A study published in June 2025 found that pages cited by ChatGPT search were almost ** percent of the time ranked in positions *** on traditional organic search. A similar trend was also observed in Perplexity and in Google's own large language models (LLMs), which also mentioned pages that do not rank well in regular search results. On the other hand, pages that were ranked 1–5 in search results had the ************** rate of mentions of all the examined ones.

Introduction

Large Language Models (LLMs) Statistics: are sophisticated AI systems built on deep learning, particularly transformer-based architectures, designed to analyze and generate human-like text by identifying statistical patterns within vast datasets. Their core functionality is grounded in probability distributions, enabling precise language prediction and contextual comprehension.

Performance and efficiency are largely determined by metrics such as perplexity, cross-entropy loss, dataset scale, and the number of parameters. With some models incorporating hundreds of billions of parameters, LLMs require immense computational resources and advanced optimization strategies, while statistical benchmarks remain central for evaluating accuracy and coherence.

Beyond the technical scope, LLM statistics also capture adoption trends, enterprise integration, and productivity impacts, with growing attention directed toward transparency, fairness, and bias assessment to ensure responsible AI development.

In 2023, Claude 3 Opus was the large language model (LLM) tool that had the largest average worldwide, with an average total of ***** percent. Close behind, in second place, was Gemini 1.5 Pro with an average of about ** percent.

Introduction

LLM Chatbots Statistics: Large Language Model (LLM) chatbots are reshaping how people, organizations, and industries engage with technology. Equipped with the ability to process natural language, deliver human-like conversations, and leverage extensive datasets, these AI solutions are increasingly embedded in education, business functions, healthcare, customer engagement, and research.

Their widespread uptake is evident through a growing body of statistics that underscore both opportunities and challenges. From being integrated into academic programs and workplace tools to sparking debates around plagiarism, data privacy, and skills gaps, the numbers illustrate how profoundly LLM chatbots are influencing digital ecosystems. These insights not only track adoption trends but also reflect shifts in user behavior, regulatory landscapes, and the expanding role of AI in daily activities.

A June 2025 study found that ****** was the most frequently cited web domain by large language models (LLMs). The platform was referenced in approximately ** percent of the analyzed cases, likely due to the content licensing agreement between Google and Reddit in early 2024 for the purpose of AI models training. ********* ranked second, being mentioned in roughly ** percent of the times, while ****** and ******* were mentioned ** percent.

As of March 2024, OpenAI o1 was the large language model (LLM) tool that had the best benchmark score in solving math problems, with a score of **** percent. Close behind, in second place, was OpenAI o1-mini, followed by GPT-4o.

OpenAI remains the most heavily funded large language model (LLM) developer worldwide in 2023 with ** billion U.S. dollars in funding. Its most well-known product, ChatGPT, launched something of a generative AI revolution in late 2022. It's backing by Microsoft has made OpenAI a leading champion in the LLM competition, though competitors like Anthropic, funded by Google, are closing the gap.

Introduction

The global enterprise LLM market is undergoing rapid expansion, supported by the accelerating adoption of large language models in business operations, knowledge management, and customer engagement. In 2024, the market was valued at approximately USD 4,500.1 million, and it is projected to reach nearly USD 58,324 million by 2034, reflecting a strong CAGR of 29.2% between 2025 and 2034. The growth is being fueled by the ability of LLMs to streamline workflows, enhance decision-making, and reduce costs through automation and intelligent data processing across industries.

The Enterprise Large Language Model (LLM) market refers to the use of advanced AI language models tailored to meet the needs of businesses and organizations. These models help automate complex workflows, enhance decision-making, and improve customer interactions by processing vast amounts of data and generating human-like text. Enterprises use LLM technology to unlock value from unstructured data across diverse functions such as customer service, data analysis, and content creation.

