An Overview of The Best LLMs of 2024: What to Expect in 2025

Today, technology moves at lightning speed, so it’s no wonder that large language models are capturing our attention. From virtual assistants who understand our every command to AI tools that help us predict storms, these powerful models are becoming a part of our daily lives.

But with so many new models emerging, how do we keep up? Recently, some exciting contenders are challenging the dominance of popular models like GPT-4 and Mistral. Let's explore the best LLMs of the year, exploring their unique features and how they can enhance everything from work to leisure.

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Google’s Gemini 1.5

Google’s Gemini 1.5 is the newest update to their AI model, bringing significant boosts in both performance and efficiency. It builds on the foundation of Gemini 1.0 but adds a Mixture-of-Experts (MoE) architecture. This new system helps the model work smarter by only activating the most relevant neural network paths, which makes it more efficient and effective.

The Gemini 1.5 Pro model is designed to handle multiple tasks and is built for scalability. It performs similarly to the previous 1.0 Ultra model but introduces a key innovation: a context window of up to 1 million tokens. This means the model can process a large amount of data, such as an hour-long video or a sizable codebase, all in one go without breaking a sweat.

Another strength of Gemini 1.5 is its powerful in-context learning abilities. It can quickly adapt to new information without needing additional fine-tuning. This was highlighted in tests like the “Needle In A Haystack” evaluation, where Gemini 1.5 Pro successfully identified specific pieces of text in large datasets 99% of the time.

For developers and businesses eager to try it out, Gemini 1.5 Pro is now available for early testing through AI Studio and Vertex AI. Google will soon offer different pricing plans based on the context window size, with the 1 million token experimental window available for free during testing.

OpenAI’s GPT-4o

Recently, OpenAI introduced GPT-4o, its newest and most advanced model. This update brings together text, audio, and visuals into one powerful tool. The "o" in GPT-4o stands for “omni,” showing how it easily works with different kinds of inputs and outputs. This makes conversations smoother, with the AI responding through text, sound, and images at the same time.

One of the best features of GPT-4o is how fast it reacts to audio; just 232 milliseconds, making it feel like talking to a person. It also performs as well as GPT-4 Turbo when handling text and coding tasks.

What makes GPT-4o stand out is that it combines many abilities into one system. Unlike older models that needed separate tools for things like transcribing audio or generating text, GPT-4o does it all in one. It can catch small details in audio, like different tones, multiple speakers, or background noises, and even create more natural responses, like laughter, singing, or emotions. It matches GPT-4 Turbo’s speed and accuracy in text, reasoning, and coding, while also working better with multiple languages, sound, and images. It’s also faster and 50% cheaper to use through the API, making it a smart choice for developers.

Right now, GPT-4o’s audio responses use only a few preset voices, but OpenAI is working to improve that for better usability and safety. Text and image inputs are fully ready, and more features will be added soon. Developers can use the OpenAI API to easily bring GPT-4o’s abilities into their apps, unlocking everything from real-time chat to multimedia creation.

GPT-4o sets a new standard for AI by making interactions faster, smoother, and affordable. With a focus on safety and responsible use, it’s a powerful tool for everyone.

Cohere’s Command R+

Cohere has just launched Command R+, an exciting new open-source large language model built for businesses. With an impressive 104 billion parameters, Command R+ is made to tackle real-world challenges, providing top-notch performance in many important areas. One of its standout features is retrieval-augmented generation (RAG), where it achieves a solid 73.7% accuracy rate. This model also supports 10 major business languages, making it a handy tool for teams around the world. Plus, Command R+ is really good at using multiple tools at the same time, which is vital for automating complex tasks in companies.

When compared to other popular models like GPT-3.5 and PaLM, Command R+ shows it can keep up. It scored 88.2% on the MMLU benchmark, which is better than GPT-3.5 and Chinchilla, and it’s almost as good as larger models like PaLM 62B and Claude from Anthropic. In coding tasks, it has a success rate of 71.4%, which is on par with other leading models. It also shines in common-sense reasoning, scoring 91.2% on HellaSwag and 90.6% on PIQA, proving it can compete with more expensive options.

For developers and researchers, Command R+ is easy to access. You can get it through an API or download it directly. The model weights are also available on Hugging Face under a CC-BY-NC license. By making this model open-source, Cohere aims to give everyone access to advanced AI technology, encouraging innovation from the community.

