App Development 29 May, 2025.
The Ultimate List of Large Language Models: Top 50 Picks for Businesses
30 May, 2025
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14 min read
According to StartUs Insights, over 2,400 funding rounds have been closed in the LLM sector, with an average investment of $32.5 million per round. Key investors include NVIDIA, Microsoft, and Coatue Management, collectively contributing over $5 billion.
Within the last decade or two, large language models (LLMs) have made their mark on the industries. With their models, the way AI systems comprehend and generate human language has undergone drastic changes. From natural language processing (NLP), customer service automation, and content creation, LLMs form the basis of a multitude of AI applications. As their influence continues to grow, exploring a list of large language models offers insight into the technology shaping our future.
The question is, what are LLMs, and how do they operate so that they are deemed essential in the terrain of AI? In this blog, we will provide deep insights into some of the largest language models built so far.
Read More: Best Open Source LLMs for Code Generation
What is LLM?
The definition of large language models needs to be introduced before we go on to the list of our favorites. A Large Language Model is a smart computer AI designed to build and generate human language. These models are formed with the help of deep learning algorithms and massive datasets for training.
With this training, the LLMs learn about anything and everything, including the context, syntax, and semantics of text. The word “large” implies that its size typically encompasses billions or even trillions of parameters, which are what the model modifies during training to better predict output.
Let’s quickly have a list of large language models. These LLMs represent the advanced frontier of artificial intelligence and are reshaping how we interact with technology.
Read More: How to Build an LLM Like DeepSeek?
The Ultimate List of Large Language Models
1. GPT-4 by OpenAI
Size: Estimated 170 trillion parameters
Overview: The first in list of large language model is OpenAI’s GPT-4. OpenAI’s GPT-4is Trending toward being the most Sophisticated LLM AI model. It generates human-like text from a vast data set containing books, articles, websites, and various other forms of textual data. From writing a creative piece or solving a convoluted task, it generates code. The colossal mass makes it among the largest operational language models.
Applications: Applications range from chatbots, virtual assistants, content-generating applications, and code completion.
Read More: DeepSeek vs ChatGPT – How Do These LLMs Compare
2. PaLM by Google
Size: 540 billion parameters.
Overview: Another large player in the area of LLMS is Google’s Pathways Language Model (PaLM). PaLM is intended for multi-task learning; different types of tasks can be performed with one single model. Moreover, it was trained on enormous datasets to generate high-quality artifacts, translate multiple languages, and answer complex questions.
Applications: PaLM is used both in Google search and in its translation and creation of digital content tools; thus, it encompasses a versatile AI covering many domains.
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3. BERT by Google
Size: 340 million parameters (base model)
Overview: BERT, or Bidirectional Encoder Representations from Transformers, is Google’s answer to not getting to the scale of GPT-4 or PaLM but still representing the most important LLMs regarding understanding the context of words in search queries. Due to its bi-directional nature, it can apprehend the meanings of words fully in both ways, thus managing very well even for NLP tasks such as question answering and text classification.
Application: BERT supports Google Search and is thus available in the given applications requiring a deep understanding of language.
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4. LaMDA by Google
Size: Unknown (rumored to be larger than PaLM)
Overview: LaMDA is another powerful addition to the list of large language models, developed by Google. It is optimized for dialogue applications, and it was developed to improve dialogue in conversational AI applications by producing more natural, coherent responses during interaction. LaMDA is capable of holding the context of conversation over time and has therefore shown useful applications in virtual assistants and chatbots.
Applications: LaMDA now powers Google Assistant and foreseeable conversational AI, enhancing users’ experience with more fluid and natural interactions.
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5. Megatron NLG by Google
Size: 530 billion parameters
Overview: Megatron-Turing NLG (Natural Language Generation) emerges at the back of joint efforts made by Microsoft and NVIDIA, especially to generate high-quality text, answer complex questions, and be used for a variety of AI tasks that require an understanding of language. This model was designed for applications ranging from chatbots to content creation and can analyze even the most complex details and nuances of language.
Application: Used in customer service, enterprise solutions, and research purposes Megatron-Turing NLG will thereby allow companies to introduce high-level NLP practice into their business workflows.
