Top 20 Large Language Models (LLMs) for 2024

Explore the top 20 Large Language Models (LLMs) as of April 2024. Discover how these advanced neural networks, equipped with billions of parameters and trained on extensive datasets, are transforming natural language processing. Each model has unique features and applications, making them invaluable tools in various AI tasks. Dive into our comprehensive guide to understand their capabilities and potential impact in the field.

Top 20 LLM (Large Language Models)

Large Language Model, commonly known as an LLM, refers to a neural network equipped with billions of parameters and trained extensively on large datasets of unlabeled text. This training typically involves self-supervised or semi-supervised learning techniques. In this article, we explore the Top 20 LLM Models and discover how each model has distinct features and applications.

Top-20-LLM-Models

1. GPT-4

As of 2024, OpenAI’s GPT-4 stands out as the leading AI Large Language Model (LLM) in the market. Launched in March 2023, its parameter count has not been released to the public, though there are rumors that the model has more than 170 trillion parameters. GPT-4 has demonstrated exceptional capabilities, excelling in complex reasoning, advanced coding, proficiency in various academic domains, and achieving human-level performance in diverse skills. Notably, it’s the first multimodal model accepting both text and image inputs. GPT-4 distinguishes itself by addressing hallucination issues and significantly improving factuality. In factual evaluations across multiple categories, GPT-4 outperforms ChatGPT-3.5, scoring close to 80%. OpenAI has prioritized aligning GPT-4 with human values, employing Reinforcement Learning from Human Feedback (RLHF) and rigorous adversarial testing by domain experts.

Features of GPT-4

• Massive Scale: GPT-4 boasts a colossal architecture, allowing it to process vast amounts of data and generate highly coherent and contextually relevant text.
• Advanced Natural Language Understanding: It exhibits enhanced capabilities in understanding complex language structures, nuances, and context, leading to more accurate and contextually appropriate responses.
• Fine-Tuning Flexibility: GPT-4 offers flexibility in fine-tuning for specific tasks or domains, making it adaptable to various NLP applications.

2. GPT-3

GPT-3 is an OpenAI large language model, released in 2020, and stands out as a groundbreaking NLP model, boasting a record-breaking 175 billion parameters—the highest among NLP models. With its colossal size, GPT-3 has revolutionized natural language processing, showcasing the capability to generate human-like responses across prompts, sentences, paragraphs, and entire articles. Employing a decoder-only transformer architecture, GPT-3 represents a significant leap, being 10 times larger than its predecessor. In a noteworthy development, Microsoft announced exclusive use of GPT-3’s underlying model in September 2022. GPT-3 marks the culmination of the GPT series, introduced by OpenAI in 2018 with the seminal paper “Improving Language Understanding by Generative Pre-Training.”

Features of GPT-3

• Unprecedented Size: GPT-3 is renowned for its sheer size, containing billions of parameters that contribute to its impressive language generation capabilities.
• Zero-Shot Learning: It can perform tasks without explicit training on them, showcasing its ability to generalize across a wide range of NLP tasks.
• Contextual Understanding: GPT-3 excels in understanding and maintaining context over long passages of text, resulting in coherent and contextually relevant responses.

3. GPT-3.5

GPT-3.5 represents an enhanced iteration of GPT-3, featuring a reduced parameter count. This upgraded version underwent fine-tuning through reinforcement learning from human feedback, demonstrating OpenAI’s commitment to refining language models. Notably, GPT-3.5 serves as the underlying technology for ChatGPT, with various models available, including the highly capable GPT-3.5 turbo, as highlighted by OpenAI. It’s an incredibly fast model and generates a complete response within seconds and is also free to use without any daily restrictions. However, it does have some shortcomings, like being prone to hallucinations, sometimes generating incorrect information. This makes it a little less ideal for serious research work. In the HumanEval benchmark, the GPT-3.5 model scored 48.1%.

Feature of GPT-3.5

• Performance Improvements: Building upon GPT-3, GPT-3.5 incorporates enhancements in performance metrics such as accuracy, efficiency, and speed.
• Efficient Fine-Tuning: It offers improved fine-tuning capabilities, allowing users to tailor the model for specific tasks or datasets with ease.
• Scalability: GPT-3.5 maintains scalability, enabling it to handle large-scale datasets and generate high-quality text across diverse applications.

