Gemma 4 26B A4B and Qwen3.6 27B are both large open-weight models that can run on higher-memory Apple Silicon Macs. They are not architectural twins, however, and the better choice depends on whether you prioritize multimodal all-round work or coding-agent performance.
Start with Qwen3.6 27B for repository work, tool use, and terminal-heavy coding. Start with Gemma 4 26B A4B for a broader local assistant with efficient MoE inference and a strong text-and-image path. For either model, 32 GB is an entry point; 48 GB or more is the more comfortable target.
Decision map based on the current Ollama listings and official model cards. Sources: Ollama Gemma 4, Ollama Qwen3.6, Gemma 4 model card, and Qwen3.6 27B model card. Checked August 15, 2026.
New in Ollama 0.31: Gemma 4 MLX and multi-token prediction
Ollama’s June 29 announcement changes the practical Gemma 4 decision on Apple Silicon. Ollama reports nearly 90% faster average output on an Aider Polyglot coding-agent benchmark, with the speedup enabled by multi-token prediction (MTP) and turned on by default. Ollama tunes the draft length at runtime; there is no MTP setting to configure.
That number is not a universal Mac benchmark. Ollama measured the result on its stated coding-agent workload, and its separate kernel example reports 2× to 2.5× faster matrix multiplications on an M5 Max with nvfp4. Different Macs, quantizations, context lengths and tasks can produce different results.
The documented starting point is Ollama 0.31 or later with the MLX tag:
ollama launch claude --model gemma4:12b-mlxIf Gemma 4 was downloaded before the MTP update, Ollama says to pull the MLX tag again. The official example targets gemma4:12b-mlx; do not silently transfer its benchmark number to every gemma4 size, especially the 26B comparison used below. For installation and storage details, see the Ollama Mac setup guide and the Mac mini local-AI RAM guide.
Decision Table
| Criterion | Gemma 4 26B A4B | Qwen3.6 27B |
|---|---|---|
| Architecture | MoE, 25.2B total / about 3.8B active | Dense model (no MoE) |
| Ollama package | about 18 GB | about 17 GB for the explicit 27B tag |
| Model context | 256K | 262,144 native; Ollama lists 256K |
| Ollama input | text and image | text and image |
| Best starting point | general assistance, reasoning, multimodal work | coding agents, repositories, tool use |
| Comfortable Mac memory | 48 GB+ | 48 GB+ |
These package sizes are not the full runtime requirement. macOS, applications, the inference engine, input data, and KV cache all share unified memory.
Architecture: Only Gemma Is MoE
The two models use different architectures.
Gemma 4 26B A4B has roughly 25.2 billion total parameters and about 3.8 billion active parameters per token. Ollama describes eight active experts, 128 experts in total, and one shared expert. Only part of the model participates in each token calculation, but all weights still need storage and memory.
Qwen3.6 27B is a dense model, not a Mixture-of-Experts model. Its published card emphasizes agentic coding; the precise attention/SSM mechanism is not presented as a local-performance differentiator. The 27B label refers to its parameter count, and runtime memory still depends heavily on quantization, context, and KV cache.
The useful takeaway is simple: active parameters affect compute, while total model weights still matter for memory. “A4B” does not turn an 18 GB Ollama package into a 4B-sized download.
Vendor Benchmarks
The two model cards do not publish identical test suites, so the values below are directional rather than a controlled head-to-head benchmark.
| Area | Gemma 4 26B A4B | Qwen3.6 27B |
|---|---|---|
| Published examples | AIME 2026 88.3%, LiveCodeBench v6 77.1%, Codeforces 1718, MMLU Pro 82.6%, MMMU Pro 73.8% | SWE-bench Verified 77.2, SWE-bench Pro 53.5, Terminal-Bench 2.0 59.3, SkillsBench Avg5 48.2, MMLU-Pro 86.2 |
| Positioning | all-round reasoning, vision, MoE efficiency | coding agents and repository workflows |
| Important caveat | Google vendor results | Qwen vendor results and internal agent scaffold |
Qwen’s published evaluation set speaks directly to coding-agent buyers. Gemma’s table covers a broader mix of reasoning, coding, vision, and long-context tasks. Neither table tells you the token rate or task success rate on your particular Mac.
Context: Model Maximum vs Mac Reality
- Gemma 4 26B A4B: 256K context in Ollama.
- Qwen3.6 27B: 262,144 tokens natively according to the model card; Ollama lists 256K.
Qwen documents extension beyond its native window with a suitable setup. That should not be presented as a normal local Ollama experience.
Large context can consume more memory than expected because the KV cache grows with the active window. On a Mac, begin with a modest value, run the real workload, and inspect offload with:
ollama psIf the model spills onto the CPU, generation can slow dramatically. A 256K label is not a promise that the whole window fits alongside macOS and your development tools.
Ollama Setup
Gemma 4 26B A4B
ollama pull gemma4:26b
ollama run gemma4:26bThe current package is about 18 GB. Long context and large image input add to the runtime footprint.
