16 of the best large language models

Large language models have been around for a while and were the driving force behind the generative AI boom of 2024.

16 of the best large language models: LLMs refer to black-box AI systems that use deep learning on massive datasets to both understand and generate new text. The origins of modern LLMs can be traced back to 2014 when the attention mechanism was introduced in a research paper titled “Neural Machine Translation by Jointly Learning to Align and Translate.” This machine-learning technique was designed to mimic human cognitive attention. In 2017, the attention mechanism was refined further with the introduction of the transformer model in another paper titled “Attention Is All You Need.”

16 of the best large language models
16 of the best large language models

It’s worth noting that some of the most renowned language models today are built on the transformer model. Notably, the LLMs’ Generative Pre-trained Transformer series and Bidirectional Encoder Representations from Transformers (BERT) are widely recognized.

ChatGPT, which operates on a group of language models from OpenAI, gained over 100 million users within just two months of its launch in 2022. Since then, several competing models have emerged, some belonging to major corporations like Google and Microsoft, while others are available as open-source.

It can be challenging to keep up with the constant advancements in the field. To help you stay informed, here are some of the most impactful models, from both the past and present. This includes models that set the foundation for current leaders and ones that have the potential to make a significant impact in the future.

Top current LLMs: 16 of the best large language models

These are some of the most relevant large language models today. They perform natural language processing and shape the design of future models.


In 2018, Google introduced a family of Language Models called Bidirectional Encoder Representations from Transformers (BERT). BERT is a transformer-based model that can convert sequences of data to other sequences of data. The architecture of BERT consists of a stack of transformer encoders with 342 million parameters. This means that it has a deep neural network that can process large amounts of data and generate accurate results.

16 of the best large language models
16 of the best large language models

BERT has been pre-trained on a huge corpus of data and then fine-tuned to perform specific tasks, such as natural language inference and sentence text similarity. The pre-training process involves exposing BERT to a vast amount of text data and allowing it to learn from that data. This helps BERT to understand the context and meaning of words, phrases, and sentences, making it more accurate in processing natural language.

One of the most significant applications of BERT is its use in improving query understanding in the 2019 iteration of Google search. BERT helps Google understand the intent behind a user’s query, which allows it to provide more accurate and relevant search results. This has been a major step forward in the field of natural language processing and has improved the search experience for millions of people worldwide.

also Read: Google’s Gemini Pro Beats GPT-4


The Claude LLM program is designed to concentrate on constitutional AI, which directs the AI outputs based on a set of principles to make the AI assistant useful, safe, and precise. The company Anthropic created Claude, which empowers its two primary product offerings, namely Claude Instant and Claude 2. According to Anthropic, Claude 2 is capable of performing complicated reasoning.


Cohere is an enterprise-level language model that can be tailored and fine-tuned for a specific company’s requirements. The creators of Cohere include one of the authors of the renowned paper “Attention Is All You Need”. One of the significant advantages of Cohere is its independence from any specific cloud provider, unlike OpenAI, which is limited to Microsoft Azure.


Ernie is a language model developed by Baidu, which powers the Ernie 4.0 chatbot. The chatbot was launched in August 2023 and has gained over 45 million users. It is rumored that Ernie has 10 trillion parameters. Although the bot performs best in Mandarin, it is also proficient in other languages.

Falcon 40B

The Technology Innovation Institute has developed a decoder-only model called Falcon 40B, which is based on transformers and is designed to be causally connected. The model has been trained on English language data and is open source. Smaller versions of the model are also available, including Falcon 1B and Falcon 7B, which have 1 billion and 7 billion parameters respectively. Amazon has made Falcon 40B available on Amazon SageMaker, while it can also be downloaded for free from GitHub.


In November 2022, Meta released Galactica, an LLM designed for scientists. It had been trained on a vast collection of academic materials, including 48 million papers, lecture notes, textbooks, and websites. However, like most models, it produced AI “hallucinations” or fake texts that sounded authoritative. As a result, the scientific community found them unsafe and challenging to detect quickly, which is particularly concerning in a domain that requires little margin for error.


GPT-3 is a language model developed by OpenAI. It was released in 2020 and has more than 175 billion parameters, making it ten times larger than its predecessor. GPT-3’s architecture uses a decoder-only transformer. Its training data includes Common Crawl, WebText2, Books1, Books2, and Wikipedia. Microsoft announced in September 2022 that it had exclusive use of GPT-3’s underlying model. GPT-3 is the final model in the GPT series, in which OpenAI publicly shares the parameter counts. The series was first introduced in 2018 with OpenAI’s paper “Improving Language Understanding by Generative Pre-Training.”


GPT-3.5 is an upgraded version of GPT-3 that was developed to reduce the number of parameters in the model. The goal was to create a model that was faster and more efficient than its predecessor. To achieve this, it was fine-tuned using reinforcement learning from human feedback, which helped to enhance its performance and accuracy.

GPT-3.5 is the version of GPT that powers ChatGPT, and it is available in several models, with GPT-3.5 turbo being the most capable, as stated by OpenAI. This version of GPT has been designed to offer better performance and accuracy in natural language processing tasks, making it ideal for various applications.

The training data for GPT-3.5 extends up to September 2021, which means that the model has been trained on a large corpus of text data from various sources. This has helped to improve its performance and accuracy in understanding and generating natural language.

