A detailed guide to large language models: ChatGPT, Claude, Gemini, LLama
Large Language Models (LLMs) have become the foundation of machine learning, transforming the way we work with information. ChatGPT, Claude, Gemini, and other systems deliver state-of-the-art results in generating and analyzing text, processing data, and solving complex tasks.
In this article we take a detailed look at the leading language models of 2024, their capabilities, architectural features, and areas of application. Our analysis will help you understand the strengths and limitations of each system, which is critical for choosing the right technology.
It's worth noting that the large language model market is evolving extremely fast. Just three years ago, systems like these were only available in research labs; today they are actively used across industry, education, and everyday life. Over the past year alone we've seen a qualitative leap both in model capabilities and in their accessibility to users.
How language models work
Large language models are built on advanced neural network architectures trained on massive text corpora. At their core lies the transformer mechanism — a special neural network architecture that efficiently processes sequences of data.
The training process
Training a neural network involves several stages. First comes pretraining on publicly available text: books, articles, web pages, and other sources. At this stage the model learns to recognize language patterns and the fundamental relationships between words and concepts. The size of the training data can reach trillions of tokens (the basic units of text the model analyzes).
After pretraining, the model goes through a fine-tuning stage. This stage uses specially prepared datasets that help the model handle specific tasks better and meet defined quality and ethical standards. Many modern neural networks also go through reinforcement learning from human feedback (RLHF), which helps them generate more useful and safer responses.
Model size is measured in the number of parameters — the tunable values within the neural network. Modern systems can have anywhere from a few billion to hundreds of billions of parameters. A larger size usually allows the network to better understand context and generate higher-quality responses, but it also requires more computing power.
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How do language models recognize information?
Context window
An important aspect of how neural networks work is the context window — the maximum amount of information the model can analyze at once. The size of this perception window determines how large a text the model can analyze and how effectively it retains context in long conversations. Modern models have context windows ranging from a few thousand to hundreds of thousands of tokens.
Tokenization
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Tokenization is a particularly important process — splitting text into the smallest processing units. A token can be a whole word, part of a word, or a single character. Effective tokenization allows the network to better understand the structure of language and use computational resources more efficiently.
Modern models also use various optimization techniques to improve performance:
- Quantization — reducing the precision of model weights to lower memory requirements
- Sparse attention — optimizing the attention mechanism for working with long sequences
- Caching — storing intermediate results to speed up processing of repeated requests
- Distributed computing — parallel data processing across multiple devices
Model architecture
The attention-based model architecture (e.g., the transformer) plays a key role. Unlike earlier approaches based on recurrent neural networks, transformers can process the entire input text in parallel. This not only speeds up processing but also allows the model to better capture dependencies between distant parts of the text.
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To improve efficiency, modern models often use a Mixture of Experts (MoE) approach. With this approach, the neural network consists of many specialized sub-networks ("experts"), each responsible for a specific type of task or area of knowledge. A special gating network routes input data to the most suitable experts.
This makes it possible to significantly increase the overall size of the model without a proportional increase in computational costs. The self-attention mechanism also deserves special attention — it allows the model to evaluate the importance of different parts of the input text relative to one another. Thanks to this, the network can:
- Understand references to previously mentioned objects
- Account for grammatical dependencies
- Correctly interpret context-dependent words
- Highlight key elements in long texts
What else goes into developing and training models?
Development also relies on sophisticated regularization and normalization methods to prevent overfitting and improve generalization. Layer normalization helps stabilize training of deep networks, while various forms of regularization, such as dropout and weight decay, prevent the model from overfitting to the training data.
Multi-task learning plays a special role in modern neural networks. Models are trained simultaneously on several related tasks, which helps them form more universal internal representations. For example, a model might learn in parallel to:
- Predict the next word in a text
- Determine the sentiment of a statement
- Classify texts by topic
- Answer questions about a text
Various techniques are used during training to improve efficiency:
- Gradient accumulation for working with large batches on limited memory
- Mixed-precision computation to speed up training
- Parallel training across multiple GPUs
- Dynamic adjustment of batch size and learning rate
The final stage of model development includes extensive testing on various datasets and benchmarks to evaluate:
- Language ability
- Logical reasoning
- Mathematical skills
- Knowledge across different domains
- Ability to follow instructions
- Safety and ethics of responses
To better understand how modern neural networks work and what sets them apart, let's dive into a detailed examination. Below we look at each of them — ChatGPT, Claude, Gemini, and LLaMA — and break down their unique features to help you make an informed choice.
