An open-source alternative to ChatGPT has two parts: an open-weight model (such as Llama, Qwen, Mistral, Gemma, DeepSeek or OpenAI’s gpt-oss) and an app that runs it, such as Ollama, LM Studio, Jan or Open WebUI. Together they give you a private chatbot on your own computer with no subscription and no message limits.
Why choose an open-source chatbot
The main reasons are privacy, cost and control. Nothing you type leaves your machine, there are no usage caps, and you can pick or fine-tune the model you want. The trade-off is that you supply the hardware, and the best open models still trail the top hosted assistants on hard reasoning tasks.
Open models worth trying
| Model family | Made by | Good at | License type |
|---|---|---|---|
| Llama | Meta | General chat, wide tool support | Llama community license |
| Qwen | Alibaba | Multilingual, coding, many sizes | Mostly Apache 2.0 |
| Mistral / Mixtral | Mistral AI | Fast, efficient, European | Apache 2.0 for open releases |
| Gemma | Small, efficient models | Gemma terms | |
| DeepSeek | DeepSeek | Reasoning, math, code | MIT for recent releases |
| gpt-oss | OpenAI | Reasoning, tool use | Apache 2.0 |
“Open source” is used loosely here. Most of these are open-weight models: you can download and run them, but the training data is not published, and licenses vary. Check the license for the exact version before commercial use.
Apps that run open models on your computer
Ollama is a command-line tool that downloads and runs models with one command and exposes a local API, so other apps can use it. LM Studio is a desktop app with a model browser and a chat window, the easiest start for non-developers. Jan is another desktop app focused on offline use. Open WebUI gives you a ChatGPT-style web interface on top of Ollama, with chat history, document upload and multiple users.
A simple setup in four steps
- Install LM Studio or Ollama.
- Download a mid-sized model that fits your memory, for example a 7B to 14B parameter version of Qwen or Llama.
- Start a chat and test it on a task you do every day.
- If it is too slow, choose a smaller or more heavily quantized version; if it is too weak, try a larger one.
What hardware you need
Model size decides the hardware. As a rough guide, a quantized 7B to 8B model needs about 8 GB of free memory, a 14B model about 12 to 16 GB, and 70B-class models need 40 GB or more, which means a high-end GPU or a Mac with a lot of unified memory. Apple Silicon Macs are popular for this because the GPU shares system memory.
Limits to expect
Local models are usually slower than hosted services, have no built-in web search unless you add it, and the small ones make more factual mistakes. They also lack the polish of the big apps: voice, image generation and file handling depend on the interface you choose. For many everyday writing and coding tasks a good 14B model is enough; for difficult analysis a hosted model is still stronger.
Hosted alternatives if you do not want to self-host
If running models yourself is too much work, the hosted ChatGPT alternatives such as Claude, Gemini and Perplexity are easier, and several providers let you use open models in the browser. For an overview of the leading assistants see the best AI chatbots, and to understand what these models are, read what a large language model is.
Frequently asked questions
Is ChatGPT open source?
No. ChatGPT itself is a closed product. OpenAI has released some open-weight models (gpt-oss) that you can run yourself, but the models behind the ChatGPT app are not open.
Can I run an open-source chatbot without a GPU?
Yes, small models of a few billion parameters run on a modern laptop CPU or an Apple Silicon Mac, but they are slower and less capable than large hosted models.
Are open-source models free for commercial use?
It depends on the license. Some use permissive licenses such as Apache 2.0 or MIT; others, like Meta's Llama license, add conditions. Read the license of the exact model before using it in a product.