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A large language model (LLM) is a type of artificial intelligence (AI) that’s designed to understand and generate human language. It uses neural networks—computing systems inspired by the human brain—to process large amounts of text and detect and learn language patterns.
Large language models are trained on massive datasets and work by predicting the next word in a sequence. This allows them to output coherent responses.
Tools built on LLMs can perform a variety of tasks without getting task-specific training. For example, they can translate or summarize text, answer questions, or provide coding help.
Note
For a primer on how LLMs work, check out our in-depth guide to AI models.
We surveyed 200 consumers to find out how they’re using LLMs. Here’s what we found out: Just under 60% of people use AI tools powered by LLMs on a daily basis.
Among polled people who use LLM tools, the most popular tools include ChatGPT (78%), Gemini (64%), and Microsoft Copilot (47%).
Research and summarization was the most common use case among respondents, with 56% of consumers saying they use LLMs or LLM tools for these tasks.
Other popular use cases include:
When it comes to choosing an LLM or tool, the qualities people value the most include accuracy, speed/latency, and the ability to handle long prompts.
Almost half of our respondents (48%) say they pay for LLMs or LLM-powered tools, either personally or through their employers. In most cases, this means they’re paying for tools like ChatGPT or Copilot, which are built on top of LLMs.
Here’s a quick overview of the most popular large language models:
Model
Developer
Release Date
Max Context Window
Best For
GPT-5
OpenAI
Aug 2025
400K
General performance
Claude Sonnet 4
Anthropic
May 2025
1M
Long-context tasks
Gemini 2.5
Google DeepMind
Mar 2025
1M
Large-scale, multimodal analysis
Mistral Large 2.1
Mistral AI
Feb 2024
128K
Open-weight commercial use
Grok 4
xAI
Jul 2025
256K
Real-time web context
Command R+
Cohere
Apr 2024
128K
Fact-based retrieval tasks
Llama 4
Meta AI
Apr 2025
10M
Open-source customization
Qwen3
Alibaba Cloud
Apr 2025
128K
Multilingual enterprise tasks
Note that you’ll typically only get the maximum context windows if you use the LLM’s API. Context windows in apps/chatbots are generally smaller.
Let’s look at each one in more detail in our list of large language models below.
Developer: OpenAI
Released: August 2025
Context window: 400,000 tokens
Best for: General performance
GPT-5 is the model behind ChatGPT, which is considered by many to be the gold standard for general-purpose AI thanks to its ability to handle a variety of input types (including text, images, and audio) within the same conversation.
This lines up with our survey findings: 78% of respondents say they’ve used ChatGPT in the past six months.
It performs consistently well across a wide range of tasks, from creative writing to technical problem-solving.
GPT-5 is also embedded into Microsoft Copilot and various other third-party tools. These integrations ensure GPT-5 is one of the most widely used LLMs.
Further reading: GPT-5 Rolls Out: What the New Model Means for Marketers
Developer: Anthropic
Released: May 2025
Context window: 1 million tokens
Best for: Long-context tasks
Claude Sonnet 4 is Anthropic’s flagship model, known for its ability to handle long and complex inputs. Its context window of 1 million tokens allows it to analyze large reports, codebases, or entire books in one go.
(Claude Opus 4 is a more powerful model for some tasks, but it has a smaller context window of 200K tokens.)
Claude Sonnet 4 is trained using Anthropic’s “constitutional AI” framework, which puts an emphasis on honesty and safety. This makes Claude particularly useful for sensitive industries like healthcare or legal.
Developer: Google DeepMind
Released: March 2025
Context window: 1 million tokens
Best for: Large-scale document analysis
Gemini 2.5 is Google DeepMind’s LLM, which is designed to process different types of input (text, images, code, audio, and video) in the same prompt. This makes it a highly versatile LLM suitable for complex, cross-format tasks.
Gemini 2.5 can handle large workflows, such as analyzing or searching through entire databases and document archives in a single session.
And Gemini 2.5 available directly in Google Workspace. So you can use it in tools like Docs, Sheets, and Gmail.
Developer: Mistral AI
Released: November 2024
Context window: 128,000 tokens
Best for: Open-weight commercial use
Mistral Large 2.1 is a commercial open-weight model, meaning it’s available for businesses to run using their own infrastructure. This makes it a great choice for organizations that require more control over their data.
Developer: xAI
Released: July 2025
Context window: 128,000 tokens (in-app), 256,000 tokens through the API
Best for: Real-time web context
Grok 4 is an LLM that’s marketed as an AI assistant and is integrated natively into the X social platform (formerly Twitter).