We recommend: GPTunneL's prompt engineering guide — Guide to working with neural networks from GPTunneL
ChatGPT (OpenAI)
The development of the ChatGPT family of large language models began with GPT-3, introduced in June 2020. This development revolutionized the field of artificial intelligence and laid the groundwork for modern neural networks. Since then, OpenAI has developed a substantial number of advanced solutions. Below are the current and most effective ones.
GPT-4 (2023)
GPT-4's architecture enables deeper analysis of information and the ability to produce precise, substantive responses. The model also has multimodal capabilities, allowing it to work with both text and images. Its technical architecture includes over 175 billion parameters and a context window of up to 128,000 tokens. This lets the system process large texts and hold continuous conversations without losing context.
Key features of ChatGPT-4 include:
- The ability to handle complex reasoning and multi-step tasks
- High accuracy when working with factual information
- The ability to fine-tune for specific tasks
- Broad integration with various services via API
The model also stands out for its robust safety systems and ethical safeguards. OpenAI implemented a multi-layered content filtering system and mechanisms to prevent misuse. The model is trained to avoid generating harmful content and to adhere to ethical principles in its responses.
GPT-4o (2024)
In 2024, OpenAI released a major update to its flagship model. The current lineup includes three main versions: GPT-4o, GPT-4o1, and GPT-4o1-mini, each optimized for different use cases.
GPT-4o is the most powerful version of the model. It features improved reasoning ability, deeper contextual understanding, and the ability to work with various types of data. The network shows impressive results in areas such as:
- Analyzing complex academic texts
- Solving multi-step logical problems
- Programming in various languages
- Working with visual information
O1 (2024)
ChatGPT-o1 combines high performance with improved efficiency. This network delivers faster responses and requires fewer computational resources while retaining most of the capabilities of the base version. Key improvements include:
- Reduced response time
- Improved context handling
- More stable generation of long texts
- Improved accuracy in specialized areas such as math, coding, and reasoning
O1-Mini (2024)
ChatGPT o1-mini is a lightweight version of the model. Despite its smaller size, it retains the high quality of core functions and adds new capabilities:
- Local data processing without a network connection
- Integration with mobile and embedded devices
- Lower power consumption
- Improved data privacy
In practical use, ChatGPT demonstrates exceptional versatility. The network handles a wide range of tasks effectively: from writing and analyzing text to helping with programming and mathematical calculations. In the corporate sector, ChatGPT is used to automate customer support, generate content, and analyze data.
O1-PRO
O1 Pro is an expanded version of O1, designed to handle the most demanding tasks. The PRO version uses more computational resources, delivering more accurate and reliable results. It significantly outperforms regular O1 and O1-preview in areas such as data analysis, programming, and document processing, showing stronger results on math, science, and coding benchmarks.
To more precisely evaluate O1 Pro's abilities, developers applied a strict criterion: the model is considered to have succeeded at a task only if it gives the correct answer in four out of four attempts. This approach highlights the high reliability and accuracy of its results, making O1 Pro an ideal solution for professionals who use cutting-edge AI technology daily and need high performance for complex tasks.
O3
In December 2024, OpenAI announced the o3 model, the successor to o1, focused on improved reasoning capabilities. The model is trained to use a "private chain of reasoning," allowing it to break complex tasks into steps and think through answers carefully, improving accuracy and reducing the likelihood of errors. o3 is available in two versions: the standard version and o3-mini, a lighter and faster version designed for resource-constrained tasks. o3-mini is expected to become available to the general public in January 2025.
Claude (Anthropic)
Claude's development history shows a unique approach to building neural networks, where ethical principles and safety are an integral part of the architecture rather than an afterthought. Let's look at the current models.