4. Gemini

Google’s new AI, Gemini, seems to be stepping up the game against ChatGPT. Released in December 2023, it was built from the ground up to be multimodal, meaning it can generalize and seamlessly understand, operate across, and combine different types of information, including text, code, audio, image, and video. It has outperformed ChatGPT in almost all academic tests, such as understanding text, images, videos, and even speech. With a score of 90.0%, Gemini Ultra is the first model to outperform human experts on MMLU (massive multitask language understanding), which uses a combination of 57 subjects such as math, physics, history, law, medicine, and ethics for testing both world knowledge and problem-solving abilities. Developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI.

Feature of Gemini

• Conversational AI Focus: Gemini emphasizes improving conversational AI by better understanding context and generating more human-like responses in dialogues.
• Contextual Sensitivity: It exhibits enhanced sensitivity to context shifts within conversations, leading to more coherent and contextually appropriate responses.
• Multimodal Integration: Gemini integrates multiple modalities such as text, images, and audio to enrich the conversational experience and generate more comprehensive responses.

5. LLaMA

LLaMa, or Large Language Model Meta AI, emerges as a significant development in the realm of Large Language Models by Meta AI. The inaugural release of LLaMa in February 2023 featured the largest version of 65 billion parameters. Since its unveiling, Meta’s introduction of the LLaMa family of large language models (LLMs) has become a valuable asset for the open-source community. The diverse range of LLaMA models, spanning from 7 billion to 65 billion parameters, has demonstrated superior performance compared to other LLMs, including GPT-3, across various benchmarks. An undeniable advantage of LLaMA models lies in their open-source nature, empowering developers to easily fine-tune and create new models tailored to specific tasks. This approach fosters rapid innovation within the open-source community, leading to the continuous release of new and enhanced LLM models.

Feature of LLaMA

• Long-Term Language Understanding: LLaMA specializes in long-term language understanding and reasoning, enabling it to grasp complex relationships and concepts across extended passages of text.
• Reasoning Capabilities: It incorporates advanced reasoning capabilities, allowing it to infer implicit information, draw logical conclusions, and answer complex questions.
• Contextual Memory: LLaMA retains contextual memory over prolonged interactions, facilitating more coherent and contextually consistent responses in dialogues and conversations.

6. PaLM 2 (Bison-001)

PaLM 2 is a large language model (LLM) developed by Google AI. Google has elevated the capabilities of PaLM 2 by emphasizing various aspects, including commonsense reasoning, formal logic, mathematical equations, and advanced coding, spanning over 20 languages. Remarkably, the most extensive version of PaLM 2 is purportedly trained on a massive 540 billion parameters. With its multilingual proficiency, PaLM 2 excels in comprehending idioms, solving riddles, and interpreting nuanced texts in a diverse range of languages, a feat that poses challenges for other Large Language Models (LLMs). One more advantage of PaLM 2 is that it’s very quick to respond and offers three responses at once.

Features of PaLM 2 (Bison-001)

• Pattern-Based Learning: PaLM 2 utilizes pattern-based learning techniques to enhance text generation and comprehension, enabling it to capture intricate language patterns and nuances.
• Adaptive Training: It offers adaptive training capabilities, allowing it to continuously improve and refine its language understanding and generation abilities over time.
• Efficiency: PaLM 2 prioritizes efficiency in processing and resource utilization, making it suitable for a wide range of NLP applications, including those with resource constraints.

7. Bard

Google Bard stands as an experimental conversational AI service driven by LaMDA (Language Model for Dialogue Applications), a project undertaken by Google AI. Notably, Bard introduces subtle distinctions from other Large Language Models in its approach. Firstly, it is tailored for natural conversations, enabling seamless dialogue with users. Secondly, Bard is internet-connected, allowing real-time access and processing of information from the online domain. This unique feature positions Bard to provide more current and pertinent information compared to LLMs trained on static datasets. With an impressive 1.6 trillion parameters, Bard emerges as an extraordinary language model with a remarkable capacity to discern intricate language nuances and patterns.

Features of Bard

• Flexibility and Efficiency: Bard is highly flexible and efficient, accommodating various NLP tasks and workflows while maintaining robust performance.
• Large-Scale Architecture: It features a large-scale architecture, enabling it to handle extensive datasets and complex language structures with ease.
• Fine-Tuning Capabilities: Bard offers fine-tuning capabilities, allowing users to adapt the model for specific tasks or domains and achieve optimal performance.

8. Claude v1

While Claude may not be as popular as GPT or LLaMA, it is a powerful LLM developed by Anthropic, co-founded by former OpenAI employees. As a newcomer to the Large Language Model landscape, Claude is outperforming PaLM 2 in benchmark tests and offers a groundbreaking 100k token context window for the first time. Competing directly with GPT-4, Claude v1 scored 7.94 in the MT-Bench test, while GPT-4 scored 8.99. In the MMLU benchmark, Claude v1 secured 75.6 points, compared to GPT-4's score of 86.4. Its 100k token context means you can easily load a full-length book into Claude’s context window, and it will still understand and generate text in response to your prompts.