Efficiency is another big plus for Command R+. It’s designed to be faster and cheaper than many competitors, generating outputs up to five times quicker and costing 50 - 75% less per output token compared to GPT-4. This blend of performance and affordability makes it a great choice for businesses wanting to expand their AI solutions without breaking the bank.

Meta’s Llama 3.1

Meta has launched Llama 3.1, its most advanced AI model yet, with a focus on being open and easy to use. The biggest model in this release, Llama 3.1 405B, is the largest open foundation model available. It shows top-level abilities in things like general knowledge, multilingual translation, and using tools. Plus, it supports a long context length of 128K and works in eight languages. Llama 3.1 is designed to boost innovation, opening doors to new possibilities like creating synthetic data and refining models. Along with the main model, Meta has also updated its 8B and 70B models. New safety features like Llama Guard 3 and Prompt Guard are included to ensure AI is used responsibly.

Meta is growing the Llama ecosystem by offering more tools and a reference system for developers to build custom AI agents. It’s supported by over 25 partners, including AWS, NVIDIA, and Google Cloud. Developers can easily download the Llama 3.1 models from llama.meta.com or Hugging Face, making them simple to implement across different environments; whether locally, on-premises, or in the cloud. The models support a range of functions like real-time and batch inference, fine-tuning, continual pre-training, retrieval-augmented generation (RAG), function calling, and synthetic data generation.

Performance-wise, the Llama 3.1 models, especially the 405B, have been tested on over 150 datasets and in real-world settings. The 405B model performs well against leading closed models like GPT-4, GPT-4o, and Claude 3.5 Sonnet in areas like general knowledge, math, tool usage, and multilingual tasks. Even with its 128K context length, it still maintains high-quality performance, placing it among the top open-source AI models.

This strong performance, paired with the flexibility of being open-source, makes Llama 3.1 a powerful choice for developers looking to build advanced AI applications without being limited by closed platforms.

xAI’s Grok-2

Founded by Elon Musk, xAI has just released the beta version of Grok-2, its newest language model, along with a smaller version called Grok-2 mini, which is still very powerful. These models are a major upgrade from the earlier Grok-1.5, offering better performance in reasoning, chatting, coding, and tasks that involve vision.

Grok-2 and Grok-2 mini have shown impressive results in various tests, including reasoning, reading comprehension, math, and science. Grok-2 stands out by outperforming popular models like Claude 3.5 Sonnet and GPT-4-Turbo on the LMSYS leaderboard, highlighting its strong abilities in chat and coding tasks. It also excels in academic areas, such as graduate-level science questions, general knowledge tests, and visual math reasoning, making it a strong competitor among top models.

You can access Grok-2 and Grok-2 mini in beta through the Grok tab if you're a Premium or Premium+ user. These models can pull in real-time information, making them great AI assistants for answering questions and helping with writing, and coding.

For developers, Grok-2 and Grok-2 mini will be available later this year through an enterprise API platform. This API will allow for fast access and secure deployment, making it easier to integrate Grok’s features into existing tools and services. It’s a valuable resource for anyone looking to create AI-powered applications.

Anthropic’s Claude 3.5 Sonnet and Claude 3.5 Opus

Anthropic has launched Claude 3.5 Sonnet, bringing faster speed, lower costs, and better performance than its predecessor, Claude 3 Opus. Sonnet outperforms Opus in many tasks while also being more affordable. It excels in humor and sarcasm comprehension, making conversations more natural, and offers advanced visual skills, accurately reading unclear text, graphs, and handwriting. While it competes well with GPT-4 in multilingual math and reasoning, GPT-4 still leads in specific math problems.

A new feature, Artifacts, allows Claude to display and interact with content like code snippets or web designs in a sidebar, transforming it into a productivity tool. Currently, in preview, Artifacts promises better integration of AI-generated content into projects.

Claude 3.5 Sonnet is available for free and paid users on the Claude website and iOS app, with Pro subscribers enjoying higher rate limits. It sits between the smaller Haiku model for quick tasks and the larger Opus for demanding ones. Offering top features at a lower price, Sonnet is a competitive choice among large language models, especially for visual reasoning and multilingual tasks.

