6 Big Language Models (LLM) that will stand out in 2024

Large Language Models (LLMs) stand out as one of the most important parts of artificial intelligence. As we mentioned in our article where we listed the prominent artificial intelligence models of the summer period, in 2024, these models will have a great impact both in the business world and in our daily lives. In this article, we have prepared a guide to find the strongest and most popular LLMs of 2024, where they stand out and which one best suits your needs.

What are Large Language Models and how do they work?

Large Language Models (LLMs), which we have previously tried to cover in full detail in 90 seconds, are artificial intelligence models that stand out with their ability to understand and produce human language. These models are trained on large data sets, learning the complex structure of the language and making predictions. Open source models, in particular, provide flexibility to users so that they can customize the models according to their needs. LLMs offer a wide range of uses, from text production to translation, from content moderation to data analysis.

How do LLMs reason?

LLMs learn the structure of language from massive data sets and predict the optimal response to a given input. Millions of parameters and complex layers are used in this process. Language models analyze text data, model relationships in language, and make predictions using these relationships. For example, when the word “apple” appears in a sentence, the next word is evaluated as possibilities such as “fruit” or “tree”.

The rise of Open Source LLMs: AI accessible to all

Open source LLMs allow users to download these models, train them with their own data, and customize them. For example, open source models such as Meta’s Llama model or Mistral give developers more flexibility. These models can be made available for both commercial and individual use based on licensing. This also stands out as a competitive strategy for AI companies, because by offering open source models, they provide access to a wider user base and encourage innovation.

In what areas do LLMs, the new supporters of daily life, shine?

LLMs have a wide range of uses thanks to their versatility. General purpose chatbots are used in many areas such as customer services, content production and text analysis. It is also preferred for operations such as translation, language correction, and text editing. With developing technology, LLMs can be customized for more specific tasks and are gaining effectiveness in different fields with their multimodal data processing capabilities.

Various LLMs are being developed so that each model can meet different needs. Competition between open source and commercial models gives users more choice and flexibility. While some models offer high accuracy, some are optimized for specific areas of use. Additionally, companies determine their preferences based on criteria such as data privacy, security and performance. This diversity allows every user to find the solution that best suits their needs.

Major Language Models that stand out in 2024

1. GPT and o1 series (OpenAI)

OpenAI’s GPT series, whose paid corporate products have reached one million users, continues to be the pioneer of the artificial intelligence world in 2024. In the past, the company’s GPT-4 and GPT-4-Turbo models could process not only text but also visual and audio content. GPT-4o, which the company introduced in May, not only provides simultaneous translation, but also solves mathematical problems over video like a private teacher and can read people’s emotions from their facial expressions. GPT-4o mini, which outperformed Google’s Gemini 1.5 Flash and Anthropic’s Haiku in many metrics, replaced GPT-3.5 Turbo in ChatGPT. As we explained in our article where we reviewed OpenAI’s thinking model series, OpenAI o1, OpenAI, as one of the biggest leaders in this market, takes artificial intelligence to a higher level every day.

These models are widely used by both individual users and businesses thanks to API integration. With a valuation of $157 billion in the past weeks 6.6 billion dollars investment alan OpenAIas part of its global expansion efforts New York, Seattle, Paris, Brussels ve Singapore announced that it will open new offices in many cities, including. The company, which came to the fore with the Apple and ChatGPT integration announced at WWDC 2024, recently introduced ChatGPT’s Windows-specific desktop application.

2. Gemini Series (Google)

Google’s Gemini model, which is based on Gmail’s smart response system as of September 2024, demonstrates strong performance in search engine optimization and chatbots. With improvements in 2024, the model can now process text and visual data simultaneously, making the user experience richer. Google announced in August that it had launched the Gems feature, similar to customizable GPTs. With this innovation, we can see Gemini’s multiple updates every month and its steps to improve the user experience. Some of these are voice usage mode and Google assistant integration. Additionally, while Google is making Gemini available to a wide range of developers via API, it is also expected to be integrated into Android Auto. Thanks to its user-friendly interface and integration into the Google ecosystem, it is preferred in data processing and customer services in different sectors. As we approach the completion of 2024, we have written many updates for Gemini. Finally, let us remind you that Gemini’s voice chat mode, Gemini Live, is available with Turkish language support.