Claude 3.5 Sonnet
In late 2023, Anthropic released Claude 3.5 Sonnet, an important step forward in the model's capabilities. This version significantly improved the system in several key areas:
The first version of Claude 3.5 Sonnet featured improved natural language processing and a deeper understanding of context. The network learned to better pick up on nuances of human speech and interpret complex requests more accurately. Improvements were especially noticeable when working with technical text and code.
Claude 3.5 Sonnet (New)
In the fall of 2024, an updated version of Claude 3.5 Sonnet was released, introducing new functional capabilities. The system gained an improved ability for analytical reasoning and processing large volumes of text data. An important innovation was an integrated verification system that allows the system to carefully check the content it generates.
Claude 3.5 Haiku
Alongside Claude 3.5 Sonnet, Anthropic released Claude 3.5 Haiku — a compact version of the system designed for fast query processing. This version retained the key advantages of Claude's architecture but ran significantly faster thanks to:
- Optimized text-processing algorithms
- A smaller model size while maintaining response quality
- An improved caching system for frequently used data
- More efficient use of computational resources
Claude 3.5 Haiku stands out among Anthropic's other large language models for its performance. According to evaluation results, it outperforms Claude 3 Opus on many metrics while offering higher processing speed. Key achievements include:
- Improved results in development and engineering tasks
- More reliable and predictable performance
- Lower operating costs (roughly 10 times lower than Claude 3 Opus)
- Optimized use of computational resources
The current version of Claude handles a wide range of tasks:
- Comprehensive, context-aware document analysis
- Creating and editing complex texts
- Writing code and technical documentation
- Scientific analysis and information review
- Customer service and information support
Gemini (Google)
The development of Gemini was a major milestone in Google's AI efforts. It's the company's first truly multimodal model, built from the ground up to handle multiple types of information at once. Unlike previous efforts, Gemini was designed from day one to work simultaneously with text, code, audio, video, and images.
Gemini 1.5 Pro
The current Gemini 1.5 family comes in several versions, each optimized for specific use cases. The flagship model, Gemini 1.5 Pro, was a breakthrough thanks to its unprecedented context window of 1 million tokens — roughly equivalent to 700,000 words.
This allows the system to analyze large documents in a single pass, significantly expanding its practical applications.
Gemini 1.5 Flash
Gemini 1.5 Flash deserves special mention as a version optimized for fast query processing. Google's engineers managed to cut response time by 50% compared to previous versions while maintaining high response quality. This is especially important for real-world applications where speed is critical.
Gemini Nano
For mobile devices, Google developed a compact version called Gemini Nano, available in two variants with 1.8 and 3.25 billion parameters respectively. Despite their significantly smaller size, these models can run efficiently directly on users' devices, ensuring data privacy and low response latency.
At the core of Gemini's architecture is an advanced Mixture of Experts (MoE) implementation. This system uses more than a thousand specialized expert sub-networks with dynamic query routing, which significantly increases the model's effective capacity without a proportional increase in computational cost. This approach not only enables more accurate handling of complex queries but also reduces power consumption for routine tasks.
Gemini 2.0 Flash
Gemini 2.0 Flash is a powerful, high-performance model. It includes a number of improvements, including native tool use and, for the first time in the Gemini family, the ability to generate images and speech directly during processing. These innovations significantly expand the ways the model can be used, making it more flexible and versatile.
The network supports a wide range of input types, including text, images, video, and audio, and can output results as text, images, and speech. It supports a context window of up to 1 million input tokens and 8,000 output tokens. The model is optimized for scenarios where response speed is critical, such as task automation.
Practical use
Google is actively integrating Gemini's capabilities into its products, from the Gmail email service to the Google Workspace office suite. The model improves document handling, helps draft emails, automates presentation creation, and expands data-analysis capabilities in spreadsheets.
Special attention has been given to the system's language capabilities. Gemini supports more than 170 languages and shows a deep understanding of context in multilingual documents. The model can not only translate text accurately while preserving nuance, but also account for dialects and regional language variations.
Llama (Meta)
Meta radically updated its family of neural networks, introducing Llama 3 in early 2024. The new version significantly outperforms its predecessors and includes three main variants: Llama 3 8B, Llama 3 70B, and Llama 3 405B.