The Claude 3.5 Opus model is still on the way, with its launch anticipated later in 2024. The Claude family is categorized as follows:

• Haiku: The quickest and most affordable, but with limited power.
• Sonnet: The ideal blend of speed, cost, and performance.
• Opus: The slowest and most expensive, yet delivers the best performance.

Mistral’s Mistral Large 2

Mistral has made waves in the LLM landscape with its latest release, Mistral Large 2.  This new version comes with big improvements in areas like code writing, math, reasoning, and support for multiple languages. Mistral Large 2 aims to boost performance, save costs, and speed things up, making it a strong choice among large language models.

Mistral Large 2 has a 128k context window and can understand many languages, including popular ones like French, German, Spanish, Chinese, and Arabic. It also supports over 80 programming languages, such as Python, Java, and C++. This model is especially good for tasks that need a lot of context and has 123 billion parameters, allowing it to handle many tasks efficiently on a single system.

It performs really well in code generation and reasoning, beating its previous version and competing with top models like GPT-4o, Claude 3 Opus, and Llama 3 405B. Mistral Large 2 has been improved to reduce mistakes, ensuring it gives accurate and reliable results, especially for math and problem-solving tasks.

Mistral Large 2 sets a new standard for performance and cost-effectiveness, achieving an impressive accuracy of 84.0% on the MMLU (Massive Multitask Language Understanding) benchmark. It’s just as good as top models when it comes to code generation and reasoning, and it does well with tasks in different languages, showing it can adapt to various needs.

The model’s ability to follow instructions and have conversations has also improved a lot. This makes it better for ongoing discussions and precise tasks. Mistral Large 2 is designed to give clear and concise answers, which is important for businesses that need quick responses.

You can use Mistral Large 2 on “la Plateforme” under the name “mistral-large-2407.” It’s accessible through an API and can be fine-tuned with Mistral’s SDK, making it a flexible tool for developers and researchers. For research and non-commercial use, it’s available under the Mistral Research License, while commercial use requires a different license. The model is also connected with major cloud providers like Google Cloud Platform via Vertex AI, Azure AI Studio, Amazon Bedrock, and IBM watsonx.ai, ensuring it’s easy to access and deploy.

In short, Mistral Large 2 is a powerful and cost-effective LLM that excels in code generation, reasoning, and multilingual tasks, with flexible options for both research and business use.

2025 AI Predictions: Transforming Businesses and Daily Life with LLMs

Predictions for 2025 in AI and large language models (LLMs) point towards several transformative trends:

1. Widespread Integration in Business

LLMs like OpenAI’s GPT-4 have shown powerful capabilities in automating tasks such as customer service, content generation, and internal communication. By 2025, it's expected that these models will be more integrated into daily business operations, especially in sectors like healthcare, finance, and customer service. This integration will improve efficiency and reduce costs significantly, allowing businesses to operate faster with better customer experiences​.

2. Advances in Self-Supervised Learning

A major shift in AI development is the rise of self-supervised learning, which will enable models to train themselves without the need for massive, human-labeled datasets. This shift is expected to reduce the costs of data labeling by 50%, accelerating AI adoption in industries such as manufacturing, healthcare, and finance. By 2025, businesses will be saving billions due to these innovations​.

3. Creative AI and NLP

By 2025, natural language processing technologies are expected to power a significant portion of digital communications. These technologies will enhance applications ranging from virtual assistants to customer service bots, allowing for more detailed, human-like interactions. In education and healthcare, this will allow systems to understand and respond to natural language queries from users, transforming how services are delivered.

4. Increased Automation in Daily Life

AI and robotics will likely become a more invisible part of daily life, handling tasks such as driving, deliveries, and even household management. In sectors like transportation and logistics, automation will change how we think about vehicle ownership and transportation efficiency, leading to driverless cars and more robot-managed services​.

These trends highlight the growing role of AI in transforming industries and daily life by 2025. Businesses that adopt AI technologies early will gain a significant competitive advantage.

Lastly

As we look ahead, the future of AI language models is exciting and full of potential. Whether you're managing a business, working on creative projects, or just curious about technology, these models, like Meta’s Llama 3.1 or xAI’s Grok-2, are making things easier and more efficient. Staying informed on the latest updates will help you make the most of these tools. 

The world of AI is moving fast, and these breakthroughs could be the key to staying ahead in both work and daily life.