What is a Large Language Model (LLM)? 14+ Best LLMs

Have you ever wondered how those chatbots seem to understand you so well, or how AI can write such convincing text?

It’s all thanks to the magic of Large Language Models, or LLMs. But with over 15,000 models available as of mid-2023, and options popping up from Google’s Gemini to Meta’s Llama, how do you even begin to make sense of it all?

Let’s dive into the world of LLMs, breaking down what is a Large Language Model at its core, what makes different LLMs unique, and highlighting some of the best contenders out there.

What is a Large Language Model? What is an LLM?

An LLM, or large language model, is fundamentally a sophisticated text generator. It’s the engine behind many AI chatbots and writing tools, taking a text prompt and producing a relevant response. Unlike simple keyword-matching systems, LLMs attempt to grasp the meaning of the input, enabling them to handle diverse tasks from customer service to content creation. While they excel at text, the rise of LMMs—large multimodal models—expands their capabilities to include images, audio, and video.

What is an Open Source LLM?

Open-source LLMs are models made available for public use and modification. They contrast with proprietary models like GPT-4o, which are closed and controlled by their developers. You can download open-source LLMs such as Llama 3 or Gemma 2 and run them on your hardware, even adapting them with your data. This allows for greater flexibility and customization.

Here’s a breakdown:

Proprietary LLMs:

• Developed and controlled by private companies.
• Source code and training data are kept secret.
• Access is typically through APIs or chatbots.
• Example: GPT-4o.

Open-Source LLMs:

• Freely available for download, use, and modification.
• Users can adapt and retrain the models.
• Permissive licenses, often requiring attribution and that derivative works be open source.
• Example: DeepSeek R1.

Open LLMs:

• Available for download and use, but with some restrictions.
• May have limits on commercial use or prohibited use policies.
• Companies who release these models, still retain some control.
• Example: Llama 3, Gemma 2.

14+ Best LLMs

Gemini (Google)

Google’s Gemini represents a diverse family of AI models, designed to be adaptable and useful across a range of devices and applications. Think of it as a toolkit, with each model tailored for different needs. What sets Gemini apart is its inherent multimodality; it’s not just about text, but also understanding and processing images, audio, video, and code. This makes it a versatile tool for developers and users alike. Google is integrating Gemini into its products, like Gmail and Docs, and making it available via APIs for wider use. The focus on a large context window means it can handle complex, lengthy interactions.

Key Features:

• Multimodal capabilities (text, images, audio, video, code).
• Varied model sizes (Nano, Flash, Pro, Ultra) for different devices and tasks.
• Large context window (up to 2 million).
• Integration into Google products (Docs, Gmail, Gemini chatbot).
• API access through Google AI Studio and Vertex AI.
• Reasoning models like Gemini 2.0 Flash Thinking.

GPT-4o (OpenAI)

GPT-4o, from OpenAI, is a significant evolution in their line of generative models, building upon the foundation of the GPT series. It’s designed to be more efficient and versatile, particularly with its enhanced handling of audio and visual inputs alongside text. This means it can engage in more natural, interactive conversations and process a wide range of information. GPT-4o is a general-purpose model, meaning it’s adaptable to a wide array of applications, and its availability through an API has led to its integration into numerous third-party tools and services. While it powers many things, the most well-known user interface for it is ChatGPT.

Key Features:

• Multimodal capabilities (text, images, audio).
• Large context window (128,000).
• General-purpose model, adaptable to various applications.
• API access for developers.
• Powers ChatGPT.

o3 and o1 (OpenAI)

OpenAI’s “o” models (o1, o3) represent a dedicated line of reasoning-focused AI, designed to excel at complex problem-solving and logical tasks. These models are built to push the boundaries of AI’s ability to “think” and draw inferences, and they’ve demonstrated strong performance in benchmarks. The o1 model was the first of this reasoning-focused model line. OpenAI is working to unify its various model lines and has stated that the naming conventions are confusing and will be changed in the future. The next major release from OpenAI is expected to be GPT-5 and will contain the capabilities of all of the other models.

Key Features:

• Specialized in reasoning and problem-solving.
• Large context window (200,000).
• API access.
• Evolving model line with o1, o3-mini, and future releases.
• Future integration into a unified GPT model line.

