8 best large language models for 2024

Feeling overwhelmed by AI jargon and countless models? You’re not alone. Understanding the best large language models in 2024 is easier than you might think. Thanks to recent advances in multimodal models, AI can now do more than just process text—it can also understand images, sounds, and other forms of data. In this blog, we’ll explore the top 8 LLMs shaping natural language processing (NLP) and help you decide which one to work with:

• GPT-4o

• Google Gemini

• Llama 3.1

• Claude 3

• Phi-2

• Mistral Large 2

• Gemma

• OLMo

But first, let’s break down what large language models are and why they matter to you.

Key Takeaways

• AI can now process not only text but also images, sounds, and other data types, making large language models more versatile.

• Each model's strengths and weaknesses are discussed to aid in making informed decisions about which one to use.

• Significant advancements in natural language processing driven by transformer-based neural networks are highlighted.

• The evolution of generative AI and the crucial role of large language models in various industries are emphasized.

What are large language models?#

A large language model is a transformer-based neural network trained on vast amounts of textual data to understand and generate human-like language. These LLMs can perform various NLP tasks, such as text generation, translation, summarization, sentiment analysis, etc. In recent developments, some LLMs have even evolved beyond simple text generation and now work with multimodal data, handling both text and other forms like images and audio. This progression marks a significant shift in generative AI with large language models.

A transformer is at the heart of large language models, like a machine that pays close attention to all the words in a sentence and figures out how they relate. It does this using a clever trick called self-attention—basically, it looks at each word and checks how important every other word is to understanding it. A basic transformer has two main parts: an encoder and a decoder. The encoder takes in the information (like a sentence), and the decoder spits the answer (like a new sentence). The encoder and decoder use layers of simple feed-forward networks to pass the information through.

Here’s the cool part: with multihead self-attention, the transformer doesn’t just look at one relationship between words—it looks at many at once, like examining the sentence from different angles. This lets the model understand complex meanings and generate text that makes sense.

Note: Not all large language models use an encoder and a decoder. For instance, decoder-only models like GPT-4o are optimized for generating human-like text based purely on input prompts.

A basic transformer-based model consisting of an encoder and decoder is shown below:

In such a model, the encoder is responsible for processing the given input, and the decoder generates the desired output. Each encoder and decoder side consists of a stack of feed-forward neural networks. The multi-head self-attention helps the transformers retain the context and generate relevant output.

The “large” in large language model refers to the massive scale of training data and the number of parameters involved. These models are trained on billions of words and sentences sourced from books, articles, websites, and other textual data. With millions to billions of parameters, LLMs capture complex linguistic patterns and relationships, making them powerful tools for diverse NLP tasks.

Training LLMs begins with gathering a diverse dataset from sources like books, articles, and websites, ensuring broad coverage of topics for better generalization. After preprocessing, an appropriate model like a transformer is chosen for its capability to process contextually longer texts. Training and fine-tuning follow afterward. This iterative process of data preparation, model training, and fine-tuning ensures LLMs achieve high performance across various natural language processing tasks.

Top 8 LLMs in 2024#

Let’s explore these top 8 language models influencing NLP in 2024 one by one.

1. GPT-4o#

First,  talk about GPT-4o, the latest and most advanced model from OpenAI. The “o” stands for “omni,” which is a fancy way of saying it can handle pretty much anything you throw at it—text, audio, images, and even video. It’s a big leap from the earlier GPT-4 and GPT-3.5-turbo, which mainly used text and images. GPT-4o can input all these input types and spit out text, sound, and pictures as output. Pretty neat, right?

However, here’s where it gets really interesting: GPT-4o is fast. It can respond to audio in just 232 milliseconds according to OpenAI benchmarks, which is almost as quick as you can respond in a real conversation, perhaps even more! That makes the whole interaction feel a lot more natural. It’s also better at handling languages other than English, and when it comes to understanding images and sound, it’s way ahead of the other models out there currently.

Of course, GPT-4o still has its quirks. Like the older models, it can sometimes hallucinate—meaning it makes up facts or mixes up names. You might ask it something about Elvis Presley, and it might give you information about Elvis Costello instead. But even with those hiccups, GPT-4o is one of the most powerful and versatile models, especially when handling multimodal tasks involving more than just plain text.

