Qwen vs Llama: open model families compared in 2026

Disclosure first: I'm not neutral. I build Outlier, a Mac app shipping Qwen-family and Gemma-family weights on-device and no Llama weights at all. That was mostly a licensing decision, and I'll show you the clauses. Llama has advantages I'd be lying to skip, so those are here too.

Neither of these is a product

Qwen comes from Alibaba, Llama from Meta, and both are just files. You run them through somebody else's runtime — llama.cpp, MLX, Ollama, vLLM — or rent them from a hosted API. Neither ships an installer, a model manager or support. And one thing people get backwards: run either locally and nothing leaves your machine. Data exposure is a question about the hosted services, not the weights.

Licensing decides whether you can ship

For a hobby script, licensing is noise. Ship software with weights inside it and it's the whole decision. Qwen is Apache 2.0. The Qwen3.6 repo says it plainly: "All our open-weight models are licensed under Apache 2.0." No user cap, no field-of-use restrictions, no naming rules, no attribution, no form. Qwen3.6-27B on Hugging Face is gated: false — fetchable programmatically, on a user's behalf.

Llama 4 isn't. Four clauses in the Llama 4 Community License matter. A scale cap: above 700 million monthly active users, you "must request a license from Meta, which Meta may grant to you in its sole discretion." An Acceptable Use Policy incorporated by reference, barring military, nuclear, espionage and weapons work, critical-infrastructure operation and unlicensed professional practice. Attribution: "prominently display 'Built with Llama'", and derived model names must begin with "Llama."

The Open Source Initiative has said publicly that Meta's license doesn't meet the Open Source Definition, citing the commercial restriction and the field-of-use policy. That post was about Llama 2 in 2023; both clause types survive in Llama 4. Then there's the gate: Llama weights on Hugging Face are gated: "manual" — name, date of birth, country, affiliation, job title, plus a review wait. For an app fetching models on a user's behalf, that's a wall.

Outlier's Quick tier is Gemma-based, under Google's separate Gemma Terms of Use — so six of my seven tiers are Apache 2.0 Qwen, and Gemma is the one exception.

Sizes, and what actually runs on a Mac

Qwen's ladder is unusually complete: Qwen3.5 spans 0.8B, 2B, 4B, 9B, 27B, 35B-A3B, 122B-A10B and 397B-A17B, and Qwen3.6 added a 35B-A3B MoE plus a dense 27B in April 2026. There's a real Qwen for an 8GB, 16GB, 32GB and 64GB Mac. Llama 4 has no small member — Scout is 17B active over 109B total, Maverick 17B over 400B, and Behemoth, previewed as still training, never shipped. A 4-bit Scout is well past 16GB, so on a Mac you fall back to Llama 3.x, where the 1B and 3B are excellent.

Measured off my own build, not a spec sheet: Qwen3.5-4B is 2.37GB needing 6GB RAM (71.7 tok/s on an M1 Ultra, 32 on an M4 Air); Qwen3.5-9B, 5.04GB and 12GB; Qwen3.6-27B, 15.13GB and 24GB at 20.7 tok/s; Qwen3.6-35B-A3B, 19.0GB and 24GB; Qwen3.5-397B-A17B, 209GB and 2.1 tok/s on a 64GB machine, streaming experts from SSD.

Strengths by task, language coverage, and the Apple Silicon gap

Qwen3.6-27B's own config.json reports 262,144-token context, a hybrid stack alternating linear and full attention every fourth layer, and native multimodality. Long context plus vision at a size that runs 4-bit on a 32GB Mac is rare. Llama 4 Scout advertises a 10M-token window, the largest published for any open-weight model, and both Llama 4 models are natively multimodal — but you need serious hardware to see it.

On Apple Silicon the gap is stark. mlx-community's 8-bit Qwen3.6-35B-A3B pulls roughly 85,800 downloads a month and the 4-bit 61,400; Llama-4-Scout 4-bit sees about 1,200. On base checkpoints: Qwen3.6-35B-A3B ~6.14M per 30 days against Scout's 462K and Maverick's 64K. Llama's counterexample is real, though — Llama-3.2-1B-Instruct pulls ~10.4M a month, more than any single Qwen3.6 checkpoint, and Meta ships its own SpinQuant-INT4 and QLoRA-INT4 on-device builds.

On language coverage I'd rather leave a gap than guess: I found no primary source for a current Qwen3.6-versus-Llama-4 multilingual head-to-head, so I'm not publishing one. What is documented: Alibaba runs separate international and Chinese-mainland deployments, and the official Qwen Studio app appears in the Singapore, Hong Kong and UK App Stores but not in a US App Store search, where the only "qwen" result is an unaffiliated third-party app — a real supply-chain trap.

Side-by-side

Where each family genuinely wins, and where each falls down

Qwen wins on license, ladder and cadence. Apache 2.0 across the whole open line is the cleanest legal posture a major lab offers. Alibaba publishes its own FP8 and GPTQ-Int4 checkpoints, so a production build doesn't hang on community re-quants, and MLX and GGUF builds land within days. Qwen Code, the official terminal agent, is Apache 2.0 and points at a local Ollama or vLLM endpoint.

Llama wins on maturity and the small end. llama.cpp is named after it. Every inference stack, quantization format, fine-tuning library and Core ML path targets Llama first, so integration risk is near zero. Llama 3.3 70B has the widest hosted-provider availability of any open model. And enterprise procurement has cleared its license thousands of times.

Llama's problem is stagnation. The newest artifact of any kind in Meta's Hugging Face org is dated 2025-04-28 — roughly fifteen months of nothing while Qwen shipped three generations. llama.com no longer exists; it redirects to developer.meta.com, a JavaScript shell where the license isn't server-rendered, so GitHub is now the reliable source. Press reports say Meta moved its frontier work to closed weights — unconfirmed from any Meta-owned page.

Qwen's problems differ. The flagship isn't open: Qwen-Max is API-only with no weights in Qwen's Hugging Face org, so the strongest Qwen isn't the one you can run. Cadence cuts both ways — Qwen3.5 shipped in February 2026 and was superseded in April, and Qwen3.6's config still carries a qwen3_5 architecture string, so pin an exact checkpoint. The developer free tier ended 2026-04-15, leaving a 1M-token trial valid 90 days. And I couldn't read Qwen's consumer privacy policy — it renders only via JavaScript — so whether Qwen Studio chats train models is unknown.

Who should pick which

Pick Qwen if you're shipping commercial software with weights bundled or auto-downloaded, you need a size that fits a consumer Mac, or you want a cheap hosted escalation path — Qwen-Flash starts at $0.05 per million input tokens.

Pick Llama if you need a sub-gigabyte always-resident model, your stack depends on tooling that only supports Llama architectures, procurement has already cleared the license, or you want a hosted fallback across many providers.

Pick neither, and use an app, if you want local AI on your Mac without choosing a runtime, a quantization and a prompt format. That's what I built Outlier for: Apple Silicon only, no account, no caps, nothing uploaded. It has no web search, no image generation, no voice mode and no Windows build — if you need those, raw weights and your own runtime serve you better.