A key difference in Llama 3 is a fundamentally new transformer architecture optimized for working with large volumes of data. The model was trained on 15 trillion tokens — significantly more data than previous versions. Training used specialized GPU clusters with 24,000 graphics processing units, which made it possible to significantly improve the model's quality.
Llama 3 70B delivers impressive results across various benchmarks, including MMLU, ARC, and DROP, often outperforming other large language models. An important improvement was the increased context window of up to 128,000 tokens, which lets the model efficiently handle long texts and complex conversations.
Special attention in the new version was given to reducing bias and improving safety. Meta introduced a dynamic knowledge-updating system that helps the model keep information current and minimize factual errors.
Llama 3 is actively used across Meta's products, including Facebook, Instagram, and WhatsApp, where it powers various AI features. Thanks to its open license, the model has also become the foundation for a wide range of third-party projects and specialized solutions.
Notable technical features of Llama 3 include:
- Improved handling of multilingual content
- Higher accuracy on programming tasks
- Lower response latency
- More efficient use of computational resources
Unlike closed models, Llama 3 gives developers full access to the source code and model weights, making it possible to create specialized versions for specific tasks. This openness has contributed to a thriving ecosystem of tools and applications built on Llama.
The model performs especially well on tasks requiring deep contextual understanding and complex reasoning. At the same time, the 8B version delivers impressive performance even on relatively modest hardware, making AI technology more accessible to a wider range of developers.
The future of language models
The development of large language models is moving in several directions at once, any of which could radically reshape the AI landscape in the coming years.
Multimodality and the integration of different data types
The next generation of models will integrate different types of information even more deeply. While today's multimodal models, such as GPT-o1 or Gemini 1.5, mostly handle text, audio, and images separately, future systems will be able to simultaneously process video, audio, 3D models, and real-time sensor data. This will open up new possibilities for applying AI in robotics, medicine, and industry.
On-device computing and privacy
There's a clear trend toward developing efficient models that can run directly on users' devices. Apple and Microsoft are embedding built-in AI features into their products, Google continues to develop the Gemini lineup, and Meta is actively promoting lightweight versions of Llama. This approach not only provides better privacy protection but also reduces latency when processing requests.
Specialization and adaptability
Instead of general-purpose models, the future may belong to systems that dynamically adapt to specific tasks and domains. The Mixture of Experts technology used in Gemini represents a first step in this direction. Looking ahead, we may see models capable of automatically restructuring their architecture depending on context and requirements.
Ethics and safety
Developing systems for ethical oversight and safety is becoming a priority. Companies are actively working on models that can not only follow set rules but also independently assess the ethical consequences of their actions. The Constitutional AI approach, first applied in Claude, is likely to see further development and become an industry standard.
Improving efficiency
Significant progress is expected in model optimization. In the future, new methods that simplify neural networks (for example, by removing redundant connections or reducing numerical precision) will make it possible to substantially lower computational requirements without degrading performance. This is especially important given the growing concerns around data center energy consumption and the need to make AI systems more environmentally sustainable.
Expanding context and long-term memory
The trend toward larger context windows will continue, but a more meaningful improvement will be in how models work with information qualitatively. Future models will not just process large volumes of text — they will efficiently structure knowledge, form long-term connections, and update their knowledge base without full retraining.
Conclusion
2024 marked a period of qualitative leaps in the development of these technologies. We're seeing not just improvements to existing capabilities, but the emergence of fundamentally new approaches to processing information. Context windows growing to a million tokens, the evolution of Mixture of Experts architectures, the rise of efficient on-device models — all of this is shaping a new reality where large language models and AI are becoming accessible and applicable in virtually every field.
The trend toward democratizing this technology is especially significant. While building and using language models used to be the domain of only large tech companies, today, thanks to open models and cloud services, developers of any skill level can integrate AI into their projects.
The future of large language models lies in their even deeper integration into everyday life, with the focus shifting from general-purpose solutions to specialized tools optimized for specific tasks and use cases. Advances in on-device computing and improved model efficiency will make AI accessible even on personal devices, opening up new use cases.