Jan vs LM Studio (2026): Open-Source and Closed-Source Desktop AI Apps Compared

Jan vs LM Studio: both are free desktop apps that download open models from Hugging Face and run them on llama.cpp, plus MLX on Apple silicon, with an OpenAI-compatible local server and MCP tools. Jan is Apache-2.0 open source from Menlo Research and also connects to cloud APIs. LM Studio is closed source, with API tokens and continuous batching on its server, a headless mode and the Bionic agent.

People comparing the two usually want the same thing: a private way to chat with open models on their own computer, offline when needed. The apps get there with different licences, different server features and different defaults for data collection. Everything below comes from each project’s documentation, GitHub repository, terms and privacy policy, checked 14 October 2026, when the current versions were Jan 0.8.6 (released 9 October) and LM Studio 0.4.26.

Which model to run is a separate question: our Mac memory guide and VRAM tier guide answer it for Apple silicon and PC graphics cards.

Jan vs LM Studio at a glance

Who builds each app and what the licences allow

Jan comes from Menlo Research. Its code moved from AGPL to the Apache License 2.0 in May 2025, and the licence file asks for attribution in user-facing documentation where appropriate. Apache 2.0 lets you use, modify and redistribute the app, commercially included, provided you keep the licence and notices. A company that wants to audit the code, patch it or ship its own internal build can do so.

LM Studio is made by Element Labs, Inc. The desktop app has been free for home and work use since 8 July 2025, but it is not open source. Its app terms, in the version of 23 August 2026, license it for personal and internal business purposes only and rule out sublicensing, service-bureau or software-as-a-service use, modification and reverse engineering. The parts you build against are open: the lms CLI, the Python and TypeScript SDKs and the MLX engine are MIT-licensed.

For ordinary internal use either licence works. The difference shows when you want to change the app, embed it in something you distribute, or run it as a service for other people.

Platforms, engines and model downloads

On a Mac the two are closer than they look. Both support Apple silicon only: Jan’s macOS guide and LM Studio’s system requirements each say Intel Macs are not supported. Jan needs macOS 13.6 or newer and LM Studio macOS 14 or newer. Jan’s rough sizing is 8 GB of memory for 3B models, 16 GB for 7B and 32 GB for 13B; LM Studio recommends 16 GB or more.

For GGUF files, both use llama.cpp. Jan’s engine updates with the app, and if a model misbehaves you can run your own llama-server and connect it as a custom endpoint. LM Studio downloads and swaps runtimes without a full app update.

MLX is where they part. LM Studio has shipped an MLX engine since version 0.3.4 in October 2024. Jan’s MLX engine needs macOS 14, runs models in MLX-Swift format, and is labelled experimental: embeddings are unavailable, reasoning output is not yet shown separately, and some newer architectures fail to load.

On Windows and Linux, LM Studio offers ARM builds and Jan does not. Jan’s Windows build targets x64 PCs, with GPU acceleration on NVIDIA, AMD and Intel Arc graphics, and its Linux build comes as a .deb, an AppImage or a Flathub package.

Both apps download from Hugging Face. Jan’s Hub tags each download Fits, May be slow or Won’t fit for your hardware, without downloading anything to decide, and gated models need a Hugging Face token in its settings. LM Studio’s search takes keywords, user/model names or full Hugging Face URLs, and its docs suggest a 4-bit quantization or higher when your machine can run it.

Local API server and parallel requests

Both apps can act as an OpenAI-compatible server for scripts, editors and agents, with different defaults.

Jan also enables CORS by default so browser apps on other domains can call it; its docs suggest switching that off if only scripts will use the server. A quick test of each:

# Jan: start the server in the app first; drop the auth header if no key is set
curl http://127.0.0.1:1337/v1/chat/completions \
  -H "Authorization: Bearer $JAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "<model-id>", "messages": [{"role": "user", "content": "Hello"}]}'

# LM Studio
lms server start
curl http://localhost:1234/v1/models

For a Linux machine without a display, LM Studio’s llmster daemon runs the same server headless, and its docs include a systemd setup. Jan’s CLI, which the desktop app installs, can serve a model at localhost:6767/v1 and keep it running in the background with --detach, but it is set up through the desktop app rather than as a standalone server install.

MCP tools, telemetry and privacy settings

MCP. Both apps are MCP hosts, so a model can call tools through servers you add. LM Studio gained this in version 0.3.17 in June 2025; local and remote servers go into an mcp.json file in Cursor’s notation, and its docs warn that some MCP servers can run arbitrary code, read local files and use your network. Jan adds servers under Settings > MCP Servers with a name, command, arguments and optional environment variables. By default you approve each tool call from a card that shows its arguments, unless you switch on Allow All MCP Tool Permissions, and Jan’s docs warn about prompt injection through tools.

