Since ChatGPT entered the public domain, it revolutionized how natural language content is generated. Public interest in Generative AI and Large Language Models (LLM) has been propelled to greater heights. The arena of LLMs is evolving rapidly with improved models entering the market from big players in the Generative AI space very frequently. Let’s explore a few of the top LLM models in 2024.
This blog defines LLMs and attempts to classify and list the most popular ones ruling the Generative AI space in 2024. The article also carries out a comparative study of these Models based on various criteria such as Features, Source Code availability in the public domain, advantages, limitations, performance, efficiency, bias, fairness, and ethical considerations
What are Large Language Models?
Large Language Models are foundational AI models that are trained on a vast amount of data. These models can understand and generate content like speech, text, images, and music that resemble human-generated creatives.
LLMs are broadly classified as Open-sourced and Closed-sourced based on whether the training data and architecture is kept confidential within the organization or is open to the public. Here we have listed 20 Top LLMs, the companies owning them, and their features, advantages, and limitations.
We can broadly classify LLMs as Closed-Source and Open-Sourced. Here we have listed the Top LLMs of 2024 and a detailed comparative study of their features.
20 Top LLMs
First, let us delve into Closed-Source LLMs creating a huge impact in the Generative AI arena.
As mentioned earlier Closed-source models are owned by specific organizations and the training data employed, and model architecture details are confidential to the parent company.
Open AI LLMs
Open AI’s GPT Family of LLMs has created a significant impact in the Generative AI space. The recent ones are:
1. GPT-4,
2. GPT-3.5 Turbo,
3. GPT-4.o
GPT-4, GPT-3.5 Turbo released by Open AI are trained on internet data, codes, instructions, and human feedback, comprising more than 100 parameters. A recent addition to this Open AI family is the GPT-4.o model. This omnichannel model takes input in the form of text, image, speech, audio, and video to generate quality answers. Importantly, GPT-4.o accepts prompts in vernacular languages to generate relevant outputs.
Fine-tuning capabilities offered by all Open AI models help you customize your queries and outputs generated. You can play around with options called temperature and max_tokens to help you have greater control over the length of text generated and output style that suits your organizational or individual needs.
Summary:
| LLM Model | Salient Features |
| 1. GPT-3.5 Turbo | Customizable to specific needs and Cost-effective option Efficient for chat-based interactions and traditional sentence completion tasks |
| 2. GPT-4 | Excels in advanced reasoning and creative tasks Accepts and Processes both text and image prompts (Multi-modal capability) |
| 3. GPT-4.o | Capable of Multi-lingual content generation and Multi-modal capabilities accepting inputs in text, image, audio, and video formats. Faster and cheaper than the GPT-Turbo version |
Google AI LLMs
- Gemini: Google AI’s Gemini LLM is a multimodal model handling text, image, audio, and videos. Recently Gemini is released in two versions, Gemini 1.5 Pro and Gemini 1.5 Flash. While Gemini 1.5 Pro is suitable for general performance tasks with a huge context length of 1 million tokens, Gemini Flash is a Lite version designed to be fast, efficient, and cost effective. Overall Gemini models excel in search optimization by answering queries accurately from various advanced fields like data science, finance, and other scientific fields.
- LaMDA (Language Model for Dialogue Applications): This Closed-Sourced LLM is designed to handle conversational applications. It is suitable for answering open-ended questions and goes ideally with chatbots and virtual assistants.
- PaLM (Pathways Language Model): This LLM from Google AI is highly scalable to suit your rapidly changing needs. Moreover, this model is built upon the Google AI’s principle of training and tuning a Single Model for multiple tasks efficiently.
Summary:
| LLM Model | Salient Features |
| Gemini | Multimodal LLM that supports text, image, audio, video, and codeMulti-lingual LLM capable of handling prompts and generate answers in different languagesAdvanced coding capabilities understands programming codes in multiple programming languages and produce coding suggestions.Advanced Reasoning capabilities are a key striking feature of Gemini AI. |
| LaMDA | Open Domain conversations: capable of answering open-ended questionsExcels contextual understanding of the queries effectivelyLaMDA also possesses Multi-lingual handling capabilities |
| PaLM | Advanced Reasoning capabilities like logical reasoning, coding, and mathematics are key striking features of PaLM models.Advanced coding capabilities to work with programming languages is another feature of PaLM.Like Gemini and LaMDA, PaLM also exhibits Multi-lingual capabilities. |
Anthropic LLMs
Anthropic has several LLMs released in the Claude series. Prominent and recent ones are:
Claude 3.5 Sonnet, Claude 3 Opus, and Claude 3 Haiku. These models claim to outperform competitors in RAG tasks and eliminate hallucinations and biases in the generated content.
