What's the difference between a frontier model and a local model?
A frontier model (Claude, GPT, Gemini) runs in someone else's data center. You send text in, pay per token, and get the most capable systems available. A local model (Qwen, Llama, DeepSeek) is a file you download and run yourself: free per use, fully private, but capped by your hardware. Most people end up using both.
There are really only two ways to use an AI model, and the difference comes down to one question. Whose computer is it running on?
What is a frontier model?
A frontier model runs in someone else’s data center. You don’t download anything. You send text over the internet, the model does its work on their hardware, and the answer comes back. You pay per token (a token is about three-quarters of a word), so the bill scales with how much you use it.
These are the names you already know: Claude from Anthropic, GPT from OpenAI, Gemini from Google. “Frontier” is just shorthand for the most capable models available right now. They need no hardware on your side. A phone can drive Claude Opus. Reach for these when you want the best possible answer to a hard problem.
The trade-off is twofold. There’s an ongoing per-use cost, and your input leaves your device. For a lot of work neither is a problem. For private data, or very high volume, both start to matter.
What is a local model?
A local model is a file. You download the actual weights and run them on your own GPU or Mac. After that, every use is free. Your only cost is the hardware you already bought and the electricity to run it.
The open-weight families here are Qwen (Alibaba), Llama (Meta), DeepSeek, Mistral, and Gemma (Google). Because the model runs on your machine, nothing you type ever leaves it, and it keeps working with the internet off. The catch is that you’re now responsible for it: you need enough memory to hold the model, and you handle your own updates. The very best open models need data-center hardware, but plenty of capable ones run on a normal gaming GPU. (Our guide on what model your computer can run covers the memory math.)
The utility analogy
The cleanest way to think about it: a frontier model is the electric grid. You pay per kilowatt-hour, you never run out, and you don’t maintain a power plant. A local model is solar panels on your roof. Real money up front, then close to free, but sized to your roof. Neither is “right.” It depends on how much you use, how much you care about privacy, and whether you’d rather own the thing or rent it.
When to pick which
| You care most about | Lean toward |
|---|---|
| Absolute top quality on a hard problem | Frontier |
| No hardware to buy or manage | Frontier |
| Privacy: data must not leave your machine | Local |
| Working offline | Local |
| High, steady volume where per-token cost adds up | Local |
| Trying things out with zero commitment | Frontier (pay only for what you use) |
Honestly, the dividing line is rarely clean, and you don’t have to draw it once. The pattern most teams settle into is a hybrid. A small local model handles the routine bulk, things like summaries, drafts, and classification, and a frontier model gets called in for the 10% of tasks that genuinely need the extra horsepower. You get most of the privacy and cost savings of local without giving up the ceiling of frontier.
If you want a model designed from the start to run on your own hardware and keep adapting to your work, that’s what Sia is built for. And if you want top-tier reasoning without managing any of it, Noesis is the hosted flagship.
Common questions
- Is a frontier model always better than a local one?
- At the very top end, yes. The best hosted models still lead on the hardest reasoning and coding. But for everyday tasks, a good 14–70B local model is genuinely close, and it runs entirely on your machine.
- What does 'frontier' actually mean?
- It's an informal label for the most capable models at any given moment: currently Claude Opus, GPT-5.5, and Gemini 3.x Pro. You rent them over an API. You never get the weights.
- Do I have to choose one or the other?
- No, and most people don't. A common pattern is a cheap local model for routine work and a frontier API for the hard 10%: drafts locally, the tricky reasoning hosted.
- Does my data leave my computer with a local model?
- No. That's the main reason people run local models. The text never leaves your machine, and it works with no internet connection. With a frontier API, your input travels to the provider.