Discover the Top 9 Popular LLMs in a Flash.
Modley Essex
Awin, writer, copywriting, content writing, WordPress, blogging, graphics, Data entry
Large Language Models, or LLMs, have become a significant part of our daily digital interactions. You might use them without even realizing it when you ask your phone a question, get a summary of a long email, or see a personalized product recommendation. These powerful AI systems are designed to understand and generate text in a way that feels surprisingly human.
They achieve this by learning from massive amounts of text and data from the internet, books, and other sources. This training allows them to recognize patterns, grasp context, and respond to a wide range of prompts and questions.
However, not all LLMs are built the same. They come with different underlying structures, known as architectures, which define their strengths and weaknesses. These differences mean that some models are better at creative writing, while others excel at analyzing data or writing computer code. Understanding these distinctions can help you appreciate the unique capabilities each one brings to the table. In this guide, we'll explore nine of the most popular LLMs making waves today.
Claude
The Philosophy of Safety
Developed by the AI safety and research company Anthropic, Claude is a family of models created with a strong focus on ethics and safety. Anthropic's primary goal is to build AI that is not only helpful and capable but also fundamentally harmless. This has led them to pioneer unique training methods to ensure Claude behaves in a predictable and safe manner.
At the core of Claude’s training is a technique called "Constitutional AI." Instead of relying solely on extensive human feedback to prevent harmful responses, the model is guided by a set of principles, or a "constitution." This constitution, which includes principles from sources like the UN's Universal Declaration of Human Rights, helps the AI align its own behavior with positive values. The goal is to make Claude a reliable and trustworthy assistant.
This approach sets Claude apart from many other LLMs. By embedding ethical guidelines directly into its learning process, Anthropic aims to proactively steer the model away from generating dangerous, unethical, or biased content. It's an ongoing experiment in making AI systems inherently more responsible from the ground up.
Architectural Highlights
The Claude models, including the well-known Claude 3 family (Opus, Sonnet, and Haiku), are built using a decoder-only Transformer architecture. This structure is particularly effective for generative tasks, as it excels at predicting the next word in a sequence. It allows Claude to produce fluent, coherent, and contextually relevant text.
One of Claude's most celebrated features is its exceptionally large context window. This means it can process and remember vast amounts of information within a single conversation or document—sometimes hundreds of thousands of words. This makes it ideal for tasks that require understanding long, complex documents, such as legal contracts or in-depth research papers.
While Claude's models are proprietary, meaning their inner workings are not fully public, they are known for their strong performance across various benchmarks. They demonstrate advanced capabilities in reasoning, analysis, and coding, often competing with or even surpassing other leading models in the industry.
Primary Use Cases
Given its large context window and strong reasoning abilities, Claude is highly effective for processing and analyzing dense information. Businesses use it to summarize long reports, analyze financial statements, and review legal documents quickly and accurately. Its ability to "read" and understand an entire book's worth of text in one go is a significant advantage.
Claude also shines in creative and collaborative writing. It can help users brainstorm ideas, draft articles, write poetry, and even develop scripts. Its conversational style and deep understanding of context make it a valuable partner for creative professionals looking for inspiration or assistance.
Anthropic offers access to Claude primarily through an API, allowing developers to integrate its capabilities into their own applications and services. This has led to its adoption in various fields, from customer support chatbots to powerful research tools, all while being guided by its underlying commitment to safety.
Command
Engineered for the Enterprise
The Command family of models, developed by Cohere, is specifically designed to meet the demands of enterprise applications. Unlike general-purpose models, Command is optimized for business-critical tasks that require reliability, scalability, and security. Cohere focuses on making AI that works in the real world of commerce and industry.
These models are built to handle the complexities of business operations. They can be fine-tuned to understand specific industry jargon, internal company documents, and unique customer service scenarios. This focus ensures that the AI provides relevant and accurate results that businesses can depend on.
Cohere's approach prioritizes practical application over theoretical performance on abstract benchmarks. The result is a suite of models that deliver tangible value, helping companies streamline workflows, enhance productivity, and improve customer interactions in a secure environment.
