The comparison of the best LLMs for businesses: OpenAI and open source alternatives

Artificial intelligence is one of the top technology trends disrupting businesses today, with large language models (LLMs) spanning virtually every sector. LLMs streamline language-related tasks such as content generation, translation, customer communication, and sentiment analysis.

If you’re interested in integrating an LLM into your business processes, read on. This guide explores the best LLMs for businesses and provides an ultimate comparison of large language models to help you make a winning choice.

What is a large language model?

Before we examine different types of LLMs, let’s define what a language model is. Simply put, a language model is a machine learning model that predicts and generates language. A common example is autocomplete. Language models operate by assessing the probability of a word, phrase, or sentence (referred to as tokens in this context) within a sequence.

Large language models differ in size from other language models. They are pre-trained on massive amounts of data, typically from the internet — leveraging statistical relationships from text documents. “Large” can refer to either the number of parameters (weights used to predict the next token in the sequence) or the number of words in the dataset. For instance, BERT and Google’s PaLM 2 have 110 million and 340 billion parameters, respectively.

The most advanced LLMs are built with transformer-based architecture, enabling them to understand basic grammar, languages, and knowledge. This architecture allows the use of hundreds of billions of parameters.

Types of large language models

Based on their availability and accessibility, LLMs can be categorized into two major groups: open source and closed source.

Open Source LLMs: These models have publicly available source code and pre-trained weights, which can be freely accessed, modified, and redistributed. For example, Hugging Face provides an open-source transformers library with a range of pre-trained LLMs that developers can easily integrate into their projects.

Closed Source LLMs: The source code and pre-trained weights of these models are not publicly available. Access to these models is restricted and typically requires licensing or subscription fees, like Google’s Gemini 1.5.

According to the specific needs they address, LLMs fall into four types:

1. Multimodal Models: Capable of integrating information from text, images, audio, video, and computer code, these models perform tasks from image classification to multimodal translation. OpenAI’s GPT-4, available via ChatGPT Plus, is an example.

2. Instruction-Focused Models: Designed to understand and generate text based on specific instructions, these models are useful for tasks such as code generation or sentiment analysis. For instance, Codex (formerly GitHub Copilot) generates code and offers context-specific suggestions.

3. Autoregressive or Generic Models: These models generate contextually relevant text by predicting the next token in a sequence based on the previous tokens. Examples include GPT-3 and XLNet.

4. Conversation-Focused Models: Trained to engage in dialogue, these models are optimized for chatbots, virtual assistants, and dialogue generation. Examples include LaMDA and DialoGPT.

Business use cases of AI language models

The capabilities of LLMs match language-related business needs such as communication and information processing. Common applications include:

• Content generation: Producing content like product descriptions, articles, marketing copy, and social media posts.

• Customer support and service: Automating customer support through chatbots and virtual assistants.

• Code generation: Assisting in writing code, making SQL queries, and designing websites.

• Data classification: Sorting and organizing data based on predefined criteria.

• Language translation: Real-time translation of documents, websites, and customer communications.

• Market analysis: Analyzing data to provide insights into customer sentiments and market trends.

Is OpenAI the best large language model developer?

OpenAI, renowned for its ChatGPT released in November 2022, is a leading LLM developer. The OpenAI API enables developers to integrate OpenAI models into applications, products, or services, supporting tasks like natural language understanding, translation, content generation, and more.

However, while specific OpenAI tools are open source, the underlying model and training data for GPT-4 are closed source, which can lead to significant costs for high-volume usage.

If investing in the OpenAI API isn't feasible, there are less expensive and even free open-source LLM alternatives.

What are the best models besides GPT-4?

Here are some top LLMs for businesses to consider:

• Falcon (TII): With 180 billion parameters, Falcon is a strong OpenAI alternative, available in versions for fine-tuning or as instruction-tuned models.

• Llama-2 (Meta): The second version of Llama, released for commercial use, trained on 40% more data than its predecessor. It includes Llama 2-Chat and Llama Code, optimized for conversation and code generation.

• LaMDA (Google): Used to power Google Bard, LaMDA is conversational AI trained on 1.56 trillion words, optimized for human-like text responses.

• BLOOM (BigScience): A 176 billion-parameter multilingual model created by over 1,000 AI researchers. It's known for its transparency and is suitable for multilingual content creation and translation.

• GPT-Neo and GPT-J (EleutherAI): Open-source models with versions ranging from 2.7 to 6 billion parameters, designed by researchers committed to open-source AI.

• AlexaTM (Amazon): A 20 billion-parameter model that outperforms GPT-3 on zero-shot learning tasks due to its encoder-decoder architecture.

The LLM models comparison

Choosing the best LLM depends on your unique business needs and circumstances. Here’s a comparison table of the top LLMs for businesses:

*Pre-trained weights; in almost all cases, the training code is open source.

Conclusion

Large language models offer transformative possibilities for businesses across many industries. By integrating an LLM into your application, you can automate tasks and engage with customers more effectively. However, the choice of LLM should depend on your unique requirements and business objectives.

We hope this list of large language models helps you find the best open-source LLM for your needs. If you need professional advice on implementing AI in your business operations, don’t hesitate to contact us. The team at TechWings has deep expertise and experience developing next-gen AI-powered products, and we’ll help you determine the most optimal options!