The LLM Index

A list of large language models (LLMs), including open-source and commercial offerings, comparisons of each, and libraries for working with LLMs. Find the best large language models for your use case.

Last updated: 2025-01-27

Large language models (LLMs) are powerful machine learning systems that for many use cases can now understand and compose text at a near human level. They are currently the leading subcategory of Foundation Models, large models pre-trained using unsupervised methods on enormous datasets that can be tuned to perform a range of tasks. Due to their capabilities, individuals as well as businesses are now regularly using LLMs through popular platforms such as ChatGPT, Gemini, and Claude. This index is a list of LLMs and their properties and functionality. For a snapshot "evolutionary tree", we recommend Figure 1 in this paper.

Note that LLMs are being developed and released at a frantic clip. While we'll try and keep this LLM list up-to-date, we may have missed some recent releases. Please contact zxie[at]sapling.ai with any significant updates.

Leaderboards

Many reading this will be most interested in which LLM will perform best for their use case. While this can depend on the evaluation method and things are changing rapidly, we recommend the following resources to help make that assessment:

• Chatbot Arena (LMSYS Org) and "Leaderboard" tab on LMSYS
• Open LLM Leaderboard (Hugging Face)

Commercial LLMs

Most software businesses are familiar with cloud service providers (CSPs) that provide scalable computing resources. With the growth of ChatGPT, new LLM cloud services have been launched by familiar incumbents as well as well-capitalized startups.

Open Source LLMs

Assuming you have the ability to run models with billions of parameters, using an open source model is one way to ensure control of your systems and data. The open source LLM ecosystem is moving quickly, most notably after the release of Meta's Llama models (including Llama 2/3) followed by the release of Mistral's models. In parallel to the release of powerful models trained on large corpora of data and instruct-finetuned by research groups, a community of developers has also made it possible to run larger and larger models in real-time on commodity hardware—even, for example, on a consumer laptop.

Comparisons

Commercial LLM Comparison

Side-by-side comparisons of different commercial LLM offerings.

Open Source LLM Comparison

Side-by-side comparisons of open source LLM options.

Scroll right to see the full table.

By Industry

The most widely known LLMs are general-purpose, i.e. they can perform a variety of tasks across different topics and commercial industries. However, sometimes users and businesses may want an LLM trained on data from a specific industry, reducing the amount of prompting required for it to behave in an industry-relevant way and constraining its behavior. Also known as domain-specific LLMs, these language models may be easier to deploy to production for many businesses or serve as a better foundation for fine-tuning.

Coming Soon

LLMs for biomedical, healthcare, finance, academia, and eCommerce.

By Language

LLMs are often trained on massive web crawls of text from various languages. Hence, often they are multilingual by default. However, there have also been LLMs trained specifically for languages besides English.

Coming Soon

Multimodal LLMs

Multimodal LLMs are LLMs that can process and generate not just text, but also other types of media, such as images, audio, and video. Most LLM platforms have multimodal support, most commonly to process documents such as PDFs as well as to generate images from text.

Coming Soon

Libraries

In addition to APIs, a number of developer libraries and SDKs have been released for working with LLMs. You can find Sapling's curated list of LLM libraries here:

  Libraries

Frequently Asked Questions