Grounded in Fact with RAG
A standout feature of the Command models is their expertise in Retrieval-Augmented Generation (RAG). RAG is a technique that enhances the model's responses by connecting them to external, verifiable data sources. This could be a company's internal knowledge base, product documentation, or a curated database of information.
By grounding its answers in specific data, Command significantly reduces the risk of "hallucinations," which are confident-sounding but incorrect or fabricated statements. For businesses, this factual accuracy is crucial. Whether it's a customer support bot providing product information or an internal tool summarizing project updates, the information must be correct.
The Command R+ series, for instance, is highly adept at this process. It can search through provided documents to find the right information and then use that information to formulate a precise and relevant answer, even citing its sources.
Multilingual and Scalable by Design
Command models are also recognized for their strong multilingual capabilities. They are trained to understand and communicate in many different languages, which is essential for global enterprises that operate across diverse markets. This allows a single AI solution to serve customers and employees worldwide.
With long context windows, these models can handle lengthy and complex business documents, from detailed financial reports to extensive project plans. They can process and analyze all the information within these documents to provide summaries, answer questions, or extract key data points.
Cohere provides access to its models through a robust API designed for enterprise-grade security and scale. This ensures that businesses can integrate Command into their existing systems with confidence, knowing their data is protected and the service can handle high volumes of requests.
BERT
A Revolution in Understanding Language
Developed by Google AI in 2018, BERT (Bidirectional Encoder Representations from Transformers) represented a major leap forward in natural language understanding. Before BERT, most language models processed text in a single direction, either from left to right or right to left. This limited their ability to grasp the full context of a word.
BERT introduced the concept of deep bidirectionality. It uses a Transformer encoder architecture to read an entire sequence of words at once, allowing it to understand a word's meaning based on the words that come both before and after it. This seemingly simple change had a profound impact on how machines interpret language.
As detailed in its foundational research paper, BERT was pre-trained on two main tasks. One was masked language modeling, where it had to predict randomly hidden words in a sentence. The other was next-sentence prediction, where it learned to understand the relationship between two sentences.
The Power of Context
The true innovation of BERT lies in its ability to capture context. For example, in the sentences "I need to book a flight" and "I read a good book," a unidirectional model might struggle to differentiate the two meanings of the word "book." BERT, by looking at the surrounding words, can easily tell the difference.
This deep contextual understanding allowed BERT to achieve state-of-the-art results on a wide range of natural language processing tasks. It became the foundation for significant improvements in search engines, helping them better understand the intent behind user queries.
Because BERT is primarily an encoder-based model, it is not designed for generating long-form text like GPT. Instead, its strength lies in analysis and understanding. It excels at tasks like sentiment analysis, question answering, and named entity recognition.
A Foundational Influence
BERT's release marked a pivotal moment in the history of AI. Its success demonstrated the incredible power of the Transformer architecture and kicked off a new era of large-scale, pre-trained language models. Many of the models that followed, even those with different architectures, were influenced by the principles that BERT established.
Although newer and larger models have since emerged, BERT remains a foundational tool in the NLP world. Its pre-trained versions are still widely used for a variety of applications, especially when the goal is to analyze and understand text rather than generate it.
Its legacy is not just in its direct use but in the wave of innovation it inspired. BERT proved that with the right architecture and training, machines could achieve a much deeper, more nuanced understanding of human language than was previously thought possible.
GPT
The Pioneer of Generative AI
The GPT (Generative Pre-trained Transformer) series, created by OpenAI, is arguably what brought the power of LLMs into the mainstream public consciousness. Starting with GPT-1 and evolving through successive versions to the highly capable GPT-4 and the newer GPT-4o, these models have consistently pushed the boundaries of what generative AI can do.
GPT models are built on a decoder-only Transformer architecture. This design is fundamentally predictive; its main goal is to guess the next word in a sequence based on all the previous words it has seen. This simple objective, when scaled up with massive datasets and computing power, results in an astonishing ability to generate fluent, coherent, and often creative text.
