Verdict: pick Jan if you want a free, open-source chat app that runs models on your own computer and can switch to a cloud model when you choose. If you care more about polish and a wide model catalogue than open source, LM Studio is the easier first install.
Other good fits
Teams that require open-source software (Apache 2.0) they can inspect and redistribute.
Trying a local model first, with the option to add a cloud provider key for harder tasks.
+ Pros
Read pros
Jan is a desktop chat app, like ChatGPT, except the model runs on your own computer. You download a model once inside the app, and after that your prompts and documents don't leave the machine. When a task needs a stronger model, you can add a key for a cloud provider (OpenAI, Anthropic, Google, Mistral, Groq, OpenRouter and others) and pick it per conversation. Jan is open source under Apache 2.0 and is built on llama.cpp.
Pick it when
You want a private first draft: meeting notes, a grant paragraph, an email reply, without sending the text to a cloud service.
Your organisation prefers open-source software it can audit.
You want one app for local and cloud models, and you want to decide per chat where the text goes.
You later want other programs to call the same model: Jan can run a local server that speaks the OpenAI API format.
− Cons
Read cons
Answer quality depends on the model your hardware can hold. A 7B model on a laptop is fine for rewording and summarising, but it is well behind the big cloud models on reasoning and long documents. Cloud providers, web search and connector tools are opt-in, but once you switch them on, text does leave the machine, so decide per conversation. The local API server has no key unless you set one, so keep it on 127.0.0.1.
Pick something else when
You want the most polished model browser and clearer hardware hints: use LM Studio.
You want a local model that scripts and other apps call in the background, without a chat window: use Ollama.
A whole team should share one assistant with logins: run Open WebUI on a server.
Avoid if
You want the smoothest model browser and hardware guidance; LM Studio is more polished.
You need a shared, multi-user assistant for a team; Open WebUI on a server fits better.
Requirements
Install Jan Desktop for macOS, Windows or Linux, download a model and choose one that Hub marks as fitting the available hardware. That guidance is not a measured performance guarantee.
First run
Documentation-based suggestion — not run here.
Read first run
Summarise a protocol without it leaving your laptop. Follow A model on your own laptop for the background.
Install Jan for macOS, Windows or Linux from jan.ai.
In the Hub, pick a small instruction model that Jan marks as fitting your hardware (a 3B–8B model on a 16 GB laptop).
Turn off Wi-Fi to prove to yourself that the model is local.
Paste a one-page lab protocol or internal procedure and ask: "List the steps as a numbered checklist. Keep every quantity and unit exactly as written."
Compare the checklist line by line with the original.
It worked if every quantity, unit and step matches the source, and the chat still answered with the network off. If the model drops or changes numbers, try a larger model or keep this task for a cloud model.
Integration
Local models run on the machine by default; cloud providers need their own API keys. Web search, agents and MCP tools are separate features, so do not enable them when a task must remain self-contained.
Check the result
Compare the draft with the supplied facts and reject invented details. A local model can still make unsupported claims.
Links & evidence
Documentation-based · not hands-on · Sep 23, 2026
Rests on Jan's website, desktop quick start, local API server page and GitHub README (licence, system requirements, v0.8.4), all checked 23 September 2026. The curriculum team has not installed or run Jan for this review; the first project is a suggested exercise.
Jan website · Sep 23, 2026 — Free and open source; local and online models; current release v0.8.4.
Jan GitHub repository · Sep 23, 2026 — Apache 2.0 licence; macOS 13.6+, Windows 10+, Linux; RAM guidance of 8 GB for 3B, 16 GB for 7B, 32 GB for 13B models; built on llama.cpp.
Jan Desktop quick start · Sep 23, 2026 — Platforms, model download with hardware-fit labels, cloud providers and connectors.
Jan local API server · Sep 23, 2026 — Default address 127.0.0.1:1337, OpenAI-style /v1 endpoints, optional API key.
Further review notes & alternatives
Costs and operating effort
Prices checked 23 September 2026: Jan is free, open-source software; there is no paid plan. You pay for the machine, and for any cloud provider whose key you add.
The GitHub README gives a rough guide for Apple computers: 8 GB of RAM for 3B models, 16 GB for 7B and 32 GB for 13B. On Windows and Linux an NVIDIA, AMD or Intel Arc GPU speeds things up a lot. Running it takes little effort: update the app now and then, and delete models you no longer use, because each one takes several gigabytes of disk.
Alternatives and trade-offs
LM Studio wins on polish, hardware guidance and Apple MLX support. It is free for work use but closed source.
Ollama wins when the model should run as a background service for other tools, and it offers optional paid cloud models.
AnythingLLM wins when your main job is chatting with a folder of documents rather than general drafting.
Chat with locally running models through an offline-first desktop app.
Download models from its built-in Hub and keep work on your machine.
Add a cloud key per thread when local inference is insufficient.
Jan (janhq) · free · self-host
offline chat
desktop assistant
local llm
privacy focused
model hub
hybrid cloud
open source
talk-think
draft-docs
View sourced imageOfficial Jan desktop documentation screenshot.
janhq/jan
Apache License 2.0 repository documentation; the original license notice is retained.
Jan
Copyright 2025 Menlo Research
This product includes software developed by Menlo Research (https://menlo.ai).
Licensed under the Apache License, Version 2.0 (the "License");
You may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Attribution is requested in user-facing documentation and materials, where appropriate. Converted on 15 October 2026 to a 1600px-wide WebP (quality 80) for faster page loads; the original image remains in the media library.
