Top 8 Large Language Models (LLMs): A Comparison

What Is a Large Language Model?

A ample connection exemplary (LLM) is simply a type of artificial intelligence (AI) that’s designed to understand and make quality language. It uses neural networks—computing systems inspired by the quality brain—to process ample amounts of matter and observe and study connection patterns.

Large connection models are trained connected monolithic datasets and activity by predicting the adjacent connection successful a sequence. This allows them to output coherent responses.

Tools built connected LLMs tin execute a assortment of tasks without getting task-specific training. For example, they tin construe aliases summarize text, reply questions, aliases supply coding help.

How Do People Use Large Language Models?

We surveyed 200 consumers to find retired really they’re utilizing LLMs. Here’s what we recovered out: Just nether 60% of group usage AI devices powered by LLMs connected a regular basis. 

Among polled group who usage LLM tools, the astir celebrated devices see ChatGPT (78%), Gemini (64%), and Microsoft Copilot (47%).

Research and summarization was the astir communal usage lawsuit among respondents, pinch 56% of consumers saying they usage LLMs aliases LLM devices for these tasks. 

Other celebrated usage cases include:

• Creative penning and ideation (45%)
• Entertainment and casual questions (42%)
• Productivity-related tasks specified arsenic drafting emails and notes (40%)

When it comes to choosing an LLM aliases tool, the qualities group worth the astir see accuracy, speed/latency, and the expertise to grip agelong prompts.

Almost half of our respondents (48%) opportunity they salary for LLMs aliases LLM-powered tools, either personally aliases done their employers. In astir cases, this intends they’re paying for devices for illustration ChatGPT aliases Copilot, which are built connected apical of LLMs.

Top 8 Large Language Models

Here’s a speedy overview of the astir celebrated ample connection 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 commercialized 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 endeavor tasks

Note that you’ll typically only get the maximum discourse windows if you usage the LLM’s API. Context windows successful apps/chatbots are mostly smaller.

Let’s look astatine each 1 successful much item successful our database of ample connection models below.

1. GPT-5

Developer: OpenAI
Released: August 2025
Context window: 400,000 tokens
Best for: General performance

GPT-5 is the exemplary down ChatGPT, which is considered by galore to beryllium the golden modular for general-purpose AI acknowledgment to its expertise to grip a assortment of input types (including text, images, and audio) wrong the aforesaid conversation.

This lines up pinch our study findings: 78% of respondents opportunity they’ve utilized ChatGPT successful the past six months. 

It performs consistently good crossed a wide scope of tasks, from imaginative penning to method problem-solving.

GPT-5 is besides embedded into Microsoft Copilot and various different third-party tools. These integrations guarantee GPT-5 is 1 of the astir wide utilized LLMs.

Strengths

• Highly versatile crossed a assortment of usage cases
• Strong reasoning abilities and precocious accuracy
• Suitable for analyzable workflows acknowledgment to multimodal input (text, audio, images) and output capabilities
• Large integration ecosystem (ChatGPT, Copilot, third-party apps)

Drawbacks

• Less customizable compared to open-source models
• More costly than open-weight models

Further reading: GPT-5 Rolls Out: What the New Model Means for Marketers

2. Claude Sonnet 4

Developer: Anthropic
Released: May 2025
Context window: 1 cardinal tokens
Best for: Long-context tasks

Claude Sonnet 4 is Anthropic’s flagship model, known for its expertise to grip agelong and analyzable inputs. Its discourse model of 1 cardinal tokens allows it to analyse ample reports, codebases, aliases full books successful 1 go.

(Claude Opus 4 is simply a much powerful exemplary for immoderate tasks, but it has a smaller discourse model of 200K tokens.)

Claude Sonnet 4 is trained utilizing Anthropic’s “constitutional AI” framework, which puts an accent connected honesty and safety. This makes Claude peculiarly useful for delicate industries for illustration healthcare aliases legal.