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6. T5 from Google
Size: 11 billion parameters (the largest model)
Overview: The Text-to-Text Transfer Transformer, or T5, processes tasks fairly related to other common NLP tasks because it is understood as going from text to text. It can translate from one language to another, summarize long texts, and even answer questions based on the textual context in which it has been formulated.
Application: T5 can be applied to most summarization, translation, and question-answering tasks in virtually every industry-from healthcare to customer care, and many more.
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7. GShard from Google
Size: 600 billion parameters
Overview: GShard is an extremely large model from Google, which employs a different mixture-of-experts style. GShard will activate only a part of the model that is required for the task, thereby making it more efficient than some other larger models. Additionally, it is used to enhance translation systems and multilingual NLP applications.
Application: GShard powers multilingual translation tools, including Google Translate, and other cross-lingual tasks.
Read More: Everything You Should Know About GPT-4
8. XLNet by Google and CMU
Size: 340 million parameters
Overview: XLNet is a generalized autoregressive pretraining model that surpasses BERT in several NLP tasks. In short, it combines the best elements of autoregressive models like GPT and autoencoding models like BERT by giving it the kind of power that can reach deep into the language.
Application: XLNet is commonly applied to text classification, sentiment analysis, and other domains of NLP that require sophisticated comprehension.
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9. EleutherAI GPT-NeoX
Size: 20 billion to 100 billion parameters
Overview: The next list of large language models is GPT-NeoX, an open-source LLM developed at EleutherAI, and it is arguably the most critical alternative of open source and community in developing models like GPT-3. GPT-NeoX generates human-like text and can perform a variety of language-related tasks, including translation, summarization, and creative writing.
Application: To be used in research, chatbots, and text generation applications, GPT-NeoX is making strides thanks to being an open-source application.
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10. Turing-NLG from Microsoft
Size: 17 billion parameters
Overview: Turing-NLG is a large language model developed by Microsoft and, at the time of release, was one of the largest language models in the world. Outlined for generating human-like text, it contains further tasks such as summarization, text generation, and questions.
Applications: Turing-NLG is used in enterprise AI solutions and various NLP-based applications.
With each advancement in LLM AI, the impact on industries and society becomes even more significant:
“Large Language Models are transforming how we communicate, learn, and create across every industry.”
– Umair Ahmed VP of Growth
11. Gopher DeepMind
Size: 280 billion parameters
Overview: Gopher is essentially one of the more frontier LLM AI models developed by DeepMind to deal with a variety of NLP tasks such as reading comprehension, text generation, and translation. DeepMind developed Gopher to enhance AI’s ability to understand complex information and context.
Applications: Gopher is used for AI-assisted content generation, research analysis, text summarization, language modeling, and other NLP tasks.
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12. Ernie 4.0 By Baidu
Size: 100 billion parameters
Overview: ERNIE 4.0 (Enhanced Representation through Knowledge Integration) is Baidu’s breakthrough model in the field of LLM artificial intelligence. The model teaches its large-scale representation of worldly knowledge to the artificial intelligence systems in a semi-contextual way. Unlike its predecessors, Ernie 4.0 interprets context not only from the text but also from the script in the global setting.
Application: Ernie 4.0 is generally used for NLP applications in Chinese, including in Baidu’s search engine, AI assistant, and language translation tools.
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13. Sparrow by DeepMind
Size: Unknown (Currently under development)
Overview: In the list of large language models, Sparrow is a new DeepMind model concerned with the safety and ethical aspects of LLM AI. Developers are creating it to address issues in language modeling with Generative AI, such as harmful outputs, misinformation, and bias. Sparrow, through reinforcement learning, attempts to sharpen its reward and the contextual safeness of responses.
Applications: Sparrow is used mostly in ethical AI research, and it is also being incorporated into virtual assistants, conversational agents, etc. to make it safer to use with the users.
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14. T5.1.1 by Google
Size: 11 billion parameters (largest model)
Overview: T5.1.1 (Text-to-Text Transfer Transformer) was an update to Google’s T5 model. It uses a unified approach by treating any kind of problem in NLP as a text-to-text problem, thus making its multi-tasking approach applicable to various fields like translation, summarization, and question-answering. The model has been tuned to reach the highest performance levels in various domains.
Applications: T5.1.1 is used comprehensively across industries for summarization, language translation, and Q&A applications. It is associated with the creation of content, customer care activities, and many enterprise solutions.