A major driving factor behind the growth of the Enterprise LLM market is the increasing demand for intelligent automation across business processes. Organizations actively seek to deploy LLM-powered tools like chatbots and virtual assistants that deliver personalized, 24/7 customer service, reduce operational costs, and allow employees to focus on more strategic tasks. Furthermore, LLMs' ability to extract insights from large volumes of unstructured data accelerates data-driven decision-making, offering enterprises a competitive edge.

https://market.us/wp-content/uploads/2025/09/Enterprise-LLM-Market-size.png" alt="Enterprise LLM Market size" width="1216" height="706">

Background:The infodemic we are experiencing with AI related publications in healthcare is unparalleled. The excitement and fear surrounding the adoption of rapidly evolving AI in healthcare applications pose a real challenge. Collaborative learning from published research is one of the best ways to understand the associated opportunities and challenges in the field. To gain a deep understanding of recent developments in this field, we have conducted a quantitative and qualitative review of AI in healthcare research articles published in 2023.Methods:We performed a PubMed search using the terms, “machine learning” or “artificial intelligence” and “2023”, restricted to English language and human subject research as of December 31, 2023 on January 1, 2024. Utilizing a Deep Learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics. Similarly, we performed a search for Large Language Model(LLM) based publications for the year 2023.Results:Our PubMed search yielded 23,306 articles, of which 1,612 were classified as mature. Following exclusions, 1,226 articles were selected for final analysis. Among these, the highest number of articles originated from the Imaging specialty (483), followed by Gastroenterology (86), and Ophthalmology (78). Analysis of data types revealed that image data was predominant, utilized in 75.2% of publications, followed by tabular data (12.9%) and text data (11.6%). Deep Learning models were extensively employed, constituting 59.8% of the models used. For the LLM related publications,after exclusions, 584 publications were finally classified into the 26 different healthcare specialties and used for further analysis. The utilization of Large Language Models (LLMs), is highest in general healthcare specialties, at 20.1%, followed by surgery at 8.5%.Conclusion:Image based healthcare specialities such as Radiology, Gastroenterology and Cardiology have dominated the landscape of AI in healthcare research for years. In the future, we are likely to see other healthcare specialties including the education and administrative areas of healthcare be driven by the LLMs and possibly multimodal models in the next era of AI in healthcare research and publications.Data Files Description:Here, we are providing two data files. The first file, named FinalData_2023_YIR, contains 1267 rows with columns including 'DOI', 'Title', 'Abstract', 'Author Name', 'Author Address', 'Specialty', 'Data type', 'Model type', and 'Systematic Reviews'. The columns 'Specialty', 'Data type', 'Model type', and 'Systematic Reviews' were manually annotated by the BrainX AI research team. The second file, named Final_LLM_2023_YIR, consists of 584 rows and columns including 'DOI', 'Title', 'Abstract', 'Author Name', 'Author Address', 'Journal', and 'Specialty'. Here, the 'Specialty' column was also manually annotated by the BrainX AI Research Team.

This dataset was created to provide comparative statistics on various general-purpose and pre-trained financial large language models (LLMs) based on their financial capabilities. We also emphasize on which specific characteristics contributed to their financial capacity. We also provide data on whether the models were open- or closed-sourced. Our intent was to generate a categorical heatmap from the CSV data.

Comparison of Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better by Model

Combined Medical Vision-Language Dataset

  Dataset Description

Comprehensive medical vision-language dataset with 4793 samples for vision-based LLM training.

  Dataset Statistics

Total Samples: 4793 Training Samples: 3834 Validation Samples: 959

  Modality Distribution

X-ray: 2325 samples CT: 1351 samples Unknown: 812 samples MRI: 231 samples Ultrasound: 70 samples Microscopy: 2 samples Endoscopy: 2 samples

  Body Part Distribution

Unknown:… See the full description on the dataset page: https://huggingface.co/datasets/robailleo/medical-vision-llm-dataset.

As of 2024, over **** the global firms planned to use LLMs (LLama and LLama-like models), while ** percent chose to use embedding models (BERT and family) in their commercial deployment. Additionally, only ***** percent planned to utilize multi-modal models.

The LLM (Large Language Model) Penetration Testing Services market is experiencing significant growth as businesses increasingly recognize the importance of cybersecurity in today's digital landscape. With the rapid evolution of threats targeting sensitive data and systems, organizations are turning to LLM-powered s

A curated dataset featuring system prompts sourced from major Large Language Model (LLM) providers such as OpenAI, Google, Anthropic, and more. This collection is designed to support research, benchmark, and innovation in prompt engineering by offering a diverse range of real-world prompts used to guide and control state-of-the-art language models.