This gives it access to live social data, including trending posts. And it makes Grok especially useful for users looking to stay on top of news, monitor and analyze online sentiment, or identify emerging trends.
Developer: Cohere
Released: April 2024
Context window: 128,000 tokens
Best for: Retrieval-augmented generation
Command R+ is a large language model that’s designed to pull information from external sources (like APIs, databases, or knowledge bases) while answering a prompt.
Since Command R+ doesn’t rely solely on its training data and can query other sources, it’s less likely to provide incorrect or made-up answers (known as hallucinations).
Command R+ also supports more than 10 major languages (including English, Chinese, French, and German). This makes it a strong choice for global businesses that manage multilingual data.
Developer: Meta AI
Released: April 2025
Context window: 10 million tokens
Best for: Tasks requiring pre-trained and instruction-tuned weights
Llama 4 is an open-source model from Meta that anyone can download and use without having to pay licensing fees.
Llama 4 offers pre-trained and instruction-tuned weights (fine-tuned to follow instructions more reliably) for public use. This gives users the flexibility to either build on top of the base model or opt for a version that’s already optimized for everyday use cases.
Llama 4 supports both text and visual tasks across 8+ languages.
Llama 4 is a good choice for enterprises and developers that need a customizable and scalable model that they have full control over (e.g., for AI agent development or research-heavy use cases).
Developer: Alibaba Cloud
Released: April 2025
Context window: 128,000
Best for: Multi-language tasks
Qwen3 is a large language model from Alibaba that supports over 25 languages and is well-suited for companies that operate across multiple regions.
Qwen3 can handle long conversations, support tickets, and lengthy business documents without loss of context.
Use these criteria to determine the right LLM for your needs:
Some models are better suited for certain use cases than others:
Opt for a model with strengths that best match your intended use case.
The cost of using an LLM depends on token pricing, hosting method (e.g., open-weight, cloud API, or self-hosted), and licensing terms.
Costs can vary widely between different LLMs.
You can self-host open-weight models such as Llama 4 and Mistral Large 2.1. This often makes them more cost-effective. But it also means they require more setup and ongoing maintenance.
On the other hand, models like GPT-5 and Claude Sonnet 4 are often easier to use. But they can come with higher costs if you run a high volume of queries.
Here’s a quick overview of (API) token costs across different models (including two options for Claude and Llama) at the time of writing this article:
Model
Input Token Cost (per 1M tokens)
Output Token Cost (per 1M tokens)
GPT-5
$1.25/1M tokens
$10.00/1M tokens
Claude Opus 4
$15/1M tokens
$75 / 1M tokens
Claude Sonnet 4
$3/1M tokens
$15/1M tokens
Gemini 2.5 Pro
$1.25/1M tokens (≤ 200K) → $2.50/1M tokens (>200K)
$10/1M tokens (≤ 200K) → $15/1M tokens (>200K)
Mistral Large 2.1
$2.00/1M tokens
$6.00/1M tokens
Grok 4
$3.00/1M tokens
$15.00/1M tokens
Command R+
$3.00/1M tokens
$15.00/1M tokens
Llama 4 (Scout)
$0.15/1M tokens
$0.50/1M tokens
Llama 4 (Maverick)
$0.22/1M tokens
$0.85/1M tokens
Qwen 3
$0.40/1M tokens
$0.80/1M tokens
Note that token costs frequently change as developers update the models.
An LLM’s context window determines how much information it can process and remember from a single prompt.
If you’re looking to analyze large datasets or lengthy documents, you’ll want to choose a model with a large context window (like Gemini 2.5).
In case you plan on using the LLM’s capabilities within an app you’re developing and need real-time results, make sure you also consider the model’s inference latency.
Inference latency essentially refers to how quickly a model generates an answer after you submit a prompt.
If sheer performance is a priority, look at model performance based on popular benchmark scores like:
You can see these scores across models in LiveBench’s LLM leaderboard. The scores can give you a general sense of a model’s capabilities.
The key to choosing the right LLM is in considering your actual needs. Whether you’re building an internal tool, trying to incorporate AI into your existing workflow, or developing AI-powered features for your software.
Curious how your website content might appear in these LLMs? Check out our guide to the best LLM monitoring tools.
Zach Paruch
Zach Paruch is a data-driven SEO strategist with 10+ years of experience driving organic growth through smart, scalable search strategies. His expertise includes on-page and technical SEO, AI search optimization, and content strategy—with a special focus on ideating and implementing AI-driven processes. By leveraging in-depth search intent analysis, refined information architecture, and user-centered design, Zach consistently delivers high-impact content that drives business outcomes.
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