3. Claude Serisi (Anthropic)

Anthropic’s Claude series, which exceeds $1 billion in mobile application revenues, offers a sensitive approach to artificial intelligence security and ethics. Claude models, which also offer the PRO feature with a paid subscription and can read a novel in less than a minute, are preferred especially in the customer service operations and sensitive data processing needs of large companies. Versions of the model in 2024 minimize users’ concerns about data security and compliance with ethical principles.

You can find our article last year comparing OpenAI’s GPT model and Claude here. In addition, Claude 3.5 Sonnet, which surpasses GPT-4o in many evaluations, offers extensible solutions thanks to API support. Thus, the model provides high performance in corporate areas. In this sense, Anthropic’s affordable batch processing option that challenges OpenAI Message Batches API’den It is worth mentioning.

4. Llama Serisi(Goal)

Meta’s Llama series, which was launched to challenge its competitors and can process images and text, has gained great popularity in research and development projects. Being open source provides flexibility to developers; This makes it possible to customize the model by retraining it. Llama offers an ideal solution especially for academic research and data science projects. Additionally, it offers flexible licensing options for commercial use, making it suitable for both individual users and small businesses.

The company introduced in the spring Call 3 modelsoutperforming similarly sized models like the Gemma, Gemini, Mistral 7B, and Claude 3 in select benchmark tests. Meta’s open AI model challenging GPT-4o and Claude 3.5 Sonnet Call 3.1 405B It is possible to say that it stands out with. The company has recently developed an open source artificial intelligence model that can process images and text. Llama 3.2 came to the fore. Where we compare Llama 3.2 to Phi 3.5 and Gemma 2 our comparison video you can review.

5. Command (Cohere)

Cohere’s Command model stands out for speed and accuracy in text-based tasks. This model, which is especially preferred in natural language processing (NLP) projects, can be easily integrated into applications and business processes thanks to API integration. Offering a wide range of customization to developers, Command provides high efficiency in tasks such as customer interactions, content creation and text analysis.

6. Falcon (Technology Innovation Institute)

Falcon is used especially in research projects with its open source structure. With the updates made in 2024, the accuracy of the model was increased by adding more parameters and deeper learning layers. This model is used especially in academic studies and projects that require high data processing capacities. Falcon’s open source nature allows it to be retrained on large data sets, enabling more customized and detailed analysis.

By the way, we should point out that Mistral, which we mentioned at the beginning of the article, stands out with different models. Large language model focused on coding tasks Codestraldeveloped in partnership with Nvidia and bringing corporate artificial intelligence to computers. Mistral-NeMo, Emphasis on code generation, mathematics and multilingual support Mistral Large 2 is one of the first things that come to mind. In the past months, the first multi-modal artificial intelligence model Pixtral 12BThe company that released ‘new artificial intelligence models that work on laptops and mobile devices. Ministerial 3B and Ministerial 8BHe introduced .

The process of selecting suitable models

If you are looking for a general-purpose model, OpenAI’s GPT models are one of the best options. It is especially ideal for wide-ranging uses such as customer service, content production and data analysis. If you prioritize safety and ethical values, the Claude model may be more suitable for you.

If you are looking for an open source solution, models such as Llama and Falcon offer great advantages in customizable projects by offering flexibility. The best example of this is Nvidia’s model that surpasses GPT-4o. Llama-3.1-Nemotron-70B-InstructWe see it in .

Finally, it should not be forgotten that language models are not limited to text only; multimodal data processing capabilities such as images, audio and even video are also becoming increasingly important.

The future of Large Language Models

The year 2024 stands out as a period of great progress in terms of language models (LLMs). Especially the rise of multimodal models offers more comprehensive and user-friendly solutions. However, small language models that can run on the device are also becoming increasingly popular. These models work on-device without the need for large cloud infrastructures, providing faster response times and superior privacy. Such models minimize privacy concerns because they enable user data to be processed locally and aim to make artificial intelligence applications easier to use in our daily lives.

These developments enable artificial intelligence companies to highlight small language models as a competitive strategy. Artificial intelligence models, especially those that can run on devices, promise more customization and efficiency, while also better managing the issue of data privacy.