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 2026-08-12 · 5 commercial and 11 open models
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.
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.
| Model | Current generation | Initial release | Developer | Instruct / RLHF | Source |
|---|---|---|---|---|---|
| Gemini | Gemini 3.5 | 2023-03-21 |  Google |
Yes | Announcement |
| ChatGPT | GPT-5.6 | 2022-11-30 |  OpenAI |
Yes | Announcement |
| Claude | Claude Opus 5 | 2023-03-14 |  Anthropic |
Yes | Announcement |
| Grok | Grok 4.5 | 2023-11-05 |  xAI |
Yes | Announcement |
| Muse | Muse Spark 1.1 | 2026-04-08 | Meta Superintelligence Labs | Yes | Announcement |
| No models match. | |||||
Open and open-weight models give teams more control over deployment, customization, and data handling. The ecosystem now ranges from compact models that run on phones and laptops to frontier-scale mixture-of-experts families such as DeepSeek, GLM, Kimi, MiniMax, and Qwen. Always review each model's license: "open weight" does not necessarily mean the Open Source Initiative definition of open source.
| Model | Current generation | Initial release | Developer | License | Instruct / RLHF | Source |
|---|---|---|---|---|---|---|
| Command | Command A+ | 2021-11-15 |  Cohere |
Apache 2.0 | Yes | Announcement |
| DeepSeek | DeepSeek V4 | 2023-11-29 |  DeepSeek |
MIT | Yes | Announcement |
| Gemma | Gemma 4 | 2024-02-21 |  Google |
Apache 2.0 | Yes | Announcement |
| GLM | GLM-5.2 | 2022-08-04 | Z.ai | MIT | Yes | Announcement |
| Kimi | Kimi K3 | 2023-10-09 | Moonshot AI | Kimi K3 License | Yes | Announcement |
| Llama | Llama 4 | 2023-02-24 |  Meta |
Llama Community License | Yes | Announcement |
| MiniMax | MiniMax M3 | 2025-01-15 | MiniMax | MiniMax Community | Yes | Announcement |
| Mistral | Mistral 3 | 2023-09-27 |  Mistral AI |
Apache 2.0 | Yes | Announcement |
| Nemotron | Nemotron 3 Ultra | 2025-12-15 | NVIDIA | OpenMDW-1.1 | Yes | Announcement |
| Phi | Phi-4 | 2023-06-20 |  Microsoft |
MIT | Yes | Announcement |
| Qwen | Qwen 3.8 | 2023-09-13 | Alibaba Cloud | Mixed; Apache 2.0 open weights and hosted previews | Yes | Announcement |
| No models match. | ||||||
Side-by-side comparisons of any two models in the index.
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:
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.
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 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
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:
LLM librariesAs these systems are evolving rapidly, we do not feel comfortable passing judgement on which LLM is best. However, a combination of cloud vs. ability to self-host, pricing, and qualitative evaluation should be enough to prune the index down to a small number of possible options.
If you'd like to compare tables of numbers, use Artificial Analysis for independently measured intelligence, speed, latency, context, and cost, and OpenRouter Rankings for live usage and task-specific adoption. No single leaderboard captures every production use case, so validate finalists on your own prompts, latency targets, and safety requirements.
An ad hoc (but usually effective) approach is to check the sentiment on X (Twitter) and the LocalLLaMA Reddit group on different LLMs.
Contact us with a brief description of your use case if you'd like for us to make a snap assessment. Depending on your requirements, a smaller, custom language model may even be the best option.
Please see the question above on how to evaluate different LLMs. Some factors you'll likely wish to consider include (1) compute costs, (2) data security requirements, (3) whether a custom language model would work best, (4) latency requirements, and (5) internal expertise available to set up the deployment.
LLMs are now available for different languages (Chinese, English, etc.) as well as different industries (healthcare/biomedical, legal, software coding, financial services, and cybersecurity). We plan to release comparisons for different languages and industries soon; in the meantime, feel free to contact us regarding your specific need.
Training an LLM is expensive (even a 7B parameter model can take hundreds of GPUs to train in reasonable amounts of time -- weeks to months). Although libraries and scaffolding for training LLMs are being rapidly released, the process can still be finicky, especially if you do not have experience training NLP models. If you need guidance on getting started, it's more than likely you should instead be finetuning one of the existing commercial LLMs using their finetuning guides and/or finding a LLM that roughly matches your use case.