Recent trends in LLMs for 2025. Chapter 2

Chapter 2: Overview of Language Models by 2025

2.1 Introduction

Large Language Models (LLMs) have experienced explosive growth in capacity, technical sophistication, and functional variety since the emergence of the first large Transformer-based models in 2017. By 2025, the LLM ecosystem is characterized by an unprecedented diversity of architectures, sizes, multimodal capabilities, and both proprietary and open-source approaches. This chapter offers a detailed analysis of the main current and emerging models, their outstanding technical features, and their relative position in the field.

2.2 Core Architecture and Key Components

LLMs are mostly based on Transformer architectures, designed to efficiently process text sequences and understand extensive contexts. By 2025, architectures have evolved to include substantial improvements such as enhanced attention mechanisms, specialized layers, and computation optimizations that maximize performance and efficiency during training and inference.

The main layers composing a typical LLM include:

• Input embeddings: where tokenized text is converted into representative numerical vectors.

• Positional encoding: which allows the model to understand the sequence and order of words.

• Multi-head self-attention mechanisms: the core that enables the model to focus on different parts of the context simultaneously.

• Feed-forward layers and nonlinear transformations with normalization and residual connections that stabilize and improve learning.

These components form deeply stacked blocks, configuring models that, in the case of the most advanced versions, can contain billions or even trillions of adjustable parameters, granting them great representational capacity. (AppyPie), (GeeksforGeeks)

2.3 Main LLM Models in 2025

2.3.1 GPT-4o and its variants (OpenAI)
GPT-4o is the latest evolution of OpenAI’s GPT series and represents one of the leading multimodal models in the market by 2025. Officially released in May 2024, GPT-4o stands out for:

• Multimodal capacity combining text, image, and audio, allowing enriched interactions and more complex conversation contexts.

• Support for extremely long context processing, with versions like GPT-4.1 enabling the handling of up to one million tokens simultaneously.

• Advanced real-time inference capabilities that enable applications in conversational chatbots, voice assistants, creative generation, and automatic programming.

• Improvements in precise generation and faithful following of complex instructions, as well as an integrated knowledge base that updates context in real time. (TechTarget), (OpenAI)

2.3.2 LLaMA 3 and LLaMA 3.2 Vision (Meta)

Meta developed the LLaMA series, with version 3 consolidating in 2025 as one of the most prominent open-source models. This model is distinguished by:

• Availability in multiple sizes (e.g., 11 billion to 90 billion parameters), facilitating adaptation to different hardware requirements.

• Multimodal capabilities in version 3.2 Vision, which integrates both text and image processing with competitive performance in classification and visual-text generation tasks.

• A focus on democratizing access to powerful LLMs through less restrictive licenses and an active developer community.

• Wide support for integration tools enabling customization via fine-tuning and plugins. (Bentoml)

2.3.3 Google Gemini 2.0 and variants.

Google launched its Gemini line, which has become a benchmark in multimodal LLMs by 2025. Outstanding features:

• Focus on multimodality, integrating text, image, and in some versions video and audio.

• Use of innovative mixture-of-experts architectures to balance efficiency and capacity.

• Optimized design for integration with cloud services and platforms like Google Workspace.

• Gemini 2.0 offers improvements in multilingual tasks, reasoning, and code generation compared to previous versions. (Analytics Vidhya)

2.3.4 Grok 3 (xAI - Elon Musk)

Grok 3 represents xAI’s bet, led by Elon Musk, in the area of multimodal LLMs:

• Optimized for conversational text generation and real-time analysis with integration for social platforms.

• Novel approaches in energy efficiency and latency reduction.

• Designed to interact with external APIs and reinforce contextualized generation based on recent data.

• Strong commitment to AI ethics and model transparency. (Analytics Vidhya)

2.3.5 Qwen 2.5-Max (Alibaba)

Qwen 2.5-Max is part of the LLM ecosystem developed by Alibaba, featuring:

• Strong presence in the Asian market and growing international adoption.

• Capabilities in multimodality and high-fidelity handling of Asian languages.

• Integration with Alibaba’s commercial systems and enterprise platforms.

• Notable optimization for e-commerce and corporate applications requiring analysis of large volumes of text and structured data. (Analytics Vidhya)

2.3.6 Claude 3 (Anthropic)

Anthropic developed Claude 3, distinguished by:

• Focus on safety, ethics, and alignment to minimize biases and inappropriate responses.

• Capabilities for natural dialogue and complex reasoning via reinforcement learning.

• Models designed for corporate environments requiring high reliability and strict controls.

• Specific functionalities for customer service, report generation, and legal processes. (ArtificialAnalysis)

2.3.7 Mistral and others

Among emerging models, Mistral stands out, an open-source project that has gained relevance for its efficient architecture and strong performance in recent benchmarks, alongside other models exploring hybrid architectures and sparsity techniques. (ArtificialAnalysis), (Medium)

2.4 Model Classification: Proprietary, Open, and Open Source

Proprietary Models: Developed and controlled by large corporations such as OpenAI (GPT-4o), Google (Gemini), and Anthropic (Claude). They generally offer access via commercial APIs and guarantee support but limit modifications and inspection of their code or training data.

Open Models: Some LLMs are released under open or semi-open licenses, allowing use and fine-tuning under certain conditions, for example LLaMA and Mistral variants. This fosters research and accessibility but requires technical resources for deployment.

Open Source Models: Driven by the technical community, with full distribution of code and weights, for example some derivative projects based on OpenLLM or collaborative platforms. These promote transparency, innovation, and extreme customization, although they may have practical limitations in resources and scale.

2.5 Multilingual and Multimodal Capabilities

2025 sees robust integration of multilingual capabilities into a single model, leaving behind the need for language-separated models. This includes languages with diverse alphabets and specific grammatical structures (e.g., Chinese, Arabic, indigenous languages).

In parallel, multimodality has been consolidated: combining text with images, audio, video, and even sensory signals is routine in recent LLMs. For example, GPT-4o allows understanding and generating content based on images and voice, while LLaMA 3.2 Vision extends these capabilities with a specialized focus on integrated computer vision. (NVIDIA), (OpenAI)

This global overview lays the groundwork for understanding the technical innovations and practical applications that we will see in later chapters, delving deeper into training techniques, optimization, data integration, as well as use cases and emerging challenges.