Telemetry. Jan collects nothing until you choose your preferences at first launch. If you allow analytics, it records active users and retention against a random ID, processed by PostHog in the EU, and you can change the choice in settings later. LM Studio’s privacy policy says the app has no telemetry or user-specific tracking; update checks send the app version and build, the operating system and your IP address, and model searches are collected in anonymised form.

Chats and documents. With local models, neither app sends conversations anywhere. LM Studio’s policy says messages, chat histories and documents are never transmitted from your system, and Jan’s privacy page says local models keep everything on the device. Cloud providers in Jan are a different matter: your messages go to that provider under its terms. LM Studio’s own hosted models, sold with Bionic plans, run on US servers under a zero data retention commitment.

For a sensitive setup, use local models only, leave analytics off in Jan, keep the server on 127.0.0.1, and set an API key or token before binding it to the network.

Jan vs Ollama

Ollama is the third name in most of these comparisons, and its design differs from both. It is an MIT-licensed model service: a command line and background server with an API on 127.0.0.1:11434, plus a desktop app for macOS and Windows since July 2025. Its OpenAI-compatible endpoints cover chat and text completions, models, embeddings and, since v0.13.3, a stateless Responses API. By default it handles one request per model at a time and queues up to 512 more, and its local API ignores API keys, so anyone who can reach the port can use it.

• Choose Jan for a chat window with a model hub, cloud providers and custom assistants, without touching a terminal.
• Choose Ollama for a service that other software calls, scripted model management, or a Linux server.
• Use both. Jan’s custom endpoint setting accepts any OpenAI-compatible server and lists Ollama among its examples, so Ollama can run the models while Jan provides the interface.

Ollama also offers cloud-hosted models; setting OLLAMA_NO_CLOUD=1 turns those features off. If you are weighing LM Studio against Ollama instead, our runtime comparison covers that pairing, and our guide to Ollama for multiple users covers serving a team.

Using Jan or LM Studio at work

Both apps can be used at work, with different paperwork. Jan needs nothing beyond its Apache licence. LM Studio is free for internal business use under its app terms; its Hub offers organisations with centralised billing for inference credits, team subscription plans are listed as coming soon, and an enterprise page takes sales enquiries. When LM Studio made the app free for work in July 2025, it pointed companies that need single sign-on, model and MCP gating and private collaboration to an enterprise plan.

Jan’s website lists no team or enterprise plan. Its GitHub organisation hosts Jan Server, a separate Docker Compose platform with an OpenAI-compatible API, MCP tools and Keycloak sign-in. Its repository was last updated in June 2026, its README shows an MIT badge although we found no licence file in the repository, and its quick start runs the Keycloak console with an admin/admin login that you should change before anything else.

Either way, the desktop app is installed and configured per person. Before a rollout, check:

1. That your AI policy allows the app, and which models staff may download; unapproved tools are at the heart of shadow AI.
2. That the local server stays on 127.0.0.1, or has a key or token, before anyone binds it to 0.0.0.0.
3. Which cloud providers, if any, staff may connect in Jan, since prompts then go to that provider.
4. The model licences: the app’s licence does not cover the weights you download, and our open-weight licensing guide lists what to check.

For one browser-based chat interface shared by a team instead of per-person installs, see our comparison of self-hosted chat interfaces.

Jan or LM Studio: who should pick which

LM Studio’s agent app is covered in our LM Studio Bionic guide, and if you want to fine-tune rather than only run models, see Unsloth Studio against LM Studio.

How VDF AI fits

Jan and LM Studio solve the problem of one person running a model privately. Extending that to a department raises questions a desktop app is not built to answer: who signs in, who can reach which model and which documents, and what was asked.

VDF AI is the governed on-premises alternative for that step, with chat, private RAG over company data and governed agents on your own servers or in VDF AI Cloud. On-premises deployments include Microsoft Entra ID single sign-on that maps security groups to roles, and other identity providers connect through an SSO-aware reverse proxy. Role-based access control comes with every plan, and audit logs record activity. VDF AI does not install, manage or connect to Jan or LM Studio.

Teams that already run Ollama can keep it: VDF AI Router accepts an Ollama endpoint as a model deployment and, in air-gap mode, routes only to local models. For a governed chat interface, see VDF AI Chat.