Strengths

• Huge discourse model (1M tokens)
• Constitutional AI model makes it safer by design
• Trustworthy exemplary for regulated industries

Drawbacks

• May sometimes garbage to grip borderline aliases grey-area queries that different models effort to lick (e.g., asking Claude to constitute a highly captious portion connected a competitor)
• Slower consequence times compared to lighter-weight models
• Limited customization owed to being a proprietary (closed source) model

3. Gemini 2.5

Developer: Google DeepMind
Released: March 2025
Context window: 1 cardinal tokens
Best for: Large-scale archive analysis

Gemini 2.5 is Google DeepMind’s LLM, which is designed to process different types of input (text, images, code, audio, and video) successful the aforesaid prompt. This makes it a highly versatile LLM suitable for complex, cross-format tasks.

Gemini 2.5 tin grip ample workflows, specified arsenic analyzing aliases searching done full databases and archive archives successful a azygous session.

And Gemini 2.5 disposable straight successful Google Workspace. So you tin usage it successful devices for illustration Docs, Sheets, and Gmail.

Strengths

• Excels astatine handling multimodal inputs consisting of text, images, code, video, and audio
• 1M discourse model makes it suitable for large-scale analysis
• Google Workspace integration makes it easy to usage successful mundane workflows

Drawbacks

• Limited customization owed to being a closed-source model
• Less elastic for users whose workflows trust heavy connected non-Google tools

4. Mistral Large 2.1

Developer: Mistral AI
Released: November 2024
Context window: 128,000 tokens
Best for: Open-weight commercialized use

Mistral Large 2.1 is simply a commercialized open-weight model, meaning it’s disposable for businesses to tally utilizing their ain infrastructure. This makes it a awesome prime for organizations that require much power complete their data.

Strengths

• Provides much power complete customization and information information owed to its open-weight and transparent nature
• Offers elastic deployment done self-hosting aliases unreality APIs
• Cost-efficient for high-volume usage cases and enterprise-scale applications

Drawbacks

• Smaller discourse model compared to models for illustration Claude and Gemini
• Requires much method setup and infrastructure

5. Grok 4

Developer: xAI
Released: July 2025
Context window: 128,000 tokens (in-app), 256,000 tokens done the API
Best for: Real-time web context

Grok 4 is an LLM that’s marketed arsenic an AI adjunct and is integrated natively into the X societal level (formerly Twitter).

This gives it entree to unrecorded societal data, including trending posts. And it makes Grok particularly useful for users looking to enactment connected apical of news, show and analyse online sentiment, aliases place emerging trends.

Strengths

• Real-time entree to societal media data
• Relatively ample discourse model (256,000 tokens done the API)
• Native integration pinch X

Drawbacks

• Limited usefulness extracurricular of the X ecosystem
• Lack of customization options owed to its proprietary nature

6. Command R+

Developer: Cohere
Released: April 2024
Context window: 128,000 tokens
Best for: Retrieval-augmented generation

Command R+ is simply a ample connection exemplary that’s designed to propulsion accusation from outer sources (like APIs, databases, aliases knowledge bases) while answering a prompt. 

Since Command R+ doesn’t trust solely connected its training information and tin query different sources, it’s little apt to supply incorrect aliases made-up answers (known arsenic hallucinations).

Command R+ besides supports much than 10 awesome languages (including English, Chinese, French, and German). This makes it a beardown prime for world businesses that negociate multilingual data.

Strengths

• Sourced-backed answers and reduced hallucinations
• Multilingual supports crossed 10+ awesome languages
• Transparency and reliability for fact-based queries

Drawbacks

• Needs integration pinch outer information sources to recognize its afloat potential
• Has a smaller ecosystem compared to models for illustration GPT-5
• Less suited for imaginative tasks

7. Llama 4

Developer: Meta AI
Released: April 2025
Context window: 10 cardinal tokens
Best for: Tasks requiring pre-trained and instruction-tuned weights

Llama 4 is an open-source exemplary from Meta that anyone tin download and usage without having to salary licensing fees.

Llama 4 offers pre-trained and instruction-tuned weights (fine-tuned to travel instructions much reliably) for nationalist use. This gives users the elasticity to either build connected apical of the guidelines exemplary aliases opt for a type that’s already optimized for mundane usage cases.