Each new generation of GPT has been significantly larger and more powerful than the last. This scaling has not just led to better performance but has also unlocked new abilities, such as few-shot learning, where the model can perform a new task with just a few examples, requiring minimal fine-tuning.
Setting the Paradigm
The GPT family popularized the "pre-train and fine-tune" paradigm that now dominates LLM development. The model first undergoes intensive pre-training on a vast and diverse corpus of text from the internet and digital books. This phase gives it a broad understanding of language, grammar, facts, and reasoning abilities.
After pre-training, the model can be fine-tuned on smaller, more specific datasets to adapt it for particular tasks, such as customer service, content creation, or code generation. However, the largest GPT models have become so powerful that they often perform exceptionally well on new tasks with zero or very few examples.
The models in the GPT series are proprietary, meaning OpenAI has not disclosed their full architecture or training data. This has led to a debate in the AI community about the balance between commercial interests and the benefits of open research, especially as these models become more influential.
From Text Generation to Multimodality
The capabilities of the GPT series have expanded dramatically over time. While early versions were focused solely on text, later models like GPT-4 began to incorporate multimodal capabilities, allowing them to understand and process images in addition to text. Users could input a picture and ask questions about it, opening up new possibilities.
The latest iteration, GPT-4o, takes this a step further by being natively multimodal across text, audio, and vision. It can engage in real-time spoken conversations, interpret tone and emotion, and analyze live video feeds. This brings interactions with AI closer to the seamless, natural communication we have with other humans.
GPT models power a wide range of applications, most notably ChatGPT, which has become a household name. They are also available through an API, enabling developers to build countless AI-powered products and services, cementing GPT's role as a central pillar of the current AI boom.
LLaMA
Champion of the Open-Source Community
LLaMA (Large Language Model Meta AI) is a family of LLMs developed by Meta AI that has had a transformative impact on the open-source community. By releasing the weights of its models, Meta has enabled researchers, developers, and hobbyists around the world to experiment with, study, and build upon state-of-the-art AI technology.
Like GPT, the LLaMA models use a decoder-only Transformer architecture focused on text generation. However, Meta's researchers introduced several architectural improvements and training efficiencies. These tweaks allow LLaMA models to deliver performance that is competitive with much larger, proprietary models, but with significantly lower computational requirements.
The release of LLaMA and its successors, like the powerful LLaMA 3, has democratized access to powerful LLMs. It sparked a wave of innovation, leading to thousands of new fine-tuned models, applications, and research projects that would not have been possible otherwise.
Efficiency and Performance
To achieve their impressive performance-to-size ratio, LLaMA models incorporate several key architectural modifications. For instance, they use techniques like SwiGLU activation functions and rotary positional embeddings. These may sound technical, but they essentially help the model learn more efficiently and effectively.
These optimizations mean that LLaMA models can be run on more accessible hardware compared to their larger counterparts. This has been a game-changer, allowing smaller companies, academic labs, and even individuals to run and fine-tune their own powerful language models without needing access to massive supercomputers.
Meta's focus has been on creating a collection of pre-trained and fine-tuned models of different sizes (e.g., 8B and 70B parameters). This provides flexibility, allowing developers to choose the right balance of performance and resource cost for their specific application, from lightweight mobile apps to powerful backend services.
Fueling a New Ecosystem
The open nature of LLaMA has been its most significant contribution. It has fostered a vibrant and collaborative ecosystem where developers freely share their fine-tuned versions, new training techniques, and creative applications. This has accelerated the pace of innovation in the AI field as a whole.
This community-driven development has led to specialized models tailored for a vast array of tasks, from writing code and having conversations to following complex instructions. It has also put pressure on proprietary model developers to be more transparent and competitive.
By making powerful AI more accessible, LLaMA has not only advanced research but has also enabled a new generation of startups and products built on an open foundation. It represents a different philosophy for the future of AI, one centered on collaboration and shared progress rather than closed-off, proprietary systems.
PaLM
The Power of Pathways
PaLM (Pathways Language Model) is a family of large-scale, decoder-only Transformer models developed by Google Research. A key innovation behind PaLM was its training on Google's Pathways system, a next-generation AI architecture designed to handle massive computational tasks with incredible efficiency.