So, if you’re curious about how to make the most out of generative AI with large language models like GPT-4o, there’s a lot you can learn, and we’ve got some great interactive courses to help you get started. Check out Unleashing the Power of AI with OpenAI’s GPT-3 for a deep dive into how GPT fundamentally works.

2. Gemini#

Gemini is a multimodal LLM developed by Google and competes with others’ state-of-the-art performance in 30 out of 32 benchmarks. Its capabilities include image, audio, video, and text understanding. The Gemini family includes Ultra (175 billion parameters), Pro (50 billion parameters), and Nano (10 billion parameters) versions, catering various complex reasoning tasks to memory-constrained on-device use cases. One standout feature is Gemini’s ability to handle context windows up to 32k tokens, allowing it to efficiently manage long and complex inputs. It’s built on transformer architecture and uses multi-query attentionMulti-query attention is an optimization in Transformer models that shares key and value projections across heads, improving efficiency while maintaining separate query projections., enabling it to process text alongside audio and visual inputs and even generate text and image outputs.

Gemini’s performance often surpasses GPT models, largely due to Google’s immense computational resources and access to vast datasets. Notably, Gemini also supports video input, a capability that most of the GPT models, apart from GPT-4o, lacked, making it more powerful for tasks requiring cross-modal reasoning.

For example, consider this physics problem shown in the illustration below. A teacher drew a question on the left, and Gemini analyzed the student’s incorrect solution and explained the correct answer, identifying the errors and formatting the response in LaTeX for mathematical clarity.

Ready to dive deeper into Gemini’s capabilities and learn how to harness its full potential? Check out our “Getting Started with Google Gemini” course, where you’ll explore hands-on examples, advanced features, and real-world applications of this cutting-edge LLM. Whether new to AI or looking to refine your skills, this course is your step-by-step guide to mastering Gemini.

3. Llama 3.1#

Meta’s commitment to open-source AI continues with Llama 3.1, giving developers unprecedented access to a model that rivals the best in areas like general knowledge, math, multilingual translation, and even tool use. With its expanded 128k token context length, Llama 3.1 is perfect for advanced tasks like long-form text summarization, multilingual conversational agents, and coding assistants. The flagship model, Llama 3.1 405B, was trained on over 15 trillion tokens—an unprecedented scale in the open-source world. To handle this massive training task, Meta leveraged over 16,000 H100 GPUs, making Llama 3.1 the first model in its series to be trained at this level.

Compared to earlier versions, Llama 3.1 uses more refined data pipelines for both pre-training and post-training, with stricter quality assurance and filtering, ensuring that the model learns from the best possible data. As you’d expect from scaling laws, Llama 3.1’s 405 billion parameters make it significantly better than smaller models trained the same way, and it even helps improve the post-training quality of its smaller siblings.

And the best part? You can download Llama 3.1 and its smaller versions today from platforms like Hugging Face and Meta’s ecosystem for free or use them to improve other models—a first at this scale in the open-source world. If you're looking to unlock the full potential of Meta's Llama models, including Llama 3.1, don't miss our Prompt Engineering with Llama course. Whether you're a beginner or an advanced user, this course will equip you with the skills to optimize your interaction with one of the most advanced open-source models available.

4. Claude 3.5 Sonnet#

Claude 3.5 Sonnet, developed by Anthropic, is the latest upgrade to the Claude series, setting new benchmarks in AI performance. Built on the solid foundation of Claude 3, Claude 3.5 takes things to the next level with a significant boost in speed, precision, and cost-effectiveness.

Claude 3.5 Sonnet excels in graduate-level reasoning (GPQA), undergraduate-level knowledge (MMLU), and coding proficiency (HumanEval). It handles complex instructions easily and shows an improved ability to understand nuance, humor, and contextual subtleties, making it ideal for generating high-quality, natural-sounding content.

The standout feature is its coding prowess. In Anthropic’s internal coding evaluation, Claude 3.5 Sonnet solved 64% of coding challenges, compared to Claude 3 Opus, which solved only 38%. This demonstrates the model’s impressive capability to independently write, edit, and troubleshoot code, especially when fixing bugs or adding functionality based on natural language descriptions. This makes Claude 3.5 Sonnet particularly effective in updating legacy applications, migrating codebases, and translating code between languages.