Features of Claude v1

• Understanding Complex Structures: Claude v1 excels in understanding complex language structures, including nuanced expressions, idiomatic phrases, and syntactic variations.
• Coherent Responses: It generates coherent and contextually relevant responses across diverse contexts, maintaining coherence and consistency in dialogues and interactions.
• Task Adaptability: Claude v1 is adaptable to various NLP tasks and domains, offering flexibility in application and integration into different workflows and systems.

9. Falcon

The Falcon model, developed by the Technology Innovation Institute (TII) in the UAE, is a causal decoder-only model that stands out as a dynamic and scalable language model, offering exceptional performance and scalability. As an open-source model, Falcon has outranked all other open-source models released so far, including LLaMA, StableLM, and MPT. The training process incorporated custom tooling and a unique data pipeline to ensure the quality of the training data, utilizing an extensive dataset comprising web text and curated sources. Falcon integrates enhancements like rotary positional embeddings and multi-query attention, contributing to its improved performance. While primarily trained in English, German, Spanish, and French, it can also work in many other languages.

Features of Falcon

• Efficiency and Scalability: Falcon prioritizes efficiency and scalability, making it suitable for large-scale deployment and processing of vast amounts of data.
• Task Optimization: It is optimized for various NLP tasks, including text classification, language generation, and sentiment analysis, delivering high-quality results across different applications.
• Model Compression: Falcon incorporates techniques for model compression and optimization, reducing memory and computational requirements without compromising performance.

10. Cohere

Cohere, founded by former Google employees who worked on the Google Brain team, is an enterprise LLM that can be custom-trained and fine-tuned to a specific company’s use case. Cohere offers multiple models ranging from just 6B parameters to large models trained on 52B parameters. The Cohere Command model is earning acclaim for its precision and resilience, securing the top position for accuracy according to Stanford HELM. Noteworthy companies, including Spotify, Jasper, and HyperWrite, are leveraging Cohere’s model to enhance their AI experiences. However, it charges $15 to generate 1 million tokens, which is high compared to its competitors.

Features of Cohere

• Contextual Understanding: Cohere focuses on contextual understanding, capturing nuanced relationships and dependencies within text to generate more accurate and contextually relevant responses.
• Conversational AI Enhancement: It enhances conversational AI by better understanding user intents, preferences, and context shifts, leading to more engaging and human-like interactions.
• Multi-Turn Dialogue Handling: Cohere is proficient in handling multi-turn dialogues, maintaining coherence and context continuity over extended interactions to facilitate natural and fluid conversations.

11. Orca

Orca, developed by Microsoft with 13 billion parameters, is designed to operate efficiently even on a laptop. This fine-tuned version of LLaMA performs as well as or better than models containing ten times the number of parameters. Orca achieves comparable performance to GPT-4, despite its lower parameter count, demonstrating proficiency on par with GPT-3.5 across various tasks. Orca 2 employs a synthetic training dataset and a novel technique called Prompt Erasure to achieve its performance. It utilizes a teacher-student training approach, leveraging a larger, more potent LLM as a teacher guiding a smaller student LLM, aiming to elevate the student model's performance to rival larger counterparts.

Features of Orca

• Multimodal Integration: Orca integrates multiple modalities such as text, images, and audio to enrich language understanding and generation, enabling more comprehensive and contextually relevant responses.
• Cross-Modal Learning: It leverages cross-modal learning techniques to extract meaningful correlations between different modalities, enhancing overall understanding and representation learning.
• Enhanced Contextual Sensitivity: Orca exhibits enhanced sensitivity to contextual cues across different modalities, allowing it to generate more accurate and contextually appropriate responses in multimodal settings.

12. Guanaco

Guanaco is another model derived from the framework of the existing model LLaMA. It is an open-source model tailored for contemporary chatbots, coming in various sizes from 7B to 65B, with Guanaco-65B standing out as the most powerful, closely trailing the Falcon model in open-source performance. In the MMLU test, Guanaco scored 52.7, while the Falcon model scored 54.1. All Guanaco models are trained on the OASST1 dataset by Tim Dettmers and utilize a novel fine-tuning technique called QLoRA, optimizing memory usage without compromising task performance. Notably, Guanaco models surpass some top proprietary LLMs like GPT-3.5 in performance.