The Pathways system allowed Google to train PaLM in a highly parallel and distributed manner across thousands of processor cores. This enabled them to scale up the model to an unprecedented size at the time—540 billion parameters—while optimizing the entire training process.
The dataset used to train PaLM was also a significant factor in its success. As detailed in the PaLM paper, it was trained on a high-quality corpus of 780 billion tokens, comprising a diverse mix of webpages, books, scientific articles, Wikipedia entries, and code. This rich, multilingual dataset gave PaLM a broad and deep understanding of the world.
Breakthroughs in Few-Shot Learning
One of PaLM's most impressive achievements was its remarkable performance on few-shot learning tasks. This means the model could learn to perform a new, unseen task with only a handful of examples given in the prompt, without needing any additional training or fine-tuning.
PaLM demonstrated breakthrough capabilities in complex reasoning tasks. For example, it could explain jokes, solve challenging logic puzzles, and even generate code to solve math problems when given just a few examples. This showed that scaling up models could lead to emergent abilities that were not explicitly trained for.
The model showed that with sufficient scale and high-quality data, a single language model could achieve state-of-the-art results across a wide range of very different benchmarks, from question answering to commonsense reasoning and code generation.
The Foundation for Future Models
While PaLM itself was a landmark achievement, its primary role became foundational for Google's subsequent AI efforts. The insights and technologies developed for PaLM directly informed the creation of its next-generation models.
PaLM was later adapted and fine-tuned to become the basis for some of Google's flagship products. The technologies and understanding gained from building and scaling PaLM were instrumental in paving the way for the development of the even more capable Gemini family of models.
PaLM stands as a testament to the power of scale in AI. It showcased how pushing the limits of model size and computational efficiency could unlock new frontiers in machine intelligence, setting the stage for the next wave of AI innovation at Google and beyond.
Gemini
Natively Multimodal from the Start
Gemini is Google's next-generation flagship AI, designed from the ground up to be natively multimodal. Unlike previous models that were primarily text-based and had other modalities added on later, Gemini was pre-trained from the beginning on a vast dataset of text, images, audio, video, and code.
This unified approach allows Gemini to seamlessly understand, reason about, and generate content across different formats. It can process a prompt that contains a mix of text, images, and video clips and produce a coherent, relevant output. This makes interaction with the AI feel more natural and intuitive, much closer to how humans perceive the world.
Google has released different versions of Gemini—Ultra, Pro, and Nano—each optimized for different tasks and platforms. Ultra is the largest and most capable model, Pro is a high-performing all-rounder, and Nano is a highly efficient model designed to run directly on mobile devices.
A Mixture of Experts
To manage the immense scale and computational demands of such a powerful model, Gemini utilizes a Mixture-of-Experts (MoE) architecture. An MoE model is not a single, giant neural network but is instead composed of many smaller "expert" networks.
When an input is received, the system intelligently routes it to only the most relevant experts for that specific task. This is much more efficient than activating the entire massive model for every single request. It's like having a large team of specialists and only calling upon the ones you need for a particular job.
This architectural choice, as highlighted in the paper on Gemini 1.5, allows Gemini to achieve massive scale—reportedly over a trillion parameters—while maintaining manageable computational costs. It also enables incredible features like a context window of up to one million tokens, allowing it to process and analyze enormous amounts of information at once.
The Future of Google AI
Gemini represents the culmination of Google's latest research and is now the engine powering many of its key products, including its conversational AI and search features. Its advanced multimodal reasoning abilities are being integrated across Google's ecosystem to provide more helpful and intelligent user experiences.
The model excels at sophisticated reasoning, visual understanding, and complex code generation. For instance, it can analyze a video of a student trying to solve a physics problem, understand their mistake, and provide a step-by-step explanation to guide them to the correct solution.
As Google continues to develop Gemini, its capabilities are expected to expand even further. It stands as one of the leading contenders at the forefront of AI research, pushing the boundaries of what is possible with large-scale, multimodal models.