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15. Claude by Anthropic
Size: An estimated 52 billion parameters for Claude 1, larger in Claude 2 and 3
Overview: Claude is a family of conversant models created by Anthropic, one of the heavyweights in LLM artificial intelligence. This model is named after Claude Shannon and was designed following safety-first and ethics-aligned principles.
Applications: Common applications are productivity tools in companies, research assistance, chatbots, and even customer service. It’s the most distinguished of the large language models because of its reasoning besides its polite interaction style.
Read More: 15 Top Chatbot Artificial Intelligence Examples for Businesses
16. Command R+ by Cohere
Size: Estimated tens of billions of parameters achieved
Overview: Command R+ is an LLM AI made by Cohere, specialized for Following Instructions and RAG retrieval purposes. It accesses external knowledge for dynamically responding to all queries while summarizing long documents adeptly.
Applications: Most appreciated in enterprise AI scenarios such as summarizing enormous documents or multi-document reasoning, it has built-in characteristics that equip it well for business-ready conversational agents.
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17. PanGu-Alpha, Huawei
Size: 200 billion parameters
Overview: PanGu-Alpha is one giant Chinese language model being developed by Huawei to provide LLM AI solutions to different industries, especially in Chinese text understanding.
Applications: This fuels Chinese-language NLP applications in text generation, summarization, and question-answering.
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18. OpenAI Codex
Size: Estimated 12 billion parameters
Overview: OpenAI Codex is an LLM AI, specialized for code generation, trained to understand and produce programming language syntax.
Applications: A paradigm shift for developers, Codex is used in code completion, documentation, and natural language to code conversion tasks.
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19. OpenAI Codex
Size: Approximately 12 billion parameters
Overview: OpenAI Codex is a specialized LLM AI for code generation, trained to understand and produce programming language syntax.
Applications: In code completion, documentation, and natural language conversion to code tasks, Codex has changed the way developers work.
Read More: OpenAI vs. DeepMind – Key Differences Explained
20. WuDao by Baidu
Size: 1.75 trillion parameters
Overview: WuDao is a large multimodal model by Baidu that generates text, images, and video; a contender for the largest among language models.
Applications: Supports applications for content creation, AI-driven multimedia, and real-time interactive systems, showcasing the advanced possibilities offered by LLM AI.
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21. GPT-3 by OpenAI
Size: 175 billion parameters
Overview: One of the first popular models of an LLM AI, GPT-3 set the stage for the evolution of text generation and NLP tasks.
Applications: GPT-3 is widely used in content generation, chatbots, and code generation, a strong LLM AI solution for businesses.
Read More: Trending Ideas and Use Cases for OpenAI GPT-3
22. Gemini (formerly Bard) by Google DeepMind
Size: Hundreds of billions of parameters
Overview: With a rebranded name from Bard, Gemini by Google focuses on combining reasoning abilities on a large scale with multimodal functionality and advanced creativity across tasks.
Applications: Chatbots, creative writing, coding assistance, and advanced research.
Read More: Is Google’s New Gemini AI Better than ChatGPT?
23. MPT-7B by MosaicML
Size: 7 billion parameters
Overview: An open-source LM allowed to be commercially used without restrictions, designed for fast inference and training efficiency.
Applications: It is applied in summarization, question-answering, and text generation tasks.
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24. Mixtral by Mistral AI
Size: 12.9 billion active parameters (mixture of experts)
Overview: Mixtral is a sparse MoE model activating a subset of the model at inference, balancing out excellent performance and computational efficiency. Mixtral is a sparse MoE model activating a subset of the model at inference, balancing excellent performance with computational efficiency. List of large language models including Mixtral, GPT-4, T5, and LLaMA showcases the evolving landscape of efficient and powerful AI systems.
Applications: Content generation, enterprise AI, and multilingual NLP tasks.
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25. Falcon 180B by TII (Technology Innovation Institute, UAE)
Size: 180 billion parameters
Overview: Falcon 180B is an open-weight model, with strong performance in reasoning, summarization, and multilingual tasks.
Applications: Research, enterprise NLP solutions, and chatbot development.
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26. Yi-34B by 01.AI (Chinese AI startup)
Size: 34 billion parameters
Overview: Yi-34B is optimized for multilingual task execution, especially in English and Chinese, with known fine-tuning capabilities.
