Jan vs Msty: pricing, features and which to choose

Jan is a desktop AI assistant developed by Menlo Research, with its open-source repository created in August 2023. The project positions itself as an alternative to ChatGPT and Claude, running language models on a user's computer. Its website reports more than 4 million downloads, while another homepage rendering reports more than 6.5 million downloads, so the exact current total is inconsistent across Jan's pages. The project supports macOS, Windows, and Linux, and its repository lists Apache 2.0 licensing. Jan's current product surface includes desktop chat, local models, remote provider connections, projects, model management, MCP integrations, and a separate Jan Agent. It is aimed at people who want control over model choice, local files, and data location.

A typical Jan workflow begins by installing the desktop application, choosing a bundled or downloadable model, and opening a chat without an account. Users can download compatible GGUF models through Jan’s hub, import local GGUF files, or connect providers including OpenAI, Anthropic, Mistral, and Groq with their own keys. Settings include context size, GPU layers, temperature, sampling controls, grammar files, and JSON schema files, helping users balance output behavior and hardware limits. Jan also supports custom assistants, file attachments, projects, MCP servers, and tool-enabled workflows. Its local server runs an OpenAI-compatible API at localhost:1337, so applications such as Continue and Cline can use local or configured remote models. This setup suits users who want desktop control, local files, and flexible provider choices.

Jan’s main limitation is that local performance depends on the computer, model size, quantization, available memory, and graphics support. Users should check hardware requirements and disk space before downloading large models. Ollama offers a more terminal-oriented model runner, while Jan adds a fuller desktop chat interface, model controls, projects, assistants, MCP integrations, and provider connections. LM Studio is a closer desktop comparison, with a polished local model catalog and server features, while Jan places more emphasis on open-source licensing and local data ownership. ChatGPT is easier for users who want a hosted service without model downloads, configuration, or hardware setup. Choose Jan for free local inference, an OpenAI-compatible local API, flexible model and provider selection, and control over where conversations are stored. Remote providers remain separately billed, while local use avoids Jan subscription charges.

Msty Studio began as Msty App, a desktop client, before Msty shifted its product focus to Studio in 2025. Msty describes Studio as a privacy-first workspace for local and online AI models, available through desktop and web versions. Its product pages cover chat, files, prompts, personas, Knowledge Stacks, media generation, and team controls. Msty publishes no founding year, headcount, funding total, or user count. A July 2025 announcement says the original desktop application was becoming a legacy tool and would be phased out as Studio replaced it. The current offering includes free desktop access, paid features, and an enterprise option. Studio is therefore an application layer for connecting and using models, not a foundation model provider or inference API. It suits people who want local privacy, multiple model connections, and reusable knowledge workflows.

A typical workflow begins with a local inference engine or an online provider, followed by chat in a shared workspace. Studio supports Ollama, llama.cpp, and MLX for local models, while hosted providers can be connected with user credentials. Split Chats places model responses side by side for comparison. Agent Mode supports multi-step, tool-aware work, and Crew Conversations puts multiple personas in one thread. Knowledge Stacks retrieve context from documents and notes. Prompt Studio, Persona Studio, Skills Studio, and Media Studio create reusable assets. Studio Web stores data in browser local storage, but local models there require a connection to Studio Desktop or Msty Sidecar. This setup suits people who want local privacy, multiple model connections, and reusable knowledge workflows, rather than one managed assistant, so Msty Studio is best for those users.

Studio’s product transition can complicate documentation because Msty App and Studio have separate guides, while Studio Web needs Desktop or Sidecar for local models, tools, and real-time data. Hosted inference remains subject to provider terms and fees, and Msty publishes no fixed token or message quota, so practical limits depend on the selected model and provider. Compared with Jan, Studio offers a broader workspace. Compared with LM Studio, it adds web access, workflow studios, and team controls. Compared with ChatGPT, Studio provides stronger local-engine control but fewer proprietary cloud capabilities. Choose Msty Studio for local privacy, several model connections, and reusable knowledge workflows when those priorities matter more than using a single managed assistant, making it best suited to users who want flexible model control and reusable workflows.