25 of the best large language models in 2025

Large language models (LLMs) have become the driving force behind the remarkable growth of generative AI. Yet, their origins date back several years. The modern era of LLMs began in 2014 with the introduction of the attention mechanism, designed to emulate human cognitive attention, as outlined in a pivotal research paper. This set the stage for the transformative transformer model introduced in 2017. Since then, several sophisticated LLMs have been developed, shaping the landscape of AI and natural language processing.

In recent developments, many key players in the AI field have made significant strides. Notable models today include well-established names that paved the way for faster, more efficient language processing and understanding. Here, we explore some of the leading large language models in 2025, focusing on their capabilities and contributions to the AI landscape.

Starting with BERT, introduced by Google in 2018, this transformer-based model laid the foundation for various applications in understanding query-based language processing. With 342 million parameters, BERT has been finely tuned to execute tasks requiring natural language inference and sentence similarity. Its impact was evident as it improved the query understanding in Google searches during 2019.

Another significant name is Claude, developed by Anthropic. Claude prioritizes constitutional AI principles to ensure that generated outputs remain helpful, harmless, and accurate. The latest version, Claude 3.5 Sonnet, demonstrates superior understanding of humor, nuance, and complex instructions and has broadened its programming capabilities, enabling it to seamlessly integrate into application development.

Cohere, an enterprise AI platform, offers tailored LLMs like Command and Rerank. These models can be custom-trained according to specific business requirements, making them versatile tools for organizations seeking to harness AI for their unique use cases. Their development is credited to some of the pioneering authors involved in the creation of the attention mechanism.

DeepSeek-R1 is an open-source reasoning model excelling in complex reasoning and problem-solving. Using reinforcement learning techniques, it can conduct logical inferences and critical assessments of intricate problems, and it employs self-verification methods to refine its reasoning capabilities.

Baidu’s Ernie represents a significant advancement, with reports suggesting it has an impressive 10 trillion parameters. Released in August 2023, the Ernie chatbot has quickly gained traction, particularly in Mandarin. Its multilingual capabilities further enhance its utility in global applications.

Falcon, a family of models from the Technology Innovation Institute, provides powerful multilingual capabilities while being accessible as an open-source solution. The Falcon 2 model features an 11 billion parameter configuration, offering multimodal capabilities that can handle both text and visual inputs. Larger variants, such as Falcon 40B and Falcon 180B, expand its cognitive abilities significantly.

Google’s Gemini family replaces Palm in their chatbot applications, showcasing notable advancements in multimodal functions, from handling images and videos to text manipulation. Gemini is available in various sizes, each tailored for different applications, and has integrated seamlessly into Google’s suite of services.

Gemma, another significant entrant from Google, comprises open-source models trained using the same resources as Gemini. Released in mid-2024, it offers local deployment options, allowing users to run models on personal computers.

With more than 175 billion parameters, OpenAI’s GPT-3 has also remained a significant player since its launch in 2020. It laid the groundwork for subsequent models such as GPT-3.5 and GPT-4. Its evolution demonstrated an enormous leap in capacity and versatility, serving as the backbone for many AI-driven products, including ChatGPT.

The introduction of GPT-4 marked a major turning point in the evolution of LLMs, as it featured multimodal capabilities enabling image and language processing in tandem. GPT-4o further enhanced user interaction with a more human-like conversational ability, showcasing real-time engagement and emotional responsiveness.

The IBM Granite family of open models debuted as fully open-source solutions, enabling businesses to leverage its capabilities in customer service, IT automation, and cybersecurity—demonstrating the versatility of LLMs for enterprise solutions.

Conversely, Meta’s Llama series demonstrated continuous refinement, with recent iterations boasting high parameter counts and expanded capabilities, all available under open licenses. Notably, Llama has spawned derivative models such as Vicuna and Orca, enhancing the flexibility and accessibility of LLM technology.

OpenAI’s recent developments also include models like o1 and o3, focusing on enhanced reasoning abilities. These models exhibit advanced capabilities in the STEM fields, marking a significant evolution in how LLMs handle complex problem-solving tasks.

In summary, the landscape of large language models is rich and nuanced, comprised of an array of innovative technologies, each contributing uniquely to the ever-expanding field of AI and generative applications. As organizations strive to implement AI solutions into their workflows, LLMs will continue to be instrumental, shaping the future of communication, creativity, and cognitive function across multiple fields. As we look toward the future, the synergy between user-friendly interfaces and powerful LLM capabilities may unlock pathways to unprecedented advancements in machine learning and human interaction.

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