Llama 4 supports some matter and ocular tasks crossed 8+ languages.

Strengths

• Open-source quality makes it free to use, integrate, and customize your ain AI agents
• 10M-token discourse model allows for very ample inputs
• Strong organization and accelerated ecosystem growth

Drawbacks

• Technical expertise needed to fine-tune the exemplary effectively
• Less polished than consumer-facing models for illustration GPT-5
• Limited customer support

Llama 4 is simply a bully prime for enterprises and developers that request a customizable and scalable exemplary that they person afloat power complete (e.g., for AI supplier improvement aliases research-heavy usage cases).

8. Qwen3

Developer: Alibaba Cloud
Released: April 2025
Context window: 128,000
Best for: Multi-language tasks

Qwen3 is simply a ample connection exemplary from Alibaba that supports complete 25 languages and is well-suited for companies that run crossed aggregate regions.

Qwen3 tin grip agelong conversations, support tickets, and lengthy business documents without nonaccomplishment of context.

Strengths

• Strong multilingual support
• Enterprise-friendly creation makes it suitable for usage crossed ample organizations
• Offers a bully equilibrium betwixt capacity and assets usage acknowledgment to businesslike Mixture-of-Experts (MoE) architecture that routes tasks to the due neural networks

Drawbacks

• Relatively mini discourse model compared to different starring models
• Less suitable for highly imaginative tasks

What to Look for When Comparing LLMs

Use these criteria to find the correct LLM for your needs:

Use Fit: Creative, Technical, aliases Conversational

Some models are amended suited for definite usage cases than others:

• GPT-5, Claude Sonnet 4, and Gemini 2.5 are awesome for imaginative tasks for illustration penning aliases ideation
• Qwen3 and Grok 4 excel astatine coding and math-related tasks
• Mistral Large 2.1 and Command R+ are champion suited for analyzing ample documents

Opt for a exemplary pinch strengths that champion lucifer your intended usage case.

Cost, Licensing, and Deployment Options

The costs of utilizing an LLM depends connected token pricing, hosting method (e.g., open-weight, unreality API, aliases self-hosted), and licensing terms.

Costs tin alteration wide betwixt different LLMs.

You tin self-host open-weight models specified arsenic Llama 4 and Mistral Large 2.1. This often makes them much cost-effective. But it besides intends they require much setup and ongoing maintenance.

On the different hand, models for illustration GPT-5 and Claude Sonnet 4 are often easier to use. But they tin travel pinch higher costs if you tally a precocious measurement of queries.

Here’s a speedy overview of (API) token costs crossed different models (including 2 options for Claude and Llama) astatine the clip of penning 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 often alteration arsenic developers update the models.

Context Window and Speed

An LLM’s discourse model determines really overmuch accusation it tin process and retrieve from a azygous prompt.

If you’re looking to analyse ample datasets aliases lengthy documents, you’ll want to take a exemplary pinch a ample discourse model (like Gemini 2.5).

In lawsuit you scheme connected utilizing the LLM’s capabilities wrong an app you’re processing and request real-time results, make judge you besides see the model’s conclusion latency.

Inference latency fundamentally refers to really quickly a exemplary generates an reply aft you taxable a prompt. 

Model Capabilities and Benchmark Scores

If sheer capacity is simply a priority, look astatine exemplary capacity based connected celebrated benchmark scores like:

• MMLU: Tests a model’s wide reasoning crossed world subjects
• GSM8K: Measures a model’s mathematics problem-solving abilities 
• HumanEval: Evaluates a model’s coding skills
• HELM: Based connected a holistic information of a exemplary crossed aggregate dimensions (including bias, fairness, and robustness)

You tin spot these scores crossed models successful LiveBench’s LLM leaderboard. The scores tin springiness you a wide consciousness of a model’s capabilities.

Get the Most Out of Large Language Models

The cardinal to choosing the correct LLM is successful considering your existent needs. Whether you’re building an soul tool, trying to incorporated AI into your existing workflow, aliases processing AI-powered features for your software. 

Curious really your website contented mightiness look successful these LLMs? Check retired our guideline to the champion LLM monitoring tools.