Jan vs textgen
Local AI desktop apps compared
jan and textgen both provide desktop interfaces for running language models locally, with options for local APIs and assistant workflows. jan also connects to cloud providers, while textgen covers a broader set of local backends and features such as document inputs, fine-tuning, and image generation.
Jan: Run Local AI on Your Desktop with llama.cpp
Jan is a Tauri desktop app built on llama.cpp that brings open-source AI into an offline ChatGPT-style interface. It runs on Windows, macOS, and Linux, with options to use local models or connect to cloud AI services.
textgen: Run Local Language Models in a Desktop App
TextGen is a private desktop and web interface for running local language models, with support for chat, vision, tools, and compatible APIs. It suits people who want to use and manage models on their own hardware.
| Jan | textgen | |
|---|---|---|
| Language | Rust | Python |
| License | — | AGPL-3.0 |
| Stars | 45k | 47.7k |
| Forks | 3.1k | 6k |
| Last analyzed | Oct 18, 2026 | Oct 3, 2026 |
Key differences
- jan can connect to cloud providers including OpenAI, Anthropic, Mistral, Groq, and MiniMax; textgen focuses on running models locally.
- jan is written in Rust and runs on Windows, macOS, and Linux; textgen is written in Python and offers desktop and browser-based interfaces.
- jan supports local models and provides an OpenAI-compatible API; textgen supports several inference backends and local APIs compatible with OpenAI and Anthropic formats.
- textgen includes image and document inputs, LoRA fine-tuning, and a separate image-generation interface; jan focuses on model use, custom assistants, and MCP integration.
- jan uses a NOASSERTION license, while textgen uses AGPL-3.0, which users should review before incorporating or distributing modified versions.
- Both projects require hardware suited to the selected models; textgen's installation path also affects backend and feature availability.
Choose Jan if you…
- want a desktop app that can switch between local models and supported cloud providers.
- need an OpenAI-compatible local API at localhost:1337 for applications.
- prefer configuring custom assistants in a desktop interface available on Windows, macOS, or Linux.
Choose textgen if you…
- want to use multiple local inference backends, including llama.cpp, Transformers, and TensorRT-LLM.
- need local workflows with vision, document inputs, custom tools, or MCP servers.
- plan to explore LoRA fine-tuning or local image generation when suitable hardware and installation are available.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.