GPT-3.5 was integrated into the Bing search engine, enabling it to provide more accurate and relevant search results to users. However, it has since been replaced by GPT-4, which is the latest version of the GPT series. Despite this, GPT-3.5 remains a powerful and capable model that can be used for various natural language processing applications.


In 2023, OpenAI released GPT-4, which is the largest model in the GPT series. As a transformer-based model, GPT-4 can process and generate both language and images, unlike its predecessors. Although the parameter count of GPT-4 has not been disclosed to the public, it is rumored to have more than 170 trillion parameters. OpenAI describes GPT-4 as a multimodal model that can process and generate both language and images. Additionally, GPT-4 introduces a system message that allows users to specify their tone of voice and task.

GPT-4 has demonstrated human-level performance in multiple academic exams, leading some to speculate that it is close to achieving artificial general intelligence (AGI), which means it is as smart or smarter than a human. Currently, GPT-4 powers Microsoft Bing search and is available in the ChatGPT Plus platform. In the future, it will also be integrated into Microsoft Office products.


Lamda, which stands for Language Model for Dialogue Applications, is a series of LLMs developed by Google Brain. It was introduced in 2021 and utilizes a decoder-only transformer language model. The model was pre-trained on a vast corpus of text. In 2022, LaMDA gained a lot of attention when Blake Lemoine, a former Google engineer, claimed that the program was sentient. The Seq2Seq architecture was used in its construction.


In 2023, Meta released its Large Language Model Meta AI (Llama), which is an LLM with a maximum size of 65 billion parameters. Initially, only approved researchers and developers had access to Llama, but it is now open-source. Llama comes in smaller sizes that require less computing power to use, test, and experiment with. Llama uses a transformer architecture and was trained on various public data sources, including web pages from CommonCrawl, GitHub, Wikipedia, and Project Gutenberg. Llama was effectively leaked and gave rise to numerous descendants, including Vicuna and Orca.


Microsoft developed Orca, which has 13 billion parameters, making it small enough to run on a laptop. Its objective is to improve on the advancements made by other open-source models by imitating the reasoning procedures achieved by LLMs. Orca has the same performance as GPT-4 with significantly fewer parameters and is on par with GPT-3.5 for many tasks. Orca is built on top of the 13 billion parameter version of LLaMA.


The Pathways Language Model is a transformer-based model developed by Google to power its AI chatbot Bard. It has 540 billion parameters and was trained across multiple TPU 4 Pods – Google’s custom hardware for machine learning. The model is particularly useful for reasoning tasks such as coding, math, classification, and question answering. It is also proficient at breaking down complex tasks into simpler subtasks. Palm is named after Google’s research initiative to build Pathways, which aims to create a single model that can be used for multiple purposes. Several versions of Palm have been fine-tuned for specific use cases, including Med-Palm 2 for life sciences and medical information and Sec-Palm for cybersecurity deployments to speed up threat analysis.


Phi-1 is a language model built by Microsoft that uses transformers. Despite having only 1.3 billion parameters, Phi-1 was trained for four days using high-quality data. This demonstrates a growing trend of using smaller models trained on better quality and synthetic data. Andrej Karpathy, former director of AI at Tesla and an OpenAI employee, predicts that we will see more scaling down work that prioritizes data quality and diversity over quantity, using synthetic data generation, and developing small but highly capable expert models. Phi-1 has a specialized focus on Python coding due to its smaller size, but it lacks some of the general capabilities of larger models.


StableLM is a collection of open-source language models created by Stability AI, the company that developed the image generator Stable Diffusion. At present, there are two models available, one with 3 billion parameters and another with 7 billion parameters. Moreover, there are models with 15 billion, 30 billion, 65 billion, and 175 billion parameters in progress. The goal of StableLM is to provide language models that are transparent, easy to use, and supportive.

Vicuna 33B

Vicuna is a significant open-source LLM that is derived from Llama. It was created by LMSYS and was improved using data from sharegpt.com. While it is smaller and less powerful than GPT-4 based on several benchmarks, it performs well for a model of its size. Vicuna has 33 billion parameters, while GPT-4 has trillions.

LLM precursors

LLMs are a recent phenomenon with distant precursor ELIZA and recent precursor Seq2Seq that set the stage for modern LLMs.


Seq2Seq is a deep learning methodology that is utilized for machine translation, natural language processing, and image captioning. It was created by Google and is the basis for some of their advanced LLMs, such as LaMDA. Additionally, AlexaTM 20B, Amazon’s large language model, also employs Seq2Seq. The approach uses a combination of encoders and decoders to generate accurate results.


Eliza was an early natural language processing program created in 1966 by Joshua Weizenbaum. It simulated conversation using pattern matching and substitution. Eliza applied weights to certain keywords and responded to the user accordingly. The program could parody the interaction between a patient and therapist. Weizenbaum wrote a book on the limits of computation and artificial intelligence. Eliza was an important early example of natural language processing that paved the way for modern chatbots and virtual assistants like Siri and Alexa.


The world of AI is rapidly evolving and these 16 of the best large language models are paving the way for major transformations. We must approach the development and use of these models with ethical considerations, user privacy, and the betterment of society in mind. These models are reshaping the way we communicate and interact in the digital age, and the journey into the future of AI is an exciting one.

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