Outlier is a Mac app that runs AI models on your own computer. It is a private alternative to ChatGPT and Claude. It can read your codebase, edit files, run your tests and search the web. Your chats and files stay on your Mac, and there is no usage cap.
Free to start: Nano + Lite.
Pro: every model for $249 once, or 4 × $62.25 ($124.50 for the first 25, or 4 × $31.13). 30-day refund. No subscription.
def merge_intervals(intervals: list[list[int]]) -> list[list[int]]: """Merge overlapping intervals. Args: intervals: List of [start, end] pairs (inclusive). Returns: List of merged non-overlapping intervals. """ if not intervals: return [] # Sort by start time sorted_intervals = sorted(intervals, key=lambda x: x[0]) merged = [sorted_intervals[0]] for current in sorted_intervals[1:]: last = merged[-1] # Overlap or adjacency: merge them if current[0] <= last[1]: merged[-1] = [last[0], max(last[1], current[1])] else: merged.append(current) return merged
The edge case most implementations get wrong: adjacent intervals (e.g., [1, 2] and [2, 3]). Many implementations use < instead of <=, which fails to merge intervals that touch at a single point.
Real output from Outlier Core on an M1 Ultra. 21 socket samples during this answer, 0 left the Mac
Local model inference runs entirely on your Apple Silicon chip. Your prompts, files, code, and chat history stay on your disk, under your control. Nothing you type leaves your Mac unless you switch on web search (then the search query does). On its own the app goes online only for things that carry none of your words: model downloads, the update check, the price shown when you open an upgrade window, and on Pro the license check. No telemetry on inference. The open-weight models are auditable on HuggingFace.
$ lsof -a -p $(pgrep -x outlier-cli) -i -n -P outlier-c 62303 22u IPv4 TCP 127.0.0.1:8766->127.0.0.1:56293 (ESTABLISHED) outlier-c 62303 25u IPv4 TCP 127.0.0.1:8766 (LISTEN) # sampled 21× across the 33s answer shown above — sockets leaving this Mac: 0
Outlier-a is load-bearing: without it lsof ORs its filters and returns every socket on the machine instead — 328 when this was captured, and a different number each time you run it, because it counts every other process too. That is how a check like this can quietly prove nothing.Every tier is sized to real Apple Silicon memory. Start free on a MacBook Air; scale all the way to the 397B Plus tier on a Mac Studio.
Apple Silicon only, M1 or newer, on macOS 26 or later (a free update). Intel Macs are not supported.
Choosing the machine first? M1 vs M4 compares memory bandwidth, Mac mini vs Mac Studio compares 48 GB against 96 GB, and Mac vs PC covers why unified memory decides the model size.
I build Outlier on my own, with no investors, so the free tier stays free. Pro is one payment for all six models and every feature.
Private AI on your Mac for everyday chat. No subscription.
Pro for as long as Outlier exists, including future model releases.
30-day money-back guarantee. To get a refund, email matt@outlier.host.
Everything below ships today, in a signed, notarized Mac app.
Streaming token output, persistent local history, rename / delete / pin, Markdown export — and a running cost display that stays at $0.00.
File read/write and shell execution behind permission modes, with a plan-review card, repair loop and audit log.
Several engines queried in parallel — DuckDuckGo, Mojeek and Wikipedia for the general web, plus OpenAlex, Crossref, Stack Exchange and Hacker News — each best-effort, so one of them blocking does not fail the search. Sources are classified (encyclopedia, official, docs, community) and every claim carries an inline citation with the excerpt it came from.
Persistent memory in SQLite — facts carry a category, a confidence score, a confirmed flag and the chat they came from, so you can see why Outlier believes something. Exportable and importable as JSON.
Upload an image or a screenshot and query it directly through Outlier Vision — reading text, diagrams and charts in the picture, all on your Mac. Text-layer PDFs are extracted too; scanned PDFs are not, because that needs a separate OCR pass the app does not ship yet.
A macOS arm64 DMG, accepted by Apple notarization and Gatekeeper, distributed via GitHub Releases with a built-in auto-updater.
