Large Language Models (LLMs)

The most versatile AI model type — capable of text generation, reasoning, coding, translation, and much more. Learn what LLMs are, how they work, and when they are (and are not) the right choice.

What Are Large Language Models?

Large Language Models (LLMs) are neural networks with billions of parameters, trained on massive text datasets to understand and generate human language. At their core, LLMs are next-token prediction machines: given a sequence of text, they predict the most likely next token (word or subword). Despite this seemingly simple objective, scaling this approach to hundreds of billions of parameters and trillions of training tokens produces models with remarkable emergent abilities — including reasoning, coding, mathematical problem-solving, and creative writing.

LLMs are the most general-purpose model type in AI today. A single LLM can perform hundreds of distinct tasks through prompting alone, without any task-specific training. This versatility is what makes them the default starting point for many AI applications — but it is also why they are sometimes used where a more specialized model would be a better fit.

Key Architectures

Not all LLMs are built the same way. There are two dominant architectures:

Decoder-Only Transformers

The vast majority of modern LLMs use a decoder-only architecture. These models generate text left-to-right, one token at a time. Each token can only attend to tokens that came before it (causal attention). This architecture is optimized for text generation tasks.

• Examples: GPT-4, GPT-4o, Claude 4, LLaMA 3, Mistral, Qwen 2.5, Phi-4, Command R+
• Strengths: Excellent at open-ended generation, reasoning, coding, conversation
• Training objective: Predict the next token given all previous tokens

Encoder-Decoder Transformers

Some models use a two-part architecture: an encoder processes the full input bidirectionally, and a decoder generates the output. This architecture excels at tasks where understanding the complete input is critical before generating output.

• Examples: T5, FLAN-T5, UL2, mT5
• Strengths: Translation, summarization, structured extraction
• Training objective: Reconstruct corrupted text spans (span corruption)

Major LLMs in 2025

The LLM landscape is evolving rapidly. Here are the most important models to know:

Closed-Source (API-Only) Models

Open-Weight Models

Parameters and Scale

The number of parameters in an LLM is one of the most discussed metrics, but it is not the only factor that determines capability. Training data quality, training methodology (RLHF, DPO), and architecture innovations all play critical roles.

Core Capabilities

Modern LLMs are remarkably versatile. Here are their primary capabilities:

Text Generation

The foundational capability of all LLMs. They can write articles, emails, marketing copy, fiction, documentation, and virtually any other text format. Quality varies by model — frontier models like Claude 4 and GPT-4o produce text that is often indistinguishable from expert human writing.

Code Generation and Understanding

LLMs have become powerful coding assistants. They can write code in dozens of languages, debug existing code, explain complex functions, convert between languages, and even build complete applications. Specialized coding models (like Codestral and DeepSeek-Coder) push this capability further.

# Prompt: "Write a Python function that calculates the Fibonacci
# sequence using memoization"

# LLM Output:
def fibonacci(n: int, memo: dict = {}) -> int:
    """Calculate nth Fibonacci number using memoization."""
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    memo[n] = fibonacci(n - 1, memo) + fibonacci(n - 2, memo)
    return memo[n]

# Usage
print(fibonacci(50))  # 12586269025 (instant, no redundant computation)

Reasoning and Analysis

Frontier LLMs can perform multi-step reasoning, solve mathematical problems, analyze complex scenarios, and draw logical conclusions. "Extended thinking" or "chain-of-thought" capabilities in models like Claude 4 and o1 allow them to work through problems step by step before providing an answer.

Translation

LLMs provide high-quality translation between languages, often rivaling dedicated translation systems. They excel at nuanced, context-aware translation that captures tone and intent, not just literal meaning.

Summarization

Given long documents, articles, or conversation histories, LLMs can produce concise, accurate summaries. Models with large context windows (Claude 4 with 1M tokens, Gemini 2.5 Pro with 1M tokens) can summarize entire books.

Limitations

Despite their impressive capabilities, LLMs have significant limitations that every practitioner must understand:

Use Cases by Industry

When to Use LLMs vs Other Model Types

LLMs are versatile, but they are not always the right choice. Here is a practical decision guide:

The Cost-Performance Tradeoff

Understanding pricing is essential for production LLM applications:

* Self-hosted costs are approximate and depend on GPU hardware, utilization rate, and hosting provider. Costs shown are compute-only, excluding engineering and maintenance overhead.

Key Takeaways