Applications: Language generation, translation, summarization, and content generation.
Read More: Beginners Guide to GPT-3 AI Language Model
27. Mistral AI’s Mistral 7B
Size: 7 billion parameters
Overview: A very compact and dense model is generally said to outperform larger models on many benchmarks.
Applications: Assistant tools, knowledge extraction, and chatbots.
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28. OPT-175B by Meta (Facebook AI Research)
Size: 175 billion parameters
Overview: Meta open-sourced the Open Pretrained Transformer (OPT), matching GPT-3’s scale and targeting research and development use.
Application: Academic research, benchmarking, and NLP studies.
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29. DeepMind’s Chinchilla
Size: 70 billion parameters
Overview: To show that small models, when trained on more data, outperform much larger models with less training in efficiency and accuracy.
Applications: Question answering, summarization, dialogue systems.
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30. Grok by xAI (Company of Elon Musk)
Size: Not disclosed publicly but guessed to be anywhere between 30B and 30B-60B parameters
Overview: Grok intends to build a management of X (formerly Twitter) and to fill chatbot AI with humor, sarcasm, and boldness.
Applications: Social media integration, conversational AI, entertainment-focused chatbots.
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31. LLaMA 2-Meta
Version: 7B, 13B, and 70B parameter versions
Overview: These are more robust models of LLaMA 2 and are commercially available, improving capabilities for reasoning and generation over the original LLaMA architecture.
Applications: Include research, enterprise solutions, AI chatbot development, and educational tools.
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32. BigScience from Hugging Face
Size: 176 billion parameters
Overview: BigScience is an open-source collaborative LLM developed by a worldwide community of researchers. It concerns multilingualism and fairness or equality in language processing and thus will facilitate NLP tasks of languages.
Applications: BigScience is applicable in multilingual applications, research, and natural language understanding tasks. Like this, in the LLM artificial intelligence field, BigScience is highly significant.
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33. GPT-2 by OpenAI
Size: 1.5 billion parameters
Overview: Though not as large as either one of its successors, GPT-3 or GPT-4, GPT-2 broke ground in generating text. A coherent and contextually relevant text rendered it a forerunner to more avant-garde models that would propel the way for surpassingly advanced models in the LLM AI space.
Applications: Extensively used in content generation, chatbots, and as a platform to build domain-specific LLM artificial intelligence solutions.
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34. GLaM by Google
Size: 1.2 trillion parameters (mixture of experts)
Overview: In our list of large language models is GLaM (Generalist Language Model), which uses a mixture-of-experts architecture activating only the parts needed for a given task, enabling efficiency and scalability for large datasets.
Applications: Used in different LLM AI applications such as summarization, question answering, and text generation.
Read More: Best Open Source Generative AI Models
35. Meena by Google
Size: 2.6 billion parameters
Overview: Meena is a conversational LLM Artificial Intelligence model that is all about letting chatbots naturally speak human words. Meena’s training used a huge conversational dataset to help it understand different contexts and respond accurately.
Applications:For now, developers mainly use Meena in conversational AI, but it aims to become one of the most fluent and responsive LLM AI models in its class.
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36. LaMDA 2 by Google
Size: Unknown (larger than PaLM)
Overview: LaMDA 2 is merely an update of the original LaMDA model, with additional optimization features to accommodate complex, multi-turn conversations while notionally preserving context between dialogue sessions.
Applications: Propagate further the utilities of LLM AI and natural language understanding in improving user experience in virtual assistants and conversational AI systems, augmenting users’ interaction with these systems.
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37. Bloom by Hugging Face
Size: 176 billion parameters
Overview: Bloom is an open-source LLM artificial intelligence model, famous for generating diverse forms of text in many languages. The larger push is research towards democratized access to large LLM AI systems.
Applications: Research, creative writing, multilingual NLP, and many others would find an equally competitive alternative in Bloom to some of the largest language models.
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38. Turing-Bletchley by Microsoft
Size: 10 trillion parameters
Overview: The Turing-Bletchley is a successor model to Turing-NLG and is built for large-scale reasoning and question-answering tasks. It’s going further into the LLM AI frontier due to its prodigious parameter scale.
Applications: Used in data-centric industries such as finance, healthcare, and research for complex LLM AI tasks and decision-making.