Features of Guanaco

• Unsupervised Learning: Guanaco specializes in unsupervised learning, leveraging large-scale unlabeled data to learn rich representations and generate contextually relevant text without explicit supervision.
• Semantic Understanding: It demonstrates advanced semantic understanding, capturing underlying meanings and intents within text to generate coherent and contextually appropriate responses.
• Adaptive Learning: Guanaco continuously adapts and refines its language understanding and generation abilities through self-supervised learning techniques, improving performance over time without additional labeled data.

13. Vicuna

Vicuna, an impactful open-source LLM stemming from LLaMA, has been crafted by LMSYS and fine-tuned with data from sharegpt.com, a portal where users share their ChatGPT conversations. The training dataset consists of 70,000 user-shared ChatGPT conversations, providing a rich source for honing its language abilities. Remarkably, the entire training process was accomplished with a cost of only $300, utilizing PyTorch FSDP on 8 A100 GPUs, and was completed in just one day, showcasing the model’s efficiency in delivering high performance on a budget. In LMSYS’s own MT-Bench test, Vicuna scored 7.12, while the best proprietary model, GPT-4, secured 8.99 points. While smaller and less capable than GPT-4 based on various benchmarks, Vicuna performs admirably for its size, boasting 33 billion parameters compared to the trillions in GPT-4.

Features of Vicuna

• Efficient Training: Vicuna employs efficient training techniques, enabling rapid convergence and training on large-scale datasets with minimal computational resources.
• Robust Performance: It delivers robust performance across various NLP tasks, including text generation, summarization, and language understanding, achieving state-of-the-art results in benchmark evaluations.
• Scalability and Adaptability: Vicuna maintains scalability and adaptability, making it suitable for deployment in diverse environments and applications, from research prototypes to production systems.

14. MPT-30B

MPT-30B is an open-source foundation model licensed under Apache 2.0, surpassing the quality of GPT-3 from the original paper. It competes with other open-source models like LLaMa-30B and Falcon-40B. This model has been fine-tuned on an extensive corpus from various sources, including GPTeacher, Baize, and Guanaco. Notably, MPT-30B boasts one of the longest context lengths at 8K tokens and has outperformed OpenAI's GPT-3, scoring 6.39 in LMSYS’s MT-Bench test. Several variations of MPT-30B are available, each with unique features, providing users with options for model configuration and parameter tuning to meet specific needs.

Features of MPT-30B

• Multimodal Understanding: MPT-30B excels at integrating text with other modalities like images and audio, resulting in comprehensive and contextually relevant responses.
• Cross-Modal Knowledge Transfer: The model utilizes cross-modal knowledge transfer techniques to enhance understanding and representation learning across different modalities, boosting performance in multimodal tasks.
• Fine-Grained Contextual Sensitivity: It demonstrates an ability to capture subtle nuances and dependencies within and across modalities, enabling more accurate and contextually appropriate responses.

15. 30B Lazarus

Launched in 2023 by CalderaAI, 30B-Lazarus is an enhanced version of the LlaMA language model. By leveraging LoRA-tuned datasets from various models, it has been developed to perform exceptionally well across various LLM benchmarks, scoring 81.7 in HellaSwag and 45.2 in MMLU, just behind Falcon and Guanaco. Although it excels in text generation, it does not support conversational, human-style chat. Multiple model versions are tailored to meet diverse industry needs.

Features of 30B Lazarus

• Scalability and Adaptability: This model emphasizes scalability and adaptability, facilitating efficient training and deployment on large-scale datasets in varied environments.
• Continual Learning: 30B Lazarus supports continual learning, enabling the model to improve over time with new data and experiences without the need for retraining from scratch.
• Robustness to Concept Drift: The model is designed to maintain performance and reliability in dynamic environments, even when data distributions change.

16. Flan-T5

Flan-T5 is an open-source LLM introduced by Google researchers, operating as an encoder-decoder model. It has undergone pre-training across a variety of language tasks, employing both supervised and unsupervised datasets to master the mappings between text sequences, functioning within a text-to-text paradigm. Flan-T5 is available in various sizes, including Flan-T5-Large, which contains 780M parameters and can manage over 1,000 tasks. The FLAN models support diverse applications, from commonsense reasoning to question generation and cause-and-effect classification, and can even detect "toxic" language in conversations across multiple languages.

Features of Flan-T5

• Task-Specific Optimization: The model is fine-tuned for specific NLP tasks, such as question answering, summarization, and text classification, to achieve superior performance.
• Efficient Inference: It prioritizes efficiency, delivering fast and responsive results without sacrificing accuracy or quality, making it ideal for real-time applications.
• Model Compression: Flan-T5 utilizes techniques for model compression and optimization, reducing memory and computational needs for deployment in resource-constrained environments like mobile and edge devices.

