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. Current Qwen is Apache 2.0. The Qwen3.6 repo says it plainly: "All our open-weight models are licensed under Apache 2.0." That line sits in the Qwen3.6 repo and covers what Alibaba ships today — Qwen3.5 and Qwen3.6. 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.

That is a recent posture, not a family tradition, and the difference bites if you pin an older checkpoint. Qwen2.5-72B-Instruct reports license: other with license_name: qwen, and its LICENSE file — headed "Qwen LICENSE AGREEMENT" — carries the same three restrictions this page criticises in Llama. A scale cap: if "your product or service has more than 100 million monthly active users, you shall request a license from us." An attribution rule: "prominently display 'Built with Qwen' or 'Improved using Qwen'". And a notice-retention clause on every copy you distribute. Qwen2.5-3B-Instruct is worse for a commercial build — license_name: qwen-research, granting rights "FOR NON-COMMERCIAL PURPOSES ONLY." Qwen2-72B-Instruct sits under Tongyi Qianwen, and Qwen1.5-110B-Chat under license: other as well. Apache 2.0 describes what Alibaba ships now, not everything with a Qwen name on it.

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 1.59 tok/s on a 64GB machine, streaming experts from SSD. (In my app the 4B and 9B tiers are free; the 27B, 35B-A3B and 397B tiers are behind Pro. The weights themselves are Apache 2.0 — you can run any of them yourself for nothing.)

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 118,000 downloads a month and the 4-bit about 63,000; Llama-4-Scout 4-bit sees about 1,200. On base checkpoints: Qwen3.6-35B-A3B ~6.5M per 30 days against Scout's ~463K and Maverick's ~62K. 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

^† The Qwen column's licensing rows describe the current line. Qwen2.5-72B-Instruct's "Qwen LICENSE AGREEMENT" adds a 100-million-monthly-active-user cap, a "request a license from us" clause and a "Built with Qwen" attribution rule; Qwen2.5-3B-Instruct is Qwen Research, non-commercial only. Check the LICENSE file of the exact checkpoint you ship.

Where each family genuinely wins, and where each falls down

Qwen wins on license, ladder and cadence. Apache 2.0 across the entire current open line — every Qwen3.5 and Qwen3.6 checkpoint, top to bottom — is the cleanest legal posture a major lab offers today. 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 is carried by more hosted providers than most open models. 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 two open-weight generations, Qwen3.5 in February 2026 and Qwen3.6 in April. 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 reporting (CNBC, December 2025) says 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.

Licensing first, then size

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 usage caps, nothing uploaded unless you ask for it. Be clear on what costs money: the free tier is Nano 4B and Lite 9B, and the 27B, 35B-A3B and 397B tiers I measured above need Pro. And on the limits — Apple Silicon only, so no Windows or Linux build, no Intel Mac support, and no image generation. If you need those, raw weights and your own runtime serve you better.