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39. Mistral 7B
Size: 7 billion parameters
Overview: Mistral 7B performs in a compact and efficient style engineered for fast inference while still being very adaptable in dealing with various NLP tasks.
Applications: This LLM performs well for text generation, summarization, and question answering. At Cubix, we use models like Mistral to build custom AI solutions for our customers.
Read More: How to Integrate ChatGPT into Your Business
40. Switch Transformer by GOOGLE
Size: 1 Trillion parameters
Overview: The Switch Transformer developed by Google is a novel method in which it activates only a part of its parameters at any instance to attain efficiency on large-scale models.
Applications: It is being used for language modeling, translation, and other NLP tasks in diverse domains. Cubix utilizes models like the Switch Transformer to build mindful AI solutions.
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41. Alpa by Microsoft Research
Size: 175 billion parameters
Overview: Alpa enhances large language models using state-of-the-art parallelization techniques to scale up their capabilities.
Applications: Works with immense-scale text generation, NLP, translation, etc. At Cubix, we utilize Alpa’s capabilities in our custom AI applications to offer optimal business solutions.
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42. PanGu-Alpha by Huawei
Size: 200 billion parameters
Overview: PanGu-Alpha is a Chinese language model aimed at augmenting the comprehension and generation of Mandarin text.
Applications: It supports Chinese NLP tasks such as aiding search engines, translation, and AI assistants. Cubix uses models like PanGu-Alpha to create tailor-made solutions for the Chinese-speaking market.
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43. Gopher by DeepMind
Size: 280 billion parameters
Overview: Gopher comes from DeepMind, which strives to enhance understanding and performance on complex language tasks.
Applications: It excels in text generation, summarization, and language modeling. At Cubix, we are applying models like Gopher to push AI at content generation and complicated tasks.
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44. LLaMA by Meta
Size: 7B, 13B, 30B, 65B parameters
Overview: LLaMA is the open-source LLM from Meta, meant to be scalable and efficient in handling a myriad of NLP tasks.
Applications: Its use is predominant in AI research and chatbot and translation solutions. Cubix utilizes the scalability of LLaMA to build power-efficient, custom NLP solutions across industries.
Read More: ChatGPT vs GPT-3-Key Differences Explained
45. Open Assistant
Size: Varies (commonly 7B–65B parameters)
Overview: Open Assistant is an open-source conversational AI project developed by LAION. It aims to provide accessible, high-quality language models for public and academic use.
Applications: Suitable for voice assistants, chatbots, educational tools, and custom AI interfaces across industries.
46. Flan-T5 by Google
Size: 7B, 11B, 13B parameters
Overview: Flan-T5 is a fine-tuned version of Google’s T5, enhanced with instruction learning to perform well across diverse NLP tasks.
Applications: Used for question answering, summarization, and classification with strong performance in low-data settings.
47. MUM by Google
Size: Not officially disclosed (estimated hundreds of billions of parameters)
Overview: MUM (Multitask Unified Model) is a multimodal model that processes text and images simultaneously, designed to understand complex search queries.
Applications: Useful for search engines, content recommendations, and multilingual query handling.
48. Chinchilla by DeepMind
Size: 70B parameters
Overview: Chinchilla trains on significantly more data than GPT-3, achieving better efficiency and accuracy as a compute-optimal transformer model.
Applications: Applied in research, document generation, NLP benchmarks, and energy-efficient intelligent agents.
49. Sparrow 2 by DeepMind
Size: Estimated 70B+ parameters
Overview: Sparrow 2 builds on safety and alignment principles with reinforcement learning from human feedback, aiming for responsible conversational AI.
Applications: Suitable for safe, ethical chatbots and applications where human-AI interaction requires trust and control.
50. ERNIE Bot Titan by Baidu
Size: 260B parameters
Overview: ERNIE Bot Titan is Baidu’s large-scale LLM, designed with deep semantic understanding and multilingual capabilities, especially strong in Chinese.
Applications: Deployed for translation, search, enterprise automation, and AI tasks requiring contextual precision in Chinese and global languages.
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Final Thoughts
This List of Large Language Models features advanced tools for chatbots, search, content automation, and more. Each model serves specific goals like instruction-following or multilingual support.
If you need custom AI solutions using any model from this List of Large Language Models, Cubix is here to help with custom, and scalable systems.
Malik Muzammil
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