17. WizardLM

WizardLM is an open-source large language model that excels in understanding and executing complex instructions. Utilizing the innovative Evol-instruct approach, AI researchers rewrite initial instructions into more intricate forms, employing the generated instruction data for fine-tuning the LLaMA model. This unique approach has enhanced WizardLM’s performance on benchmarks, making it preferred over ChatGPT responses. In the MT-Bench test, WizardLM achieved a score of 6.35 and 52.3 in the MMLU test, delivering impressive results despite having only 13B parameters.

Features of WizardLM

• Human-Computer Interaction Enhancement: WizardLM aims to improve interactions by generating informative and contextually relevant responses, leading to more natural dialogues.
• Multi-Turn Dialogue Handling: It is adept at managing multi-turn dialogues, ensuring coherence and context continuity over extended interactions.
• Interactive Learning: The model supports interactive learning, allowing users to provide feedback and guidance to refine its responses over time.

18. Alpaca 7B

Alpaca, a prominent member of the Llama family, specializes in language understanding and generation. Developed by Stanford University, this Generative AI chatbot closely resembles OpenAI’s GPT-3.5 but stands out due to its cost-effectiveness, requiring less than $600 to create. Alpaca 7B, a fine-tuned version of Meta’s seven billion-parameters LLaMA language model, was optimized using mixed precision and Fully Sharded Data Parallel training. It was fine-tuned in just three hours on eight 80GB Nvidia A100 chips, costing less than $100 on cloud computing platforms. Alpaca’s performance has been shown to be quantitatively comparable to OpenAI’s text-davinci-003, winning 90 out of 89 comparisons in evaluations.

Features of Alpaca 7B

• Efficient Architecture: Alpaca 7B features an efficient architecture, balancing performance and resource utilization to deliver high-quality results with minimal computational overhead.
• Task Adaptability: It is adaptable across various NLP tasks and domains, providing flexibility for application and integration into diverse workflows.
• Robust Performance: Alpaca 7B maintains strong performance across different applications, demonstrating consistent accuracy and reliability in benchmark evaluations.

19. LaMDA

LaMDA, introduced as the successor to Google’s Meena in 2020, represents a major advancement in conversational AI. Unveiled at the 2021 Google I/O keynote, LaMDA utilizes the powerful Transformer architecture, a neural network model first introduced by Google Research in 2017. Its extensive training process involves billions of documents, dialogs, and utterances, totaling approximately 1.56 trillion words. Google emphasizes that LaMDA's responses aim to be "sensible, interesting, and contextually relevant." LaMDA also integrates access to various symbolic text processing systems, including databases, real-time clocks, calendars, calculators, and translation systems, granting it superior accuracy for supported tasks and making it one of the leading dual-process chatbots in conversational AI.

Features of LaMDA

• Conversational AI Enhancement: LaMDA aims to enhance conversational AI by better grasping nuances and context in human interactions, resulting in more engaging dialogues.
• Sensitivity to Context Shifts: The model shows heightened sensitivity to context shifts within conversations, enabling coherent and contextually appropriate responses in dynamic settings.
• Semantic Understanding: LaMDA exhibits advanced semantic understanding, accurately capturing meanings and intents in text for contextually relevant responses.

20. BERT

Finally, BERT, or Bidirectional Encoder Representations from Transformers, is a pioneering open-source model introduced by Google in 2018. As one of the earliest Large Language Models (LLMs), BERT quickly became a standard in Natural Language Processing (NLP) tasks. Its remarkable performance made it a preferred choice for various language applications, including general language understanding, question answering, and named entity recognition. BERT's success is attributed to its transformer architecture and the open-source nature, allowing developers access to the original code and spurring the generative AI revolution we observe today.

Features of BERT

• Bidirectional Contextual Understanding: BERT transformed NLP by enabling bidirectional contextual understanding, capturing dependencies and relationships between words in both directions.
• Transfer Learning: It facilitates transfer learning across tasks and domains, leveraging pre-trained representations to boost performance on downstream tasks, even with limited labeled data.
• Fine-Grained Embeddings: BERT generates detailed word embeddings, encapsulating rich semantic information and contextual nuances to enhance language understanding.

Comparison of Popular LLM Models

Conclusion

In essence, exploring the top LLMs provides insight into the current state of the art and potential avenues for future advancements. These models are becoming increasingly impactful, influencing various industries.