7. Claude 3.5 Sonnet: Boasts to outperform its predecessors and models like GPT-4.o in advanced logical reasoning and mathematical capabilities. Reviewers claim its DROP benchmark is very high for mathematical tasks. Also, it outperforms its predecessors in speed and is easily affordable. Interestingly it comes with real-time editing and building artifacts.
8. Claude 3 Opus: This model is designed to perform well in contextual reasoning tasks and RAG- (Retrieval-Augmented Generation) tasks as it can minimize hallucinations and biases. Like Claude 3.5 Sonnet, this model scores well in the DROP benchmark for mathematical tasks.
9. Claude 3 Haiku: This Anthropic LLM is aptly suited if you are constrained by budget. It is a cost-effective model that is affordable and efficient. Moreover, this model is mostly suited for closed-domain task resolutions.
Summary:
| LLM Model | Salient Features |
| Claude 3.5 Sonnet | Advanced Reasoning, Math and Coding capabilitiesPerforms well on RAG tasks by reducing biases and hallucinationsOffers real-time editing and building artifactsDROP benchmark is very high |
| Claude 3 Opus | Performs well on RAG tasks by reducing biases and hallucinationsDROP benchmark is very high |
| Claude 3 Haiku | Suitable for cost-effective solutions for closed-domain tasks |
Open-Source LLMs
These models are publicly available, allowing researchers and developers to access and build upon their architectures.
Meta AI LLMs
Meta AI pioneers open sourced LLM domain with its many interesting models. The most recent ones are:
10. LLaMA 3: This Open-sourced LLM from Meta is built with 7 billion to 70 billion adjustable parameters. Its multi-lingual capabilities to support 30 plus languages is a distinctive feature. The model is trained by using a massive dataset of more than 15 trillion tokens.
11. OPT (Open Pre-trained Transformer): Accessibility and Transparency are the key features of OPT LLM from Meta. This is a completely transparent open-sourced AI model with detailed documentation and training logs. Moreover, it can be easily fine-tuned for customized tasks demonstrating better accessibility in comparison to competitors. It is a flexible model with more than 175 billion adjustable parameters.
12. BlenderBot 3: This Meta AI model is built to cater for advanced conversational tasks. The model has a built-in long-term memory to remember and recall past interactions. Moreover, the model exhibits better safety and customizable features to handle conversational queries.
Summary:
| LLM Model | Salient Features |
| LLaMA 3 | Efficiency is its paramount feature with Improved tokenizer and grouped query attention |
| OPT (Open Pre-trained Transformer) | Transparency is its best feature with fully open-sourced and detailed training logs and documentation |
| BlenderBot 3 | Long-term memory to address advanced conversational tasks is its key feature |
Cohere LLMs
Cohere has come up with two versions of popular command LLMs.
13. Command R: This LLM is suitable to handle simple RAG tasks and single-step tool use. The model scores well on efficiency parameters with low latency and high throughput. The model is built to perform exceptionally in natural language understanding and generation. Finally, if you are constrained by financial commitments this model is affordable.
14. Command R Plus: The model is inherently built to handle complex RAG tasks and multi-step tool usage. Optimized for multi-language handling capabilities. It also has advanced features like cross-lingual task-handling capabilities and RAG citations.