Gemma (Google)

Google’s Gemma is a set of open-source models, essentially a sibling to their more prominent Gemini family. It’s like Google sharing the core technology behind Gemini with the wider world. Gemma models come in various sizes, making them adaptable for different needs and hardware. By offering Gemma openly, Google is contributing to the broader AI community, allowing developers and researchers to experiment and build upon their work. It’s a way for Google to foster innovation while still leveraging the strengths of its underlying research.

Key Features:

• Open-source models.
• Available in multiple parameter sizes (2 billion, 9 billion, 27 billion).
• Based on the same technology as Gemini.
• Context window of 8,200.
• Designed to be accessible to a wide range of developers.

R1 (DeepSeek)

DeepSeek’s R1 made waves by demonstrating that top-tier reasoning AI isn’t solely the domain of massive, well-funded tech giants. This model, developed by a Chinese company, showed comparable reasoning capabilities to models like OpenAI’s o1, all while using less computational power. It’s an open model, which is a significant contribution to the field. However, the long-term impact of DeepSeek’s technical innovations, as well as the potential effects of geopolitical factors, are still to be seen.

Key Features:

• Reasoning-focused model.
• Open-source availability.
• Developed with efficient hardware usage.
• Large parameter size (671 billion).
• Context window of 128,000.
• Available through API and chatbot.

V3 (DeepSeek)

DeepSeek’s V3 is their attempt to compete with the likes of GPT-4, offering a strong general-purpose language model that’s also open-source. Like their R1 model, V3 is notable for being developed with resource efficiency in mind. It’s a powerful tool for text generation and understanding, though it doesn’t include the reasoning or multimodal capabilities of some other models. The question now is whether DeepSeek can maintain momentum and gain wider adoption for its models in the competitive AI landscape.

Key Features:

• General-purpose language model.
• Open-source.
• Developed with efficient hardware usage.
• Large parameter size (671 billion).
• Context window of 128,000.
• Available through API and chatbot.

Llama (Meta)

Meta’s Llama family is a significant player in the open-source LLM world. Think of it as a set of building blocks that Meta has made available for anyone to use and adapt. With a range of models, from smaller ones suitable for basic tasks to massive ones capable of handling complex operations, Llama aims to provide flexibility. It’s not just about research; Meta also uses Llama to power AI features in its apps. The fact that it’s open-source has spurred a lot of innovation, with many other LLMs using Llama as a starting point. They have text-only and multimodal models in their lineup.

Key Features:

• Open-source models.
• Variety of parameter sizes (1B, 3B, 8B, 11B, 70B, 90B, 405B).
• Large context window (128,000).
• Both text-only and multimodal models.
• Used within Meta’s own applications.
• Source code available on GitHub.
• Free for research and commercial use.

Claude (Anthropic)

Anthropic’s Claude is positioned as a strong competitor in the LLM space, with a focus on being reliable and safe. The models are designed with a “helpful, honest, harmless” philosophy, which is particularly appealing to businesses concerned about responsible AI. The focus on enterprise-grade safety has led to partnerships with major companies, showcasing Claude’s appeal for professional applications. Claude is built to handle large amounts of data and provide helpful responses.

Key Features:

• Focus on safety and reliability.
• Designed for enterprise use.
• Large context window (200,000).
• Models: Claude 3.7 Sonnet, Claude 3.5 Haiku, Claude 3 Opus.
• API access.
• Partnerships with companies like Slack, Notion, and Zoom.

Nova (Amazon)

Amazon Nova is Amazon’s entry into the field of advanced LLMs, offered through Amazon Web Services. While Amazon’s progress in this area might have started a bit later than some others, the Nova models are now proving to be competitive. With the strength of AWS as a platform, Nova has the potential to become widely used, especially by businesses already relying on Amazon’s cloud services. The models are designed to scale, and provide a range of capabilities.

Key Features:

• Available through Amazon Web Services (AWS).
• Models: Amazon Nova Micro, Amazon Nova Lite, Amazon Nova Pro.
• Large context window (up to 300,000).
• API access.
• Designed for scalability.
• Competitive performance in benchmarks.