Comprehensive comparison of Latency (Time to First Token) vs. Output Speed (Output Tokens per Second) by Model

Introduction

The Global Large Language Model (LLM) Market is projected to reach USD 82.1 billion by 2033, rising from USD 4.5 billion in 2023, at a CAGR of 33.7% between 2024 and 2033. The market’s growth is driven by the increasing adoption of generative AI systems for automation, analytics, and software development. GPT-4, capable of processing up to 1 million tokens, exemplifies this versatility, supporting applications from customer service automation to code generation.

Adoption of LLMs is expanding rapidly, with 67% of organizations now deploying generative AI products powered by these models. In financial services, for instance, 60% of Bank of America’s clients leverage LLM-based solutions for investment and retirement planning, reflecting the growing integration of AI into core business functions.

This trend underscores LLMs’ transformative impact on decision-making and operational efficiency across industries. Moreover, smaller models like Microsoft’s PHI-2 with 2.7 billion parameters are gaining traction by outperforming larger counterparts such as Llama-2 in coding tasks, demonstrating that optimized models can deliver high performance with greater efficiency and lower computational demands.

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The Large Language Model (LLM) market is growing rapidly as these advanced AI models become essential tools for various industries. LLMs can understand and generate human-like text, helping businesses automate tasks like content creation, customer support, and data analysis. This technology is transforming sectors including banking, healthcare, and IT, where the need for natural language understanding is ever-increasing. The steady rise in digital content and demand for efficient language processing solutions are key reasons behind this surge, with over 30% growth rates reported recently. As organizations seek more personalized and scalable digital experiences, LLMs are becoming core to their transformation efforts.

Several top driving factors fuel the LLM market’s expansion. One major factor is the ongoing improvements in machine learning algorithms and computational power, which allow models to handle larger datasets and deliver more precise results. The growing availability of vast datasets for training contributes significantly to better model accuracy. Another crucial element is the escalating demand for automation across business functions to enhance operational efficiency and reduce human error. Additionally, industries are pushing for more domain-specific language models that can deliver expert-level knowledge in specialized fields, supporting higher adoption rates.

The Large Language Model (LLM) market has emerged as a transformative force across various industries, redefining the way organizations leverage text-based data to enhance operations, improve customer engagement, and drive innovation. With the capacity to understand and generate human-like text, LLMs span a wide arr

This is the statistic result of Emo-LLMs corpus, which analyses 60 publications related to LLM-assisted emotion research. The corpus systematically analysed their strategies, technologies, data, and utilization of traditional affective models.

a collection of 2 datasets used in the article 'LLaMA 3 vs. State-of-the-Art LLMs: Performance in Detecting Nuanced Fake News' Description: English Dataset Structure: The English dataset consists of 100 entries, each containing eight columns. These columns include:

Category (Categorie): The classification of the news/claim into one of four categories—True, False, Mostly True, and Mostly False. News/Claim: The textual content of the claim or news item being evaluated. Model Outputs: Predictions from various models, including "ChatGPT 4", "ChatGPT 4.o", "Gemini", "Llama 2-13b", "Llama 3.1-8B base", and "Llama 3.1-8B fine tuned." Data Types: All columns in this dataset are of type object (string), reflecting the categorical nature of the data.

Categories: The dataset contains four unique categories, with each category represented equally (25 entries per category).

Model Performance: Each model provides a categorical prediction (e.g., True, False) for each claim.

Romanian Dataset Structure: Similar to the English dataset, the Romanian dataset also contains 100 entries with eight columns:

Category (Categorie): The classification of the news/claim into one of four categories—Adevărat (True), Fals (False), Parțial Adevărat (Mostly True), and Trunchiat (Mostly False). News/Claim: The textual content of the claim or news item in Romanian. Model Outputs: Predictions from various models, including "ChatGPT 4", "ChatGPT 4.o", "Gemini", "Llama 2-13b", "Llama 3.0-8B base", and "Llama 3.0-8B fine tuned." Data Types: All columns are of type object (string), indicating categorical data.

Categories: The dataset includes four unique categories, with a balanced distribution of 25 entries per category.

Model Performance: Similar to the English dataset, each model provides a categorical prediction for each claim.

Key Observations: Both datasets have an identical structure and are balanced in terms of category distribution, making them suitable for comparative analysis of model performance across different languages. The datasets allow for the evaluation of model consistency and accuracy in classifying news/claims into nuanced categories, particularly in a bilingual context.