Summary:
| LLM Model | Salient Features |
| Command R: | Ideal for simple RAG tasks and single-step tool useHighly efficient in Natural Language Understanding (NLU) and Natural Language Generation (NLG)Affordable/ Cost-effective |
| Command R Plus: | Ideal for advanced RAG tasks and multi-step tool useMulti-lingual support, long context length handling capabilities, cross-lingual task handling and RAG citations |
Mistral AI
Mistral AI is a renowned name in developing Open-sourced LLMs. Its state-of-the-art LLM is Mixtral 8x22b Instruct-v0.1. Here are its salient features:
15. Mixtral 8x22b Instruct-v0.1: This open-sourced LLM is built upon Sparse Mixture-of-Experts (SMoE) Architecture using 39 billion active parameters out of 149 billion parameters. It is optimized to be cost-effective and efficient. The model offers a function-calling feature to help software development and tech stack modernization. It also offers a 64k context window for easier information recall from huge documents. The model comes with multi-lingual support and advanced coding and programming language proficiency.
Other Prominent Open-Source LLMs
16. Gemma 2 27B Instruct: It is an advanced LLM developed by Google. Here are its salient features and technical advancements:
- The model is built using 27 billion adjustable parameters to ensure it is capable of complex NLU and NLG.
- It is trained by employing 13 trillion tokens spanning the internet, math world, and coding documents
- The model supports a context length of 8K plus tokens to handle longer text lengths.
The model incorporates technical advancements in the form of sliding window attention (for better quality content generation), Logit soft capping (to improve training stability), and knowledge distillation to train smaller models from larger teacher models.
17. Nemotron-4 340B Instruct: It is an LLM developed by NVIDIA. Here are its salient features and technical advancements:
- The model is built using 340 billion parameters making it as one of the powerful and largest LLMs.
- It is trained by employing 9 trillion tokens spanning the internet, coding languages, and vernacular language documents
- The model supports a context length of 4K plus tokens to handle longer text lengths.
This model incorporates technical advancements in the form of Grouped Query Attention (GQA)– to enhance the model’s ability to stay focused on relevant parts of input), Rotary Position Embedding (RoPE) helps the model understand the positional relationship between tokens and supervised fine-tuning (SFT) helps fine-tune the models with human-annotated data.
18. Qwen2 72B Instruct: It is a sophisticated LLM developed by Alibaba. Here are its salient features and technical advancements:
- The model is built using 72 billion parameters making it capable of contextual text generation and understanding.
- It is trained using a huge data set, that includes 27 languages to enhance its multi-lingual capabilities
- The model supports a context length of 13K plus tokens to handle longer text lengths.
This model incorporates technical advancements such as Transformer Architecture Supervised-Fine Tuning (SFT), Direct Preference Optimization (DPO), and Improved Tokenizer.
19. DeepSeek-Coder V2 Instruct: It is an advanced LLM developed by DeepSeek AI. Here are its salient features and technical advancements:
- The model uses 236 billion parameters, enabling complex code generation and understanding.
- It is trained using a customized data set, of 6 trillion tokens, enhancing its mathematical and coding reasoning capabilities
- The model supports a context length of 128K tokens to handle longer text lengths.
This model incorporates technical advancements such as a Mixture of Architecture, Enhanced Code and Math reasoning, Multiple Programming Language support, and Supervised Fine Tuning (SFT).
20. Yi 1.5 34B Chat: It is a cutting-edge technology LLM model developed by 01.AI. Here are its salient features and technical advancements:
- The model uses 34 billion parameters, enabling complex code generation and understanding.
- It is trained using a huge data set, of 500 billion tokens that is tuned with 3 million diverse samples.
- The model supports a context length of 4K, 16K, and 32 K tokens to handle extensive inputs and maintain coherence for long texts.
This model incorporates technical advancements such as a Transformer Architecture, Improvised Tokenizer, Supervised Fine Tuning (SFT), and Direct Preference Optimization.
Common Challenges and Parameters Used in Evaluating LLMs
There is no one-size-fits-all strategy to evaluate Large Language Models. You can consider vivid parameters while evaluating, their strengths and weaknesses.
The first and foremost parameter is Performance. In this method, you can analyze LLMs based on Benchmark scores or Task-Specific performance measurements. Measuring the ‘Efficiency’ is another way to evaluate LLMs. Further, you can compare Model Architectures to arrive at a decision. The Training data employed in building the models is another parameter that plays a vital role.
In addition, Cost, Bias Fairness, and Ethical consideration are foundational pillars for evaluating not only LLMs but any generic AI/ML models.
