The Contenders
Gemma 4 is Google DeepMind's latest open-weights family. The headline features are multimodal input (text, image, and audio on E2B/E4B variants), a PLE (Parameter-Light Experts) architecture on the MoE models, native function calling on every variant, and 256K context across the board. The 31B dense model sits at Arena rank #3 globally, while the 26B-A4B MoE holds Arena #6 — extraordinary for models you can run on a MacBook Pro.
Qwen 3.5 is Alibaba Cloud's latest series, continuing the momentum from Qwen 2.5 and Qwen 3. It offers the widest size range of any open family (0.8B to 397B), a thinking/non-thinking toggle that lets you trade latency for reasoning depth, and strong raw benchmark scores especially on coding tasks. The 35B MoE variant activates roughly 8B parameters per token and is the primary competitor to Gemma 4's MoE.
Head-to-Head: Small Models (E4B vs Qwen 3.5 4B)
The 4B tier is where most Mac users start — these models run comfortably on any Apple Silicon Mac including 8 GB machines. According to LLMCheck benchmarks on an M4 MacBook Pro:
| Factor | Gemma 4 E4B | Qwen 3.5 4B | Winner |
|---|---|---|---|
| Parameters | 4B | 4B | Tie |
| License | Apache 2.0 | Apache 2.0 | Tie |
| RAM (Q4) | ~3 GB | ~3 GB | Tie |
| Speed (tok/s) | ~125 | ~132 | Qwen |
| capScore | 16 | 12 | Gemma |
| Multimodal | Text + Image + Audio | Text only | Gemma |
| Context Window | 256K | 32K | Gemma |
| Function Calling | Native | Template-based | Gemma |
Key takeaway: Gemma 4 E4B dominates this tier. It matches Qwen 3.5 4B on RAM and gets within 5% on speed, but adds multimodal capabilities, a dramatically larger context window, and a 33% higher capScore. The only reason to pick Qwen 3.5 4B here is if raw text-generation speed is your single priority.
Head-to-Head: Mid-Range MoE Models
The mixture-of-experts tier is the most interesting battleground. Both models use MoE to deliver high capability at moderate RAM cost, but their architectures differ significantly. According to LLMCheck testing:
| Factor | Gemma 4 26B-A4B | Qwen 3.5 35B MoE | Winner |
|---|---|---|---|
| Total / Active Params | 26B / 3.8B active | 35B / ~8B active | — |
| License | Apache 2.0 | Apache 2.0 | Tie |
| RAM (Q4) | ~18 GB | ~24 GB | Gemma |
| Speed (tok/s) | ~48 | ~45 | Gemma |
| capScore | 35 | 27 | Gemma |
| Arena Ranking | #6 | Not ranked | Gemma |
| Context Window | 256K | 262K | Tie |
| Thinking Mode | No | Yes (toggle) | Qwen |
| Coding Benchmarks | Strong | Stronger | Qwen |
This is where the decision gets nuanced. Gemma 4 26B-A4B uses 6 GB less RAM, runs 3 tok/s faster, scores 30% higher on capScore, and holds Arena #6. But Qwen 3.5 35B MoE has a thinking mode toggle that lets you activate deeper reasoning on demand, and its raw coding benchmark scores edge ahead. For developers who live in their code editor, Qwen's coding edge matters. For everyone else, Gemma's efficiency and overall quality win.
Head-to-Head: Flagship Dense (~31-32B)
At the dense flagship tier, we compare Gemma 4 31B against Qwen 3 32B (Qwen 3.5 does not yet have a dense 32B variant). This is the tier for users with 32 GB+ Macs who want maximum capability without cloud dependency.
Gemma 4 31B holds Arena rank #3 globally — tied with models that cost hundreds of dollars per month via API. It requires approximately 20 GB of RAM and generates around 24 tok/s. Qwen 3 32B needs about 22 GB and runs at a similar 25 tok/s, but its capScore of 25 falls well short of Gemma's 40. According to LLMCheck analysis, the Gemma 4 31B dense model is the single best local LLM you can run on a Mac today when quality is the top priority.
Arena #3 on your laptop: Gemma 4 31B dense matches or exceeds the chat quality of cloud models that were state-of-the-art six months ago. If you have a 32 GB or 64 GB Mac, this is the model to try first.
Multimodal & Function Calling
This is Gemma 4's clearest differentiator. The E2B and E4B variants process text, images, and audio natively — you can ask them to describe a photo, transcribe speech, or analyze a diagram without any external pipeline. Qwen 3.5 is text-only at launch (Alibaba offers separate Qwen2.5-VL models for vision, but they are not part of the 3.5 series).
On function calling, Gemma 4 includes native tool-use support across all variants. This makes it exceptionally strong for agentic workflows: you can define tools, have the model call them, and process the results in a structured loop. Qwen 3.5 supports function calling through its chat template, but community reports indicate Gemma 4 is more reliable at generating correctly formatted tool calls, particularly with complex multi-tool schemas.
For developers building AI agents, RAG pipelines, or multimodal applications on Mac, Gemma 4 is the clear choice in this category.
The Verdict — Which Should You Choose?
According to LLMCheck, the right choice depends on what you are building:
Choose Gemma 4 if you need:
Best overall chat quality (Arena #3/#6). Multimodal input — images and audio on-device. Reliable function calling for agentic workflows. Lower RAM usage at the MoE tier. 256K context on every variant. Multilingual support across 140+ languages.
Choose Qwen 3.5 if you need:
Top raw coding benchmark scores. Thinking mode toggle for deeper reasoning on demand. The widest range of model sizes (0.8B to 397B). Marginally faster tok/s at the same parameter count. A model ecosystem you are already invested in.
For most Mac users running local AI in 2026, Gemma 4 is the stronger default. Its Arena rankings, multimodal capabilities, function calling reliability, and efficient RAM usage give it an edge across the majority of use cases. Qwen 3.5 remains a strong alternative for coding-heavy workflows and for users who need extreme size flexibility or the thinking mode toggle.
The good news: both families are Apache 2.0 licensed, both run efficiently on Apple Silicon via Ollama or LM Studio, and both continue to improve rapidly. You can install both and switch between them depending on the task.