Jan vs GPT4All 2026 | Private Local AI for AU

Head-to-head verdict

No single winner. The right choice depends on the job; the verdict below says who should buy which.

It's a tie on the scorecard — 20/25 each — so pick on the job, not the number. Choose Jan if your priority is provable privacy: it's fully open source under Apache 2.0, ships with analytics off by default, and never collects your chats or model choices. Choose GPT4All if you want to chat privately with your own documents: its LocalDocs feature does offline, private Q&A over a folder of files without the usual setup. Both run AI on your own hardware, both are free, and with either one your data never leaves your machine.

Last reviewed: 9 June 2026 by Adam.

At a glance

	Jan	GPT4All
Our score	20/25 ⭐⭐⭐⭐ Very Good	20/25 ⭐⭐⭐⭐ Very Good
Price (AUD)	Free (Apache 2.0)	Free (MIT)
Maker	Menlo Research	Nomic
Licence	Apache 2.0, fully open source	MIT, open source
Telemetry	Analytics off by default, opt-in only	Telemetry opt-in, off by default
Standout feature	Provable, auditable no-telemetry privacy	LocalDocs — private offline document chat
Interface	Clean, ChatGPT-style	Desktop app aimed at users
Runs offline	Yes	Yes
Data residency	On your machine — no offshore data	On your machine — no offshore data
Best for	Provable privacy where you must demonstrate control	Private Q&A over your own sensitive files

Both are genuine local AI apps: free, open source, and offline by design. That already puts them ahead of every cloud assistant on the privacy question that defines this category. They tie on our 25-point scorecard. The contest is decided by what you actually need them for.

What both do

At the core, Jan and GPT4All do the same job. You install a desktop app, download an open AI model sized to your hardware, and you've got a local chat assistant — drafting, summarising, answering questions, working through ideas — entirely on your own computer. No account, no cloud, no internet required.

Both keep everything local by design, which is the whole point of this category: nothing you type leaves your machine. Both are open source, so the code is open to inspect rather than taken on trust. And both default to no data collection — Jan's analytics are off unless you opt in, GPT4All's telemetry is off unless you turn it on.

One shared limit applies to both, and it isn't optional to understand: a model running on your own machine won't match the latest cloud model on the hardest tasks, and capable models need real RAM and a GPU. That's true across the whole local-AI category, not a knock on either app.

Pricing in Australia

There's no pricing table to argue over. Both apps are free and open source — Jan under Apache 2.0, GPT4All under MIT. No subscription, no licence fee, no tiers.

For an Australian buyer that means three things. There's no AUD-versus-USD conversion to do, because nothing is billed. There's no GST question, because there's no invoice. And there's no per-seat cost as you add people — open-source licences don't meter users. The only real cost is hardware: a machine with enough RAM and ideally a GPU to run capable models at a usable speed. That cost is identical for both apps and for the rest of the category.

So price doesn't separate these two. Neither does the GST or currency angle that usually decides an Australian software call. The decision sits entirely on capability and privacy posture.

Where Jan wins

The strongest provable privacy posture. Jan is fully open source under Apache 2.0, with analytics off by default and opt-in only. Even if you turn analytics on, Jan's privacy policy states it never collects your chats, settings, prompts or model choices — only anonymous aggregate metrics. For users who treat "trust us" as insufficient, Jan offers "check for yourself."
Auditability you can demonstrate, not just assert. Because the whole app is open source, an Australian organisation can have the code inspected to prove no data leaves its control — the difference between claiming privacy and being able to show it. That's a genuine edge for a law firm's compliance review or a health practice under the Privacy Act.
A clean, familiar interface. Jan opens to a chat window, a model picker and conversation history — much like a cloud AI assistant, except entirely on your machine. It's approachable for non-developers, so you don't trade usability for privacy.
Flexibility. Jan can run local models or connect to cloud models if you choose, though the reason to use it is the local, private path.

Where GPT4All wins

Built-in private document chat. This is GPT4All's decisive edge. LocalDocs lets you point the model at a folder of your own documents, index it, and ask questions answered from those files — locally. It's private retrieval-augmented generation without the setup project that usually entails.
It answers a very common Australian need directly. "I have a pile of contracts, reports or policies and I want to ask questions of them, but I can't upload them to ChatGPT." GPT4All is built for exactly that. A Tasmanian accounting practice or a Melbourne law firm can interrogate sensitive client files while every document stays on its own machines.
A credible team behind it. GPT4All comes from Nomic, and the desktop app is aimed at users rather than just developers — straightforward to install and use.
Opt-in telemetry, off by default. Like Jan, nothing is collected unless you turn it on, and the code is open to inspect.

A fair caveat on GPT4All's strength: LocalDocs is good, not enterprise-grade. It's built for individual document chat, not multi-workspace, multi-user knowledge bases. For heavy or team document workflows, a dedicated tool like AnythingLLM goes further. (That's our assessment, not a stated product limit.)

Australian fit

Both score a clean 5/5 on Australian Fit, for the structural reason the whole category earns it: local execution means no offshore data, no cross-border disclosure question, and no AUD pricing or GST issue because nothing is billed. For any Australian handling confidential material, that's the comfort zone the Privacy Act wants you in.

The two get there by slightly different routes. Jan pushes furthest on the privacy axis itself — open-source auditability plus analytics off by default makes it the option you can prove, not just assert. That matters most when you need to demonstrate to a regulator, a client or an insurer that data never left your control. GPT4All turns the same local-execution guarantee into a concrete workflow: private Q&A over your own sensitive files. Both keep data on your hardware; Jan optimises for demonstrable privacy, GPT4All for a private use case.

Neither carries an Australian-specific caution beyond the one shared by the whole category — these are smaller projects with smaller ecosystems than the leaders, so weigh community support and longevity for mission-critical deployments.

Which to pick

A short decision framework for the default reader — a 35-year-old business owner who wants AI help without handing sensitive material to the cloud.

Privacy is the whole point → Jan. If you need the strongest, most defensible privacy guarantee — the kind you can have audited and demonstrate to a third party — start with Jan. Open source, no telemetry by default, never your chats. It's the privacy purist's pick.
You want to ask questions of your own files → GPT4All. If your real need is "chat with my contracts, reports or policies, privately and offline," LocalDocs is exactly the right tool. That single, well-executed feature is the reason to choose it.
You want both a clean general chat app and provable openness → Jan. Its ChatGPT-style interface makes it the more natural everyday assistant of the two.
Heavy or team document workflows → neither, on the strength of these two. GPT4All's LocalDocs is individual-grade; for configurable, multi-user RAG, look at a dedicated tool like AnythingLLM.

The verdict

This one genuinely ties at 20/25, and that's the right result — both are very good, free, open-source, offline AI apps that keep an Australian's data exactly where it should be: on their own hardware. The scorecard won't choose for you, so the use case has to.

Pick Jan when provable privacy is the priority. Fully open source under Apache 2.0, analytics off by default, never collecting your chats or model choices — it's the most defensible option in the category when you have to demonstrate, not just claim, that data never leaves your control. And you don't sacrifice usability to get there.

Pick GPT4All when you want to chat privately with your own documents. LocalDocs answers the very common situation of having sensitive files you can't put in the cloud, and answers it well, in an approachable package. It's not enterprise-grade RAG, but for individual private document Q&A it's one of the most directly useful tools in this directory.

Either way, the data stays on your machine — which is the entire point of going local.

Frequently asked questions

Which is better for Australians, Jan or GPT4All?

It's a tie at 20/25 each, so pick on the job. Choose Jan for provable privacy — fully open source under Apache 2.0, analytics off by default, never collecting your chats. Choose GPT4All for private offline document chat via its LocalDocs feature.

Are they free, and do they bill in AUD?

Both are completely free and open source — Jan under Apache 2.0, GPT4All under MIT. There's no subscription, no AUD-versus-USD conversion and no GST question, because nothing is billed. Your only real cost is hardware with enough RAM and ideally a GPU.

Do they keep my data in Australia?

Yes — everything runs locally on your own machine, so nothing leaves your computer and there's no offshore data or cross-border disclosure question. Both score a clean 5/5 on Australian Fit, the comfort zone the Privacy Act wants you in for confidential material.

Can I chat with my own documents?

That's GPT4All's decisive edge. Its LocalDocs feature lets you point the model at a folder of your files and ask questions answered from them, entirely offline. It's individual-grade, not enterprise RAG — for heavy team workflows, look at a dedicated tool like AnythingLLM.

Sources

Imported from our verified reviews of Jan and GPT4All (both reviewed 8 June 2026).

Jan (Menlo Research) — Primary — https://jan.ai/ — Wayback: https://web.archive.org/web/20260607213322/https://www.jan.ai/ — checked 13 June 2026
Jan Privacy (analytics off by default, opt-in only; never collects chats/settings/model choices) — Primary — https://www.jan.ai/docs/desktop/privacy — Wayback: http://web.archive.org/web/20260601041619/https://www.jan.ai/docs/desktop/privacy — checked 13 June 2026
Jan source & releases (Apache 2.0, fully open source) — Primary — https://github.com/menloresearch/jan — Wayback: http://web.archive.org/web/20251021123628/https://github.com/menloresearch/jan — checked 13 June 2026
GPT4All (Nomic) — Primary — https://www.nomic.ai/gpt4all — Wayback: https://web.archive.org/web/20260607213715/https://www.nomic.ai/gpt4all — checked 13 June 2026
GPT4All source (MIT, open source) — Primary — https://github.com/nomic-ai/gpt4all — Wayback: http://web.archive.org/web/20260603012024/https://github.com/nomic-ai/gpt4all — checked 13 June 2026
GPT4All settings (LocalDocs; Datalake telemetry opt-in, default off) — Primary — https://docs.gpt4all.io/gpt4all_desktop/settings.html — Wayback: http://web.archive.org/web/20260226135247/https://docs.gpt4all.io/gpt4all_desktop/settings.html — checked 13 June 2026

Jan vs GPT4All 2026: which private local AI app for Australians?