a neural network trained on enormous quantities of text to predict the next token in a sequence. From that simple objective emerges the ability to write essays, debug code, summarise contracts and reason through novel problems. This page lays out what an LLM actually is, how the field arrived here, and where the real boundaries sit in 2026.
An LLM is a next-token predictor at massive scale
An LLM is a transformer neural network with billions to trillions of parameters, trained to predict the next token (roughly, the next word piece) given everything that came before. That is the entire training objective.
The surprising result, demonstrated repeatedly since 2020, is that scaling this single objective up far enough produces models that can translate languages, write code, solve maths problems and follow complex instructions, without ever being explicitly trained on those tasks.
From RNNs to transformers to frontier models
Before 2017, language models used recurrent networks (RNNs, LSTMs) that processed text one token at a time. The 2017 Transformer paper introduced self-attention, allowing models to consider every prior token in parallel. That unlock made today's scale possible.
GPT-2 in 2019 showed promising generative ability. GPT-3 in 2020 showed emergent capabilities at 175B parameters. ChatGPT in 2022 brought LLMs to the public. Since then, the frontier has been a multi-lab race between OpenAI, Anthropic, Google DeepMind, Meta, Mistral, DeepSeek and others.
Base, instruct, reasoning and multimodal variants
Base models are trained purely on next-token prediction over web text. They complete patterns but do not follow instructions naturally.
Instruct models are base models fine-tuned on instruction-response pairs, often with reinforcement learning from human feedback (RLHF). This is what most consumer chatbots are.
Reasoning models add a chain-of-thought training stage that lets them think before answering, trading speed for accuracy on hard problems. Multimodal models extend the architecture to also read images, audio or video as input.
How Namulai gives you eight different LLMs at once
Namulai is a chat interface that routes prompts to eight frontier LLMs: ChatGPT, Claude, Gemini, Mistral, DeepSeek, Grok, LLaMA, Perplexity. Each was trained by a different lab on a different mix of data with different fine-tuning, so each has different strengths.
The practical consequence: instead of guessing which single model is best for everything, you pick the right one per task. One subscription at 19.80 EUR per month, eight specialists.
learn.what-is-llm.faqTitle
learn.what-is-llm.faq.q1
learn.what-is-llm.faq.a1
learn.what-is-llm.faq.q2
learn.what-is-llm.faq.a2
learn.what-is-llm.faq.q3
learn.what-is-llm.faq.a3
learn.what-is-llm.faq.q4
learn.what-is-llm.faq.a4
Try eight LLMs on your next real question
Try Namulai free30-day free trial · €19.80/month after · cancel anytime