Jan

Jan is an open-source ChatGPT alternative that runs large language models 100% offline on your own computer, while letting you optionally connect to cloud models like GPT-5.5, Claude, Gemini, Mistral and DeepSeek from the same interface. We rate it 83/100 — the cleanest, most polished local LLM desktop app available right now, and a serious contender against LM Studio for anyone who values open source and privacy.

What is Jan?

Jan is a privacy-first desktop application built by Menlo Research that ships under an Apache 2.0 license. The project first appeared on GitHub in August 2023 and has grown to 42,000+ GitHub stars and over 5 million downloads across macOS, Windows and Linux as of April 2026. It uses llama.cpp and Apple MLX as its inference backends, wrapped in a Tauri-based UI that feels closer to ChatGPT than the developer-tool aesthetic of Ollama or Text Generation WebUI.

The pitch is simple: download the app, pick a model from a curated list (or paste any HuggingFace GGUF link), and start chatting. Nothing leaves the device unless you explicitly add a cloud provider. For developers, Jan also exposes an OpenAI-compatible REST API at localhost:1337, which means you can drop it in as a private replacement for the OpenAI SDK in any existing project.

Key Features of Jan

• Local model runner: Llama 3, Llama 4, Qwen, Gemma, Mistral, DeepSeek, Phi, GPT-OSS — pulled directly from HuggingFace as GGUF. The HuggingFace integration means tens of thousands of community quantizations work without manual setup.
• Cloud provider connectors: One-click access to OpenAI, Anthropic, Google, Mistral, Groq, MiniMax, OpenRouter and any custom OpenAI-compatible endpoint. Bring your own key — no usage markup.
• OpenAI-compatible local API: http://localhost:1337/v1 exposes the same chat-completions, models and embeddings endpoints as OpenAI. Drop-in replacement in code that already uses the OpenAI SDK.
• Model Context Protocol (MCP): Jan added MCP server support in v0.5, so models can call browser, filesystem and custom tools — turning Jan into an agent runtime, not just a chat client.
• Custom Assistants: Create per-task assistants with their own system prompts, model, temperature and tool access — the local equivalent of OpenAI Custom GPTs.
• Hardware acceleration: CUDA on NVIDIA, Vulkan/HIP on AMD, Intel Arc support, and MLX-native execution on Apple Silicon. Auto-Optimize chooses sensible defaults based on detected hardware.

What Users Say About Jan

On Reddit's r/LocalLLaMA, the most upvoted Jan threads consistently praise it as the best open-source choice for non-technical users — one user puts it as "my pick over LM Studio because it's actually open source." Reviewers on independent comparison sites like neurocanvas.net and promptquorum.com repeatedly call out the UI polish, the plugin system, and the cleaner model-management experience versus Ollama's CLI-first workflow.

The complaints are also consistent and worth knowing. Several Reddit threads report higher idle memory usage than LM Studio, occasional slowdowns when switching between very large models, and a documentation site that lags behind feature releases — the v0.6 UI revamp shipped weeks before the docs caught up. Apple Silicon users on M1/M2 with 8 GB RAM report Jan struggling with anything past a 7B-parameter model, which is a hardware reality more than a Jan-specific issue.

Jan Pricing

Jan is fully free and Apache 2.0 licensed. There is no Pro tier, no usage cap, no telemetry-driven plan upsell. The only money you'll spend is on cloud API usage if you choose to connect a paid provider — and those keys go directly to the provider, not to Jan.

Plan	Price	Key Limits
Jan Desktop App	$0	Unlimited local models, unlimited messages, no account required
Cloud Models (BYO Key)	Pay provider directly	Routed through your own OpenAI / Anthropic / Mistral / Groq keys
Source Code	Free (Apache 2.0)	Self-build, fork, redistribute — commercial use allowed

The catch: you provide the hardware. A reasonable local experience needs at least 8 GB of RAM for 3B models, 16 GB for 7B, and 32 GB for anything in the 13B+ range. Apple Silicon delivers the best price-per-token by a wide margin in 2026.

Who Should Use Jan?

Best for: Privacy-conscious users who want a ChatGPT-style experience without sending data to a cloud, developers building OpenAI-compatible apps that need an offline fallback, and teams in regulated industries (legal, healthcare, finance) where the model has to stay on-device. Apple Silicon owners get the most out of Jan thanks to MLX acceleration.

Not ideal for: Casual users on a base-model laptop with 8 GB RAM (the cloud version of ChatGPT will feel snappier), and developers who only want a CLI — Ollama or llamafile fit that use case better. If you need image generation, Jan supports image inputs but does not run diffusion models locally.

Pros and Cons

Pros:

• Truly free and Apache 2.0 — no asterisk, no "open core" gating of features.
• Cleanest UI in the local LLM space — onboarding is a five-click affair, not a Docker compose file.
• OpenAI-compatible localhost API works as advertised and is ideal for offline LangChain or Vercel AI SDK projects.
• Active development cadence — multiple releases per month, public roadmap, responsive Discord.
• Cross-platform downloads from Microsoft Store, Flathub and direct DMG/EXE/AppImage builds.

Cons:

• Memory usage is reported higher than LM Studio in head-to-head comparisons — a real issue on 8 GB machines.
• Documentation site frequently trails feature releases by 1–2 weeks.
• No built-in retrieval over local files yet (you can wire MCP tools, but it's not one-click).
• Linux ARM64 still relies on a community-built workaround rather than a first-class binary.

Alternatives to Jan

Ollama is the CLI-first counterpart — lighter footprint, no GUI by default, and the most popular pick for power users who want scripting. LM Studio has the most polished UI of any local LLM app and slightly better memory efficiency, but it is closed source — Jan's main differentiator. Open WebUI is the self-hosted browser-based alternative that pairs well with Ollama on a home server. llamafile is the easiest single-binary option but lacks Jan's plugin and MCP ecosystem.

Verdict: Is Jan Worth It?

If you want a ChatGPT-shaped app on your own laptop without sending data to OpenAI, Jan is the easiest way to get there in 2026. It has earned its 42k stars by being the best-balanced option between LM Studio's polish and Ollama's openness — and the Apache 2.0 license means it stays that way. Power users with strong CLI habits will still prefer Ollama, and 8 GB-RAM laptops will still struggle with 13B+ models, but for everyone else, Jan is the local LLM client to install first. We rate it 83/100.

Frequently Asked Questions

Is Jan free?

Yes. Jan is fully free, open source, and Apache 2.0 licensed. There is no paid tier and no telemetry-driven upsell. You only pay if you choose to connect cloud providers like OpenAI, Anthropic or Groq, and those charges go directly to the provider.

What platforms does Jan support?

Jan runs on macOS 13.6+, Windows 10+ and most Linux distributions (deb, AppImage, Flathub). Native MLX acceleration is available on Apple Silicon, and CUDA/Vulkan acceleration on NVIDIA, AMD and Intel Arc GPUs.

How does Jan compare to LM Studio?

Jan and LM Studio cover similar ground — both are desktop apps for running local LLMs. The decisive difference is licensing: Jan is open source (Apache 2.0), LM Studio is closed source. LM Studio currently has slightly lower memory usage and a marginally smoother first-run experience; Jan has a more active plugin ecosystem and MCP support.

Is Jan open source?

Yes — the source code is on GitHub at github.com/janhq/jan under the Apache 2.0 license. You can fork, modify, redistribute and use it commercially without restriction.

Does Jan work offline?

Yes. Once a local model is downloaded, Jan runs without any internet connection. Cloud provider features (OpenAI, Anthropic, etc.) require connectivity, but the local mode is fully offline and conversation data never leaves your device.