Qwen3.6 27B
ollama pull qwen3.6:27b
ollama run qwen3.6:27bThe 27B package is about 17 GB. At the August 15 check, Ollama listed qwen3.6:latest at 24 GB and qwen3.6:35b at 24 GB. Use the explicit qwen3.6:27b tag when reproducing this comparison; an unversioned/latest tag is not the 17 GB 27B package.
Unified-Memory Guidance
| Mac configuration | Practical recommendation |
|---|---|
| 16 GB | Use a smaller model instead |
| 24 GB | Experimental; short context and memory pressure likely |
| 32 GB | Practical entry point for controlled local tests |
| 48 GB+ | Comfortable range for longer prompts and stable use |
| 64 GB+ | Better for parallel apps, larger context, and agent workflows |
Twenty-four gigabytes is below the comfortable range for both current Q4-class packages once macOS and KV cache are included. Thirty-two gigabytes can work with disciplined context. Forty-eight gigabytes provides the headroom that makes these models more useful as daily tools.
Multimodality
Ollama currently lists both gemma4:26b and qwen3.6:27b with text and image input. That does not mean every frontend exposes exactly the same vision features or that all model-card modalities work through every runtime.
Gemma is the clearer first choice when the workflow regularly mixes chat with screenshots, diagrams, or document pages. For code repositories and terminal agents, Qwen’s coding-oriented evaluation and ecosystem are the stronger reason to try it first.
Which Model Should You Choose?
Choose Gemma 4 26B A4B if:
- you want an efficient MoE all-rounder for text, reasoning, and images,
- you value the Gemma and MLX ecosystem on Apple Silicon,
- your tasks are broader than coding-agent benchmarks,
- you have enough memory for an 18 GB package plus runtime headroom.
Choose Qwen3.6 27B if:
- repository tasks and tool use are the main job,
- you want a model explicitly evaluated on coding-agent harnesses,
- terminal and code-edit loops matter more than general multimodal assistance,
- you can allocate at least 32 GB, preferably 48 GB or more, to the workflow.
Neither model is universally better. The right first test follows the task, not the highest benchmark number.
A Fair Local Test
If your Mac has enough storage and memory, install both and run the same prompts with the same context and settings:
ollama run gemma4:26b "Review this function and propose a minimal patch."
ollama run qwen3.6:27b "Review this function and propose a minimal patch."For a useful comparison, record:
- whether the model remains fully GPU-offloaded,
- time to first token and output rate,
- peak memory pressure,
- correctness on a task you can verify,
- number of tool-call or edit retries,
- response quality after the same context limit.
Do not compare one model at 4K context with the other at 64K and call the result an architecture benchmark.
Verdict: Two routes to local multimodality on Apple Silicon
Gemma 4 26B A4B is the broader local all-rounder; Qwen3.6 27B is the more obvious first candidate for coding agents and repository work. Their Ollama packages are close in size, but their architectures and published evaluation priorities differ.
For either model, the download size is only the starting point. Quantization, context, KV cache, and other applications determine whether the experience is smooth. Use 32 GB as an entry point, prefer 48 GB or more for regular work, and test both on a real task before choosing a default.
Sources and Status
Status: August 15, 2026. Ollama tags, package sizes, and model-card benchmark tables can change. No independent Mac benchmark is included.
Frequently Asked Questions
What does Ollama 0.31 change for Gemma 4 on Mac?
Ollama documents multi-token prediction (MTP) for Gemma 4 on its MLX path. The announcement reports nearly 90% faster average output on an Aider coding-agent benchmark, but that is a vendor benchmark and not a guarantee for every Mac, quantization or Gemma tag.
Is Gemma 4 26B A4B better than Qwen3.6 27B?
Not across every workflow. Qwen3.6 27B is positioned strongly for coding agents and repository tasks. Gemma 4 26B A4B is an efficient MoE all-rounder with a strong local multimodal path.
Which model is better for coding on a Mac?
Start with Qwen3.6 27B for coding agents, repository work, tool use and terminal workflows. Gemma 4 26B A4B remains useful for general assistance, reasoning and multimodal tasks.
Is 24 GB of unified memory enough?
It is below the comfortable range for both Ollama packages. Short contexts may work, but memory pressure and partial CPU offload are likely. Treat 32 GB as the practical entry point and 48 GB or more as the comfortable range.
How much context is realistic on a Mac?
The models advertise 256K context, but practical context depends on free unified memory, quantization and KV-cache size. Start far below the model maximum and verify memory use with `ollama ps`.
Are both models Mixture-of-Experts models?
No. Gemma 4 26B A4B uses an MoE architecture with about 3.8 billion active parameters per token. Qwen3.6 27B is a dense model, not a Mixture-of-Experts model. Its published card emphasizes agentic coding; the precise attention/SSM mechanism is not presented as a local-performance differentiator.
Which Ollama tags should I use?
Use `ollama run gemma4:26b` and `ollama run qwen3.6:27b`. At the August 15 check, Ollama listed `qwen3.6:latest` at 24 GB while the explicit 27B tag was 17 GB, so specify the Qwen tag when reproducing this comparison.