Three things Outlier does that a chat box does not. Same window, same offline machine, nothing leaving it.
search_files — find the upload clientread_file — src/upload/client.tsedit_file — add retry with backoffrun_bash — npm testapprove, approve_edited,
deny — so a plan can be edited before it runs, and the
tool names are the real registry of 22. The example task is illustrative;
the mechanism is not.⌘N new · Esc stop · ⌘⇧M hideFree is useful immediately with Nano + Lite. Pro adds the other four tiers — Quick, Core, Vision, and the 397B Plus tier. There is one model per memory bracket — 6, 12, 16, 24 and 64 GB — so the tier you want is simply the largest your Mac can hold. (Not sure what yours has? Apple menu → About This Mac — the “Memory” line is the number to match.) The single exception is Vision 3.8, which shares Core’s 24 GB bracket because it is the only tier that reads images; on text and code the two are level, so Core stays the default and you switch only when you have a screenshot or a diagram to hand it.
| Tier | Plan | Best for | Disk / RAM | Speed / note |
|---|---|---|---|---|
Outlier Nano Qwen3.5-4B · MLX 4-bit |
Free | Fast iteration, lightweight chat, small Macs | 2.37 GB · 6 GB min RAM | ~30–70 tok/s (Mac-dependent) |
Outlier Lite Qwen3.5-9B · MLX 4-bit |
Free | Daily local AI, writing, Q&A | 5.04 GB · 12 GB min RAM | Mac-dependent |
Outlier Quick Gemma-4-26B MoE |
Pro | Thinking-mode reasoning, not a code substitute | 15.61 GB · 16 GB min, 24 GB recommended | Mac-dependent |
Outlier Core Qwen3.6-27B · text-only |
Pro | Best default quality, reasoning, coding | 15.13 GB · 24 GB min RAM | High-end quality, fully offline |
Outlier Plus Qwen3.5-397B-A17B · V9 paged |
Pro | Largest local tier · 397B MoE on high-end Macs. Slow on purpose-built hardware: 1.59 tok/s measured at the shipped K=20 setting on an M1 Ultra, 60–90s to first token on 64 GB. Use Core for everyday work. | 209 GB disk · 64 GB min, 128 GB recommended | For depth, not for speed |
Outlier Vision 3.8 Qwen3.8-27B · images + text |
Pro | Images, screenshots, diagrams — and plain text questions too | 15.5 GB · 24 GB min RAM | 17.2 tok/s |
Quick / Core / Vision / Plus 397B are all included with Pro, which is a one-time purchase. Coding used to be a separate Code tier on the same weights; it is now a mode of Core rather than a second download. Quick is useful for reasoning, not positioned as a coding tier.
Benchmarks for the shipping tiers, measured on this hardware against the shipping build. Where a tier has not been measured, the cell says so rather than carrying an estimate.
Dot size follows disk footprint. Plus is absent because it has no MMLU run on the shipping 4-bit build — the figure this chart plots. Vision 3.8 has one, and it is in the table: it shares Core’s 24 GB bracket, so on an axis of required memory it would sit on top of Core rather than tell you anything new.
