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LumaBrowser vs LM Studio, Jan, and Ollama

Last updated: September 8, 2026

These are the four names that come up when someone asks "how do I run AI on my own computer?" All four are free to use, all four run the same open models on the same engine underneath, and all four will get you a private chat. They differ in who they are built for and in what happens in the first fifteen minutes. We make LumaBrowser, so read this as an informed opinion rather than a neutral review, and check the sources yourself; links are at the end.

The short version

• Ollama is the developer's pick: a command-line tool and background service with a huge model library and a clean API. There is a desktop app now, but the culture is the terminal. If you are comfortable typing ollama run, it is hard to beat for plumbing.
• LM Studio is the enthusiast's pick: a polished desktop app with a model search, hardware fit hints, lots of settings, and a local server that mimics the OpenAI API. Free for personal use; closed source.
• Jan is the open-source desktop pick: a clean chat app that runs local models and can also talk to cloud providers, with an extension system. Fully open source.
• LumaBrowser is the "I do not want to choose anything" pick for chat, and the "one app instead of five" pick for people who also want images, roleplay, voice, games, or an agent that can use a real browser. Free, no account, no paid tier.

Side by side

This comparison reflects publicly available feature information gathered to the best of our knowledge from each project's public materials as of the date above. These tools move fast and we are a small team; if anything here is wrong, email [email protected] with the correction and a source and we will update the page.

If you are a beginner

The problem you will actually have is not "which app." It is "which model, and will it fit," followed by "why is it slow," followed by "it is dumber than ChatGPT and nobody told me." All four apps run the same models, so the differences that matter in week one are: does it pick the model for you, does it use your graphics card without you knowing what that means, and does it warn you what quality to expect.

LM Studio and Jan show you the model list with size hints, which is helpful if you know what a 7B or a Q4 is. Ollama expects you to know a model's name. LumaBrowser reads your hardware and picks, then shows you a one-sentence explanation and the download size; it also sizes the conversation memory to your machine instead of defaulting to a short one. That is the whole reason our beginner page exists.

If you want images, characters, or voice

None of the other three do image generation; you would pair them with a separate image tool (ComfyUI, Automatic1111, Fooocus, or Pinokio to install those), which is where most of the Python and driver pain lives. LumaBrowser installs the image engine and a model next to the chat model in the same wizard, and adds roleplay, voice, and small games on top. It is not the deepest image tool on the market; ComfyUI is far more capable if you want node graphs. It is the one that gets a first picture out in one session on a gaming PC. The Create page has model sizes and card requirements.

If you are building something

Ollama's API and model library are excellent and widely supported; if you want a local model behind your own code, it is a fine choice, and LumaBrowser adopts the models you already pulled with it by hardlink, so switching costs no re-download (what you keep and what changes). LM Studio's OpenAI-compatible server is convenient for the same job. What none of them provide is a browser: if your agent needs to open pages, click, fill forms, and read what is there, you add Playwright or a cloud browser. LumaBrowser is the browser, with an MCP server, REST API, and WebDriver and CDP endpoints, and the local model is in the same process. Builder hub.

If you have two GPUs and opinions

Ollama and LM Studio both expose a solid set of runtime settings, and llama.cpp itself exposes everything. LumaBrowser sits in between: Easy Setup is a default, and the Setup tab exposes placement modes, KV and weight quants, tensor split, MoE expert offload, RAM pinning, draft models, swappable runtimes including ik_llama and MLX, and GPUs borrowed from another machine. Every knob.

Honest verdict

• Live in a terminal and only want a model server: Ollama.
• Want a desktop app with a big settings panel and do not mind closed source: LM Studio.
• Want an open-source desktop chat app: Jan.
• Do not want to choose a model, or want images, roleplay, voice, games, or a browser your agent can drive in the same app: LumaBrowser.

Whatever you pick, try it on the hardware you own before buying anything, and read what to expect from a local model first so the first session does not surprise you.

Sources

• Ollama: ollama.com and its GitHub repository.
• LM Studio: lmstudio.ai and its documentation.
• Jan: jan.ai and its GitHub repository.
• LumaBrowser: this site, and the app itself.