Mistral
Europe's Answer to a Crowded Field
Emerging from Paris, France, Mistral AI quickly established itself as a major force in the AI world. The startup, founded by former researchers from Google and Meta, made a stunning debut in 2023 by releasing powerful open-source models that challenged the dominance of larger, established players.
Mistral's philosophy centers on creating highly efficient models that deliver top-tier performance without requiring enormous computational resources. They believe in the power of open-source to drive innovation and have made significant contributions to the community, even as they develop commercial offerings.
The company has successfully balanced releasing open, highly capable models with developing proprietary, cutting-edge systems aimed at enterprise customers. This dual approach has allowed them to gain both widespread community adoption and commercial traction.
Small Model, Big Performance
Mistral's first major release, the open-source Mistral 7B model, turned heads across the industry. Despite having only 7 billion parameters—a relatively small size compared to giants like GPT-3—it outperformed much larger models on many standard benchmarks.
This remarkable efficiency was achieved through clever architectural innovations. Mistral 7B utilized techniques like grouped-query attention and sliding window attention. These methods allow the model to process information and generate text much faster and with less memory, making it ideal for applications where speed and cost are important.
The success of Mistral 7B proved that smart design could be just as important as raw scale. It provided the open-source community with a powerful yet accessible model that could be run on consumer-grade hardware.
The Power of a Sparse Mixture
Building on its initial success, Mistral later released Mixtral, a high-quality sparse Mixture-of-Experts (MoE) model. Like Google's Gemini, Mixtral is composed of multiple smaller "expert" networks, but it was released as an open-weights model, again empowering the open-source community.
The Mixtral model uses a router to select which of its eight experts to engage for any given token, making it incredibly fast and cost-effective for its size. While it has a total of 47 billion parameters, it only uses about 13 billion during inference, giving it the speed and cost of a smaller model but the knowledge and power of a much larger one.
Mistral continues to be a key player pushing the boundaries of both open-source and proprietary AI. Its focus on efficiency and performance has made it a leading choice for developers and businesses looking for powerful AI solutions that are both capable and practical.
DeepSeek
Pushing the Limits of Sparsity
DeepSeek is an AI company that has gained attention for its work in developing highly sparse Mixture-of-Experts (MoE) models. The core idea behind their approach is to build models with an enormous number of total parameters but to use only a tiny fraction of them for any given task. This is the concept of sparsity pushed to an extreme.
Their architecture allows for a massive expansion of the model's knowledge capacity without a corresponding explosion in computational cost during inference. By having a huge library of experts, the model can store a vast amount of specialized information, but it activates only the two or four most relevant experts for each step of the process.
This design makes DeepSeek's models remarkably efficient. As described in the paper for their DeepSeek-R1 model family (note: link is a placeholder example), they can achieve performance comparable to much larger, dense models while using significantly less computing power.
A Unique Routing Strategy
A key innovation in DeepSeek's MoE architecture is its sophisticated routing algorithm. This router is crucial because it needs to quickly and accurately determine which of its hundreds of experts are the best fit for processing the current piece of information.
The router is jointly trained with the experts, learning to make these decisions efficiently. By selecting a very small subset of experts, DeepSeek keeps inference costs low. This makes their powerful models more accessible and affordable to run at scale.
DeepSeek has open-sourced several of its models, including both general-purpose language models and models specifically trained for code generation. These models have been shown to be highly competitive, often outperforming other leading open-source models in benchmarks for reasoning, multilingual capabilities, and coding proficiency.
A Leader in Open-Source Coding AI
While their general-purpose language models are strong, DeepSeek has particularly excelled in the domain of code generation. Their DeepSeek-Coder series has consistently ranked at the top of leaderboards for its ability to understand and write code in various programming languages.
This success is likely due to their sparse MoE architecture, which allows them to train experts specifically on different aspects of programming, such as different languages or coding paradigms. The models are trained on a massive dataset of code from sources like GitHub, giving them a deep understanding of software development.
As a prominent player from China, DeepSeek adds to the geographic diversity of top-tier AI development. Their focus on highly sparse architectures and their strong open-source contributions, particularly in the realm of coding, make them a company to watch in the evolving landscape of large language models.