In addition to being twice as fast as Claude 3 Opus, Claude 3.5 Sonnet is cost-effective, making it perfect for tasks like context-sensitive customer support, multi-step workflow orchestration, and content creation. Whether you need to solve complex problems or generate smooth, conversational text, Claude 3.5 delivers exceptional performance.

With its cutting-edge abilities in advanced reasoning, code generation, and content creation, Claude 3.5 Sonnet is not just an AI tool—it’s a highly reliable partner for coding, translation, troubleshooting, and data-driven decision-making.

5. Phi-2#

Phi-2, developed by Microsoft Research, is a 2.7 billion-parameter model that delivers impressive performance on complex reasoning and language understanding tasks. Thanks to model scaling and training data curation innovations, it matches or outperforms models up to 25x larger.
Built on the success of its predecessors, Phi-1 and Phi-1.5, Phi-2 leverages high-quality textbook data and carefully selected web content to train the model on common sense reasoning, science, and general knowledge. Despite its smaller size, Phi-2 performs exceptionally well across benchmarks for models under 13 billion parameters.

Designed as a compact yet powerful tool, Phi-2 is ideal for researchers exploring interpretability, safety improvements, or fine-tuning experiments. Available through the Azure AI Studio model catalog, it fosters research into smaller, highly efficient models that rival much larger counterparts.
The following illustration depicts Phi-2 accurately solving a similar problem to the physics problem we saw in the Gemini example.

6. Mistral Large 2 #

Mistral Large 2, developed by Mistral AI, is a 123 billion-parameter model designed for single-node inference and long-context applications. It supports a 128k token context window, enabling precise handling of large documents across dozens of languages, including French, German, Spanish, Chinese, and Arabic, along with over 80 coding languages like Python, Java, and C++.

Mistral Large 2 delivers cutting-edge performance, achieving 84% accuracy on MMLU benchmarks and setting new standards for open models’ performance/cost ratio. Built on a strong code and reasoning training foundation, it rivals models like GPT-4o and Llama 3 405B in coding and problem-solving tasks.

The model’s fine-tuning reduces hallucinations, ensuring more accurate and cautious outputs.

7. Gemma#

Gemma is a family of open models based on Google’s Gemini architecture, trained on up to 6 trillion text tokens. These models excel in textual understanding, reasoning, and generalist capabilities across various domains. Available in two sizes—7 billion parameters for GPU/TPU applications and 2 billion parameters for CPU/on-device tasks—Gemma provides both pretrained and fine-tuned checkpoints, optimized for dialogue, instruction-following, and safety.

Gemma outperforms many comparable and larger open models, with strong performance in question answering, commonsense reasoning, mathematics, and coding. Its release includes a comprehensive open-source codebase, allowing for extensive research, development, and safe model deployment.

8. OLMo#

The Allen Institute for AI (AI2) developed the Open Language Model (OLMo). The model's sole purpose was to provide complete access to data, training code, models, and evaluation code to collectively accelerate the study of language models.

OLMo is trained on the Dolma dataset developed by the same organization, which is also available for public use.

Which model to use?#

Every model comes with its own set of strengths and weaknesses, so the right choice really boils down to what you need it for. Are you working with text, images, or both? Do you have plenty of computing power, or are you limited? Maybe you're focused on speed, or perhaps you care more about things like ethical AI or working with open-source tools. The best model for you depends on your priorities and the resources you have available.4o

Here's a comparison table to take into account:

We hope this overview helped you acquaint yourself with the LLM landscape.

If you're looking to dive deeper into the world of LLMs, Educative has several interactive courses you may find useful:

• Unleash the Power of Large Language Models Using LangChain

• Essentials of Large Language Models: A Beginner’s Journey

• Transformers for Natural Language Processing

We also offer Projects, which you can use to build as you learn (while growing your portfolio):

• Query CSV Files with Natural Language Using LangChain and Panel

• Build an LLM-Powered Wikipedia Chat Assistant with RAG

You can explore more of our hands-on AI resources here.