Command (Cohere)

Cohere’s Command models are built with businesses in mind, much like Claude. They emphasize reliability and accuracy, particularly for enterprise applications. A key feature is their optimization for Retrieval Augmented Generation (RAG), which allows companies to connect the model to their data, ensuring responses are relevant and precise. This makes Command models a valuable tool for customer support, internal knowledge bases, and other business-critical tasks. Cohere is gaining traction with large enterprise customers.

Key Features:

• Designed for enterprise use.
• Optimized for Retrieval Augmented Generation (RAG).
• API access.
• Command R7B, Command R, and Command R+ models.
• Focus on accuracy and reliability.
• Used by companies like Oracle, Accenture, Notion, and Salesforce.

Qwen (Alibaba Cloud)

Alibaba’s Qwen is a diverse family of AI models, offering a wide range of options for different needs. Think of it as a comprehensive toolkit, with models optimized for various tasks like vision, coding, and math. Notably, Qwen models boast a very large context window, allowing them to handle extensive amounts of information. The Qwen2.5 Max model is a very high-performing model and is showing that it can compete with the top models available. Alibaba is making Qwen available through multiple avenues, including open-source options, APIs, and a chatbot, which increases its accessibility.

Key Features:

• Diverse model family with specialized versions.
• Large context window (up to 1,000,000).
• Open-source availability.
• API and chatbot access.
• Models tailored for vision, coding, and math.
• High-performing Qwen2.5 Max model.
• Range of parameter sizes (0.5B to 72B).

Mistral Large 2 (Mistral)

Mistral Large 2, from Mistral AI, is a strong contender in the advanced LLM arena, representing a significant European effort in AI development. Mistral is positioning its models as direct competitors to the leading models from major tech companies. The availability of Mistral Large 2 with open weights allows for customization and further development, which is valuable for research and specific commercial applications. The company is producing high-quality models and tools.

Key Features:

• High-performance LLM.
• Open weights for research and commercial use.
• Large parameter size (123 billion).
• Context window of 128,000.
• Competes with GPT-4o and Gemini.
• Offers multimodal models, and a chatbot.

Grok (xAI)

Grok, developed by Elon Musk’s xAI, has gone from a somewhat niche model to a serious contender with the release of Grok 3. Initially, it gained attention mainly due to its connection to X (formerly Twitter) and its creator. However, Grok 3 has demonstrated impressive performance, even topping some benchmarks. While its long-term impact on the broader AI landscape remains to be seen, xAI’s high profile ensures that Grok will continue to be a topic of discussion. Essentially, it’s a model that’s now proving its capabilities, even if it initially rode on the coattails of its creator’s reputation.

Key Features:

• Trained on data from X (Twitter).
• State-of-the-art performance and reasoning abilities (Grok 3).
• Context window of 128,000.
• Available through chatbot and open access.
• Developed by xAI.

Phi-3 and Phi-4 (Microsoft)

Microsoft’s Phi-3 and Phi-4 models are all about achieving high performance in a compact package. These small language models are designed to be efficient, proving that size isn’t everything. They’re optimized to excel at language tasks, often outperforming larger models. Microsoft is making these models widely available, which makes them accessible to a broad range of developers and researchers. They are focused on being small, and high-performing.

Key Features:

• Optimized for performance at small size.
• Phi-3 Mini (3.8 billion), Phi-3 Small (7 billion), Phi-3 Medium (14 billion), and Phi-4 (14.7 Billion) parameter models.
• Context window up to 128,000.
• Available through Azure AI Studio, Hugging Face, and other open platforms.
• Focus on efficient language task performance.
• Open access.

Final Thoughts

As we’ve seen, Large Language Models are more than just advanced text generators; they’re transformative tools that are redefining how we interact with technology. Each model, whether open-source or proprietary, offers unique features and capabilities that cater to a variety of needs—from creative content generation to complex problem-solving. While challenges like ethical use and resource management remain, the ongoing evolution in this field promises a future where AI becomes an even more integral part of our daily lives. Ultimately, exploring the world of LLMs not only opens up exciting opportunities for innovation but also invites us to reconsider the way we communicate and work in a digitally connected world.

Read more: 7+ Best DeepSeek Alternatives You Should Try

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