Qwen 4 Preview vs Llama 5: Best Open-Source LLM for Mac (May 2026)

Quick Verdict: Who Should Pick Which

If you have already decided you want the short answer, here it is. The longer reasoning — and the benchmark numbers behind it — follow below.

Architecture: MoE 3B-Active vs Dense 8B vs MoE 17B-Active

These three models take three genuinely different approaches, and the architecture is the key to understanding why they behave so differently on a Mac.

Qwen 4 Preview 32B-A3B is a sparse mixture-of-experts (MoE) model: 32 billion total parameters, but only about 3 billion are activated per token. That means it has the knowledge breadth of a 32B model while generating tokens at roughly the compute cost of a 3B model. The catch is memory — all 32B parameters must still be resident in unified memory, so you need the RAM of a 32B model even though it runs at the speed of a much smaller one. It also ships a hybrid reasoning mode that automatically decides whether a prompt needs a slow, deliberate "thinking" pass or a fast direct answer.

Llama 5 8B is the opposite philosophy: a classic dense model where every one of its 8 billion parameters fires on every token. Dense models are simpler, predictable, and — crucially for a Mac — small enough to fit a 16GB machine with room to spare. You give up the breadth of a larger model but get excellent per-token throughput.

Llama 5 Scout 109B-A17B is Meta's big MoE: 109 billion total parameters with about 17 billion active per token, plus native multimodal support for vision and audio. The 17B active footprint makes it heavier and slower per token than Qwen 4, and the 109B total footprint pushes it firmly into 64GB+ Mac territory.

Benchmark Head-to-Head

Here is how the three models stack up across capability benchmarks and measured Mac throughput. Speed figures are from LLMCheck benchmarks at Q4_K_M quantization on the chip noted in each row's footnote below.

Llama 5 8B is genuinely faster — 110 tok/s versus 78 tok/s for Qwen 4 on the same M5 Max — because a dense 8B model moves fewer bytes per token than a 32B-total MoE. But Qwen 4 wins decisively everywhere capability is measured: +9 LLMCheck points over the next-best model, and double-digit leads on every benchmark where both compete. That capability gap is large enough that for most real work, Qwen 4's slightly lower throughput is a worthwhile trade.

License Showdown: Apache 2.0 vs Llama 5 Community

This is the section that quietly decides a lot of real-world deployments, and it is where the gap between the two families is widest.

Qwen 4 Preview ships under Apache 2.0 — one of the most permissive licenses in existence. There is no user-count cap, no requirement to display attribution on your product, no field-of-use restriction, and no obligation to share derivative weights. You can fine-tune it, embed it in a closed-source commercial product, and ship it to millions of users without ever talking to Alibaba. In LLMCheck's scoring formula, Apache 2.0 earns the full 10 license points.

Llama 5 uses the Llama 5 Community License, which is permissive for the vast majority of users but carries conditions Apache 2.0 does not. The most-cited is the 700-million-monthly-active-user clause: if your product crosses 700M MAU, you must request a separate license directly from Meta, which it may grant or deny at its discretion. There are also naming requirements (derivative models must carry "Llama" in the name) and an acceptable-use policy. For a startup or mid-size company these terms rarely bite — but for any team that wants zero legal ambiguity, Apache 2.0 is unambiguously the cleaner choice.

Mac Performance by RAM Tier

Unified memory is the real gatekeeper on a Mac. Here is which model to actually run at each common RAM tier, based on LLMCheck benchmarks.

• 8GB Mac — None of these three fit. Stick with Llama 5 8B at heavy quantization only as a stretch, or step down to a smaller model entirely. 8GB is below the practical floor for all three contenders.
• 16GB Mac — Llama 5 8B is the clear winner. It runs at ~58 tok/s on an M3 16GB and leaves headroom for other apps. Qwen 4 will not fit comfortably here; this is Llama 5 8B's home turf.
• 24GB Mac — Qwen 4 Preview becomes viable and is the better pick. It runs at ~58 tok/s on an M4 Pro 24GB at Q4_K_M, delivering a massive capability jump over Llama 5 8B for the same real-time feel. This is the sweet-spot tier for Qwen 4.
• 64GB Mac — Qwen 4 for text, or unlock Llama 5 Scout for multimodal. Qwen 4 hits ~65 tok/s on an M5 Max 64GB, while Scout runs at ~38 tok/s — slower, but the only one of the three that sees images and hears audio.
• 128GB Mac — Run whatever the task needs. Qwen 4 at ~78 tok/s and Llama 5 8B at ~110 tok/s both fly; Scout at ~42 tok/s is comfortable for multimodal sessions. With this much memory you can keep more than one loaded at once.

Coding Comparison

For local coding on a Mac, this is not close. Qwen 4 Preview posts 76% on SWE-Verified — the benchmark that measures whether a model can resolve real GitHub issues end-to-end — versus roughly 52% for Llama 5 8B. On HumanEval, the gap is 92% to 80%.

Those numbers translate directly into the editor. Llama 5 8B is perfectly competent at single-function completions, docstrings, and well-scoped snippets. But on a multi-file refactor or a bug that spans several modules, Qwen 4's larger knowledge base and its reasoning mode let it plan the change before writing it — sketching which files to touch and in what order. That planning step is exactly what SWE-Verified rewards, and it is why agentic coding tools running Qwen 4 locally feel a generation ahead of the same tools on an 8B model.

If coding is your primary use case and you have the RAM, Qwen 4 Preview is the best local coding model you can run on a Mac today. Install it with ollama run qwen4:32b-a3b.

Reasoning Comparison

Reasoning is the other domain where Qwen 4's architecture pays off. Its hybrid reasoning mode automatically detects when a prompt warrants a deliberate chain-of-thought pass versus a quick answer. Ask it a trivia question and it responds instantly; ask it a competition math problem and it silently works through intermediate steps before answering. That auto-think behavior is reflected in its 89% AIME score — a math-olympiad benchmark where most models without a reasoning pass fall apart.

Llama 5 8B has no dedicated reasoning mode. It is a strong, fast generalist, and you can coax better reasoning out of it with explicit "think step by step" prompting, but it lacks the built-in escalation that Qwen 4 applies automatically. For everyday Q&A the difference is invisible; for math, logic puzzles, and multi-step planning, Qwen 4 pulls clearly ahead.

Llama 5 Scout, with its 17B active parameters, reasons more capably than the 8B but still lacks Qwen 4's explicit hybrid-reasoning machinery — and you pay for its size in throughput. Scout's real edge is multimodal reasoning: it can reason about an image or an audio clip, which neither Qwen 4 nor Llama 5 8B can do at all.

The Verdict

Qwen 4 Preview 32B-A3B is the better default open-source LLM for Mac in May 2026. It is the new #1 on the LLMCheck leaderboard with a score of 73, it wins every capability benchmark we measured, it carries the cleanest possible license, and its 3B-active MoE design makes that capability runnable at real-time speeds on a 24GB Mac. For anyone with the RAM, it should be your first install.

The exceptions are real and worth respecting. If your Mac has 16GB of unified memory, Qwen 4 simply will not fit comfortably and Llama 5 8B is the right call — it is fast, capable enough for most daily tasks, and the best model in its weight class. If you need to work with images or audio and have a 64GB+ machine, Llama 5 Scout is the only one of the three that can, and its multimodal capability outweighs its slower throughput.

For everyone else — coders, researchers, agent builders, and anyone who wants the strongest local model on a mainstream Mac — Qwen 4 Preview is the answer. According to LLMCheck benchmarks, nothing else in the open-source field currently matches its capability-per-gigabyte on Apple Silicon.