Models, tiers, and what runs on this device
Gezel doesn't come with its own AI — it puts your choice of AI to work. That can be a cloud provider (OpenAI, Anthropic, GitHub Copilot) or a model running locally on your own machine. With a local model, your data never leaves the room.
Tiers: matching the model to the job
Models come in sizes, and size matters for what a gezel can be trusted to do alone. Gezel groups them into tiers:
| Tier | Size (in billions of parameters) | Good at |
|---|---|---|
| tiny | under 5B | quick drafts, simple lookups |
| small | 5–12B | everyday chat, focused single tasks |
| medium | 12–45B | multi-step work with tools |
| large | 45B and up, local | most roles, unsupervised steps |
| cloud | hosted frontier models | everything |
Every role declares a model floor — the minimum tier it needs to work unsupervised. A reviewer gating your deliverables needs more headroom than a gezel drafting a shopping list, and gezel enforces that automatically.
On this device
Gezel classifies each device as tiny, small, medium, or large from the memory available to local models. Open this page in the app to see the current device.
Installed local models
Gezel runs local models (llama.cpp, MLX, Ollama and friends) alongside cloud providers — the Models catalog in the app shows what fits your machine.
Choosing
You don't have to get this right up front. Set a default provider in Settings, and override it per gezel only when a companion needs something different — a big cloud model for your reviewer, a fast local one for day-to-day chat. Gezel checks each new model's fitness on your hardware before recommending it.