Gemma 4 vs Qwen 3.5: Open LLM Comparison (2026)

Two open-weight families dominate the self-hosting conversation in 2026: Google's Gemma 4 (released March 31, 2026) and Alibaba's Qwen 3.5. Both ship under Apache 2.0, both run on Ollama, and both punch well above their parameter counts. But they make different bets — Gemma 4 optimizes for the best model that fits a single consumer GPU, while Qwen 3.5 spans a far wider size ladder and leans into coding, agents, and multilingual coverage.

This guide compares them tier-by-tier on specs, benchmarks, coding, reasoning, and local deployment, so you can pick the right one for your stack rather than chasing leaderboard headlines.

What are Gemma 4 and Qwen 3.5?

Gemma 4 is Google DeepMind's open-weight family distilled from Gemini research. It ships in four tiers: two efficient small models (E2B and E4B), a 26B Mixture-of-Experts model that activates only ~3.8B parameters per token, and a 31B dense model. All four are multimodal (text + image), the smaller two add native audio input, and context runs up to 256K tokens. It supports 140+ languages and is licensed Apache 2.0 for unrestricted commercial use.

Qwen 3.5 is Alibaba's open-weight family and spans a much larger ladder — 0.8B, 2B, 4B, 9B, and 27B dense models, plus 35B-A3B, 122B-A10B, and a 397B-A17B MoE flagship. The flagship activates only ~17B parameters per forward pass while carrying 397B total. Context is 262K tokens natively across sizes, and the family is built for strong coding, agentic tool use, and multilingual breadth (200+ languages). It is also Apache 2.0.

How do the specs and benchmarks compare?

Here's the head-to-head on the load-bearing facts. Treat benchmark numbers as directional — vendors test under different harnesses — but the ordering is stable across independent runs.

The headline difference: Gemma 4 tops out at a 31B dense model, while Qwen 3.5 keeps climbing to a 397B MoE flagship. If you have the VRAM (or a multi-GPU box) and want the strongest raw capability, Qwen wins on ceiling. If you want the best model that fits one consumer card, the race is much closer.

How do they compare size-tier by size-tier?

The fairest comparisons are within the same memory budget, not across the whole family. Here's how the practical tiers line up:

• ~4B class (Gemma 4 E4B vs Qwen 3.5 4B): Both fit in ~5–6 GB at 4-bit and run comfortably on a laptop. Gemma 4 E4B brings vision and native audio; Qwen 3.5 4B is the better pure-text reasoner and coder. Pick by whether you need multimodal input.
• ~9B class (Qwen 3.5 9B): Gemma 4 has no direct 9B equivalent — you'd step up to the 26B MoE. The Qwen 3.5 9B is a standout: it reportedly matches or beats far larger models on reasoning benchmarks while running in ~6–8 GB. For a single mid-range GPU, this is one of the best value tiers in either family.
• ~26–31B class (Gemma 4 26B/31B vs Qwen 3.5 27B): The most-asked matchup. Gemma 4's 26B MoE is fast (only ~3.8B active params) and frugal; the 31B dense is the quality peak. Qwen 3.5 27B is the strongest dense open coder in its class. On a 24 GB card, all three are viable at 4-bit.
• Flagship (Qwen 3.5 397B MoE): Gemma 4 has no answer here. If you need frontier-class capability and can serve a large MoE (multi-GPU or a serving provider), only Qwen plays at this tier.

Which is better for coding?

For day-to-day coding and agentic tool use, Qwen 3.5 has the edge — it was tuned hard for code generation, repository-scale edits, and tool-calling agents. The 27B dense tier is widely cited as the best open dense coder in its size class on SWE-bench-style tasks, and the larger MoE tiers push into frontier territory on LiveCodeBench.

Gemma 4 is no slouch — the 31B posts strong LiveCodeBench and Codeforces numbers — but coding isn't its signature. Where Gemma 4 wins for developers is efficiency: the 26B MoE gives you near-31B quality at a fraction of the per-token compute, which matters when you're running an inline code assistant locally and care about latency.

Rule of thumb: building an autonomous coding agent or doing heavy repo work → Qwen 3.5. Want a fast, low-VRAM local copilot for completions and small edits → Gemma 4's 26B MoE is a great fit.

Which is better for reasoning, math, and multilingual?

Reasoning and math: Qwen 3.5 holds a slight edge thanks to a mature "thinking" mode and strong AIME/GPQA results, especially at the larger tiers. Gemma 4's 31B is highly competitive on math (strong AIME 2026 scores) and reasoning, and at the small end Gemma's efficient models reason well for their footprint.

Multilingual: Qwen 3.5 is the clearer winner if you need broad language coverage — 200+ languages versus Gemma 4's 140+, with particularly strong performance in Chinese and other Asian languages. Gemma 4 is excellent in English and major European languages and is improving on the long tail, but Qwen's multilingual breadth is a genuine differentiator for global products.

Long context: Roughly a wash at the top — Qwen's 262K native (extensible toward ~1M) slightly edges Gemma 4's 256K on the 26B/31B tiers, but Gemma's small models cap at 128K.

How do you run each one locally?

Both families are first-class citizens on Ollama, so the workflow is nearly identical. Install Ollama, then pull the tag for the tier you want:

# Gemma 4
ollama run gemma4:4b        # E4B — laptop-friendly, multimodal
ollama run gemma4:26b       # 26B MoE — fast, ~14–18 GB at 4-bit
ollama run gemma4:31b       # 31B dense — quality peak, ~20–24 GB

# Qwen 3.5
ollama run qwen3.5:4b       # laptop-friendly text reasoner/coder
ollama run qwen3.5:9b       # best value mid-tier, ~6–8 GB
ollama run qwen3.5:27b      # strongest dense open coder, ~16–20 GB

Approximate VRAM at 4-bit quantization (rule of thumb — add headroom for context and KV cache):

For most teams self-hosting on a single 24 GB GPU, the realistic finalists are Gemma 4 26B MoE (fast, frugal, multimodal) and Qwen 3.5 27B (best dense coder). Both fit; the choice comes down to workload.

What about Apple Silicon and MLX?

Both run well on Apple Silicon. The simplest path is Ollama (which uses Metal under the hood) for either family. For maximum throughput on a Mac, use MLX — Apple's array framework — with MLX-format builds of these models, which the community publishes on Hugging Face shortly after each release.

• 16 GB Macs: Comfortable with the ~4B tiers and Qwen 3.5 9B at 4-bit. Leave headroom for the OS and context.
• 32 GB Macs: Can run Gemma 4 26B MoE or Qwen 3.5 27B at 4-bit. The Gemma MoE will feel snappier because it activates fewer parameters per token.
• 64 GB+ Macs: Gemma 4 31B dense runs comfortably; you can also serve larger Qwen MoE tiers depending on quantization.

On unified memory, the MoE designs (Gemma 4 26B, the Qwen MoE tiers) are particularly attractive — you pay the memory cost of the full model but the speed of a much smaller active set, which is exactly what you want on a memory-rich but bandwidth-bound Mac.

Which should you pick by use case?

The honest summary: Qwen 3.5 is the better default for engineering teams — broader size ladder, stronger coding and agents, wider language coverage. Gemma 4 wins where efficiency and multimodality matter most — the best capability-per-gigabyte at the small and mid tiers, plus native vision and on-device audio. Many teams end up running both: Gemma 4 for fast, cheap inference on edge or laptop, Qwen 3.5 for the heavy coding and reasoning lifts.

Choosing