Ollama vs LM Studio vs Jan vs GPT4All

#In short: which tool for which profile

The right choice depends on what you want to do with the model: chat with it in a window, call it from a script or application, or experiment with many models. The table below summarizes the four tools; the following sections justify each row using the official documentation.

#The criteria that truly distinguish these tools

Five-star ratings are easy to write and impossible to verify; this guide prefers observable criteria in the documentation. Seven questions are enough to narrow it down.

Where do the computations run?

All of them run models on your machine. Jan and LM Studio also offer optional cloud models; if you want a strictly local tool, make sure these options remain disabled.

What do you want to call?

An application may need a server compatible with the OpenAI API: Ollama, LM Studio, and Jan provide one, and GPT4All also offers an API server in its documentation.

What hardware?

LM Studio announces llama.cpp and MLX engines, the latter for Apple Silicon Macs. Jan also supports llama.cpp and MLX. Ollama covers NVIDIA, AMD, and Apple Silicon.

Which license?

MIT for Ollama and GPT4All, Apache 2.0 for Jan. LM Studio is distributed by Element Labs under terms of use and is not presented as open source.

Should you browse a catalog?

LM Studio and Jan download models from the app; Ollama uses its library and the pull command.

What is the project's activity?

Ollama et Jan publient régulièrement. GPT4All n'a pas eu de version depuis février 2025, ce qui compte pour les nouveaux modèles.

What professional use case?

Review each tool's terms of use before deploying it in a business, especially for LM Studio.

#Ollama: the engine other tools call

Ollama is distributed under the MIT license, and its repository was updated on the day of this check. It installs a local server on port 11434 and is controlled from the command line: ollama run, ollama pull, ollama list. Its strength is its ecosystem: countless interfaces and coding tools know how to communicate with Ollama, and its API is also available in formats compatible with OpenAI and Anthropic, according to the official documentation.

The “no interface” criticism is outdated: since July 2025, the macOS and Windows versions have included an app for downloading and chatting with models, with file drag-and-drop and image support for multimodal models. On Linux, usage remains centered on the terminal and third-party interfaces such as Open WebUI, compared in a separate guide.

Strengths

One-command installation, local API, integration ecosystem, Modelfile for locking in a configuration, runs as a service on a server.

Limitations

The catalog is the Ollama library; the chat app is more streamlined than LM Studio; no fine-grained inference settings in a window.

Who it's for

Developers, integrators, homelabs, anyone who wants a service that runs in the background.

#LM Studio: the full application, now with an agent

LM Studio remains the most convenient option for exploring models from a single window: search, download, chat, settings, and presets. Its September 2026 home page now highlights Bionic, an agent for documents, code, and automations that works with local models and, optionally, hosted models. The inference engine is based on llama.cpp and MLX.

As for plans, Free costs 0 dollars and includes the Bionic agent, local model execution with llama.cpp and MLX, offline voice transcription, and LM Link for up to five devices. The Bionic+ plans (20 dollars per month) and Pro (100 dollars per month) add access to open models hosted in the United States, with zero data retention according to LM Studio. These cloud offerings are optional.

Strengths

Richest interface, two engines (llama.cpp and MLX), OpenAI-compatible server, documented headless mode, per-model settings.

Limitations

The application is not presented as open source; the offering is oriented toward cloud services, so check its settings if you want 100% offline use.

Who it's for

For those who want to test lots of models from an interface, without using a terminal.

For a getting-started tutorial, see the LM Studio guide for beginners; the direct showdown between LM Studio and Ollama has its own page.

#Jan: open source, local, and cloud in the same application

Jan est publié par Menlo Research. Le README et le fichier de licence de son dépôt indique la licence Apache 2.0, et non l'AGPLv3 que l'on trouve encore dans d'anciens comparatifs. Sa dernière version publiée au moment du contrôle, la 0.8.4, a été publiée le 23 juillet 2026, ce qui la place parmi les projets actifs.

The app aims to be an open-source replacement for ChatGPT: local models via llama.cpp (and MLX on Mac), connections to cloud providers such as OpenAI, Anthropic, Gemini, Groq, Mistral, or OpenRouter, projects, assistants, and an OpenAI-compatible local API server on 127.0.0.1:1337. You can therefore use a local model for your sensitive documents and a cloud model for everything else, in the same interface.

Strengths

Permissive license, interface similar to an online assistant, local server, local/cloud hybrid, MCP extensions.

Limitations

Less fine-grained tuning than LM Studio for inference; narrower catalog; younger project.

Who it's for

People who value an open application and want a single place for local and cloud workloads.

The dedicated guide to Jan covers installation and the initial settings.

#GPT4All: simple, but last version in February 2025

GPT4All, from Nomic, targets beginners: download an installer, start chatting, and use LocalDocs to query your files without configuring RAG. Its repository is under the MIT license. But the latest release, 3.10.0, dates from February 25, 2025.

This does not mean the application no longer works, but that it does not keep up with new models and formats as quickly as the others. For a modest workstation without a GPU where you want to chat with your documents, it remains a viable option; to test the latest models, prefer Ollama, LM Studio, or Jan.

#API and integration: what each tool exposes

If another piece of software needs to call the model (code editor, automation, in-house application), the API matters more than the interface. All the tools below are configured in the application; the first three are compatible with existing OpenAI clients, avoiding the need to rewrite code.

Change only the base address and model name in your client; the rest of the code does not need to change. The guide dedicated to LM Studio as an API server shows how.

#Can you use multiple tools? Yes, with a caveat

These tools don't interfere with one another: each has its own model directory and port. A common setup is to run Ollama as a service for scripts and code editors, LM Studio for trying new models, and a web interface such as Open WebUI for everyday use.

The drawback is disk space: models are not automatically shared between tools. The same 5 GB model downloaded in three applications uses 15 GB. Choose one primary tool and use the others only occasionally, deleting unnecessary models. Ports to monitor if two servers run at the same time: 11434 for Ollama, 1337 for Jan.

#Decision table by situation

#Decide in 30 seconds

You are a developer

Ollama: one command, one API, one service.

You want to test 20 models this weekend

LM Studio: search and download from the interface.

You want an open application

Jan: Apache 2.0, local and cloud.

You want the simplest possible option

Ollama with its chat app, or LM Studio: two files to install, no command line.

You're hesitating

Install Ollama and LM Studio; disk space is the only cost, and you'll remove one of them after a week.

• Ollama vs. LM Studio: the detailed showdown
• Graphical interfaces for Ollama: comparison
• Compare inference engines
• Jan: the open-source alternative to ChatGPT
• LM Studio for beginners
• Install Ollama in 5 minutes

• Source: Jan's repository
• Source: Ollama repository
• Source: LM Studio pricing
• Source: GPT4All repository