| Tier | MMLU | HumanEval | SWE-bench (agentic) | Decode | Disk |
|---|---|---|---|---|---|
Outlier Nano Qwen3.5-4B · MLX 4-bit |
52.0% | Not measured | Not measured | 71.7 tok/s | 2.37 GB |
Outlier Lite Qwen3.5-9B · MLX 4-bit |
77.0% | Not measured | Not measured | 53.4 tok/s | 5.04 GB |
Outlier Quick Gemma-4-26B MoE |
81.0% | Not measured | 0% | 43.6 tok/s | 15.61 GB |
Outlier Core Qwen3.6-27B · text-only |
89.5% | 95.1% 156/164 |
46.0% | 20.7 tok/s | 15.13 GB |
Outlier Plus Qwen3.5-397B-A17B · V9 paged |
Not measured — the 209 GB weights are not on the test machine. Plus itself runs here at 1.59 tok/s, 14.04 GB peak during generation, streaming experts from SSD; MMLU awaits a re-download, not a hardware limit. | Not measured | Not measured | 1.59 tok/s | 209 GB |
Outlier Vision 3.8 Qwen3.8-27B · images + text |
82.5% | 94.5% 155/164 |
Not measured | 17.2 tok/s | 15.5 GB |
Decode speed on this M1 Ultra, log scale — the tiers span 45×, so a linear axis would flatten Plus to nothing. These figures come from an earlier measurement round (2026-05-20) than the MMLU and SWE-bench numbers above, which were taken 2026-08-09; the dataset file says so too. Quick is blank for the same reason Plus is absent from the chart above: no run on the shipping build. Vision 3.8 is measured but shares Core’s memory bracket, so it appears in the table rather than the chart. Speed varies by Mac.
How these were run — sample sizes, grading and hardware for every figure above. The download is a later build than the one measured; these figures have not been re-run on it.
Why this table is unusual — checked on their live pages 2026-08-13, Ollama, LM Studio, Jan and GPT4All publish no MMLU, HumanEval or SWE-bench figure at all. That is not a criticism — the scores belong to the upstream models — but it means nobody in this category tells you what a local model will actually get right. These are ours to be held to.
The cloud tools proved the workflow: coding agents, long-context research, always-on help. The problem is the meter. Outlier is building the local version — Mac-native, private by default, and not capped by an Outlier token allowance.
| Cloud AI tools | Cloud coding agents | Outlier Free | Outlier Pro | |
|---|---|---|---|---|
| Monthly cost | $20+ | Often much higher | $0 | $249 once · or 4 × $62.25 |
| Usage model | Server-side limits | Usage windows / caps | No token meter | No token meter |
| Where inference runs | Provider cloud | Provider cloud | Your Mac | Your Mac |
| Privacy default | Remote request | Remote repo / context | Local by default | Local by default |
| Offline use | No | No | Yes, once downloaded | Yes, once downloaded |
| Current maturity | Very mature | Very mature | Useful beta | Ambitious beta |
Honest framing: Outlier is not claiming parity with the best cloud coding agents today. The beta is the foundation; Pro and Founders revenue funds the climb toward that experience, locally. See the raw data: 54-prompt Outlier vs Claude · Mac local-AI benchmarks · streaming-engine tok/s · RAM → model size.
Cloud inference needs data centers, networking, cooling, and ever-growing GPU clusters. Local inference uses the Apple Silicon chip you already own — no round-trip, no per-token meter. What a cloud prompt actually travels to.
of AI compute is inference — the calls made every time someone uses a cloud model, now drawing more grid electricity than training did (MIT Tech Review, 2025).
Local models create no Outlier cloud-inference bill and no cloud token meter.
Apple Silicon unified memory is efficient for local inference versus shipping every prompt to a server.
Not every local query is automatically cleaner — hardware, model size and electricity source all matter. The point is directional: move everyday inference onto devices people already own, and the load on cloud infrastructure drops.
The best environmental feature isn’t a green badge. It’s a model good enough, small enough and fast enough that people actually choose to run it locally.
Up to 3 Macs at a time; deactivate one to move it. Deactivate from Settings > License on the Mac you are leaving, then activate on the new one — you do not need to contact us, and there is no limit on how many times you move it.
It runs on your Mac, so there is no token cap and no monthly bill.
Free · M1 or newer · macOS 26+ (free update) · 290 MB. Intel Macs are not supported. Pro is $249 once, or 4 × $62.25 ($124.50 for the first 25), with a 30-day refund.
On your phone?
Open weights ship constantly and most of them are not worth your disk. When one is — a model that beats a tier Outlier already has, on hardware you already own — we say so, and what it replaces. That is the only reason we will email you.
Your address and which of our sites you sent it from. No IP, no user agent, no referer, and no tracking pixel on this form — it sends nothing until you press the button. One click to leave, on every email. Privacy.