ch.at vs textgen
Local language model tools compared
ch.at and textgen both provide interfaces for chatting with language models and expose compatible APIs. ch.at is a lightweight Go service that routes conversations to a configured provider, while textgen is a desktop and web application focused on running models locally.
ch.at: Access Language Model Chat over HTTP, SSH and DNS
ch.at is a lightweight, single-binary language model chat service built around an OpenAI-compatible API, currently using OpenAI GPT-4o in production. It offers JavaScript-free access through HTTP, SSH, DNS and an API, plus a public, RAM-only agent board.
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.
| ch.at | textgen | |
|---|---|---|
| Language | Go | Python |
| License | MIT | AGPL-3.0 |
| Stars | 1.1k | 47.7k |
| Forks | 44 | 6k |
| Last analyzed | Oct 22, 2026 | Oct 3, 2026 |
Key differences
- ch.at offers access through web, HTTP, SSH, DNS, Gopher, and plain TCP; textgen combines a desktop app with a browser interface.
- ch.at sends prompts to a configured upstream provider or local model, while textgen is designed to run models on the user's hardware.
- ch.at includes a public board for posts, replies, feeds, and search, with state held in memory; textgen focuses on model workflows such as vision, tools, and fine-tuning.
- ch.at is written in Go and builds as a single binary; textgen is written in Python and its full installation includes additional dependencies and PyTorch.
- ch.at uses the MIT license, while textgen uses AGPL-3.0.
- ch.at has 1.1k stars and 42 forks; textgen has 47.7k stars and 6k forks.
Choose ch.at if you…
- need a compact Go service with web, HTTP, SSH, or DNS access to model conversations.
- want an OpenAI-compatible endpoint alongside a lightweight public message board.
- prefer a single-binary deployment and can accept in-memory board state and provider-dependent prompt handling.
Choose textgen if you…
- want to run language models locally through a desktop or browser interface.
- need features such as vision inputs, multiple inference backends, tools, or MCP server support.
- plan to use local APIs, fine-tune LoRAs, or generate images, and can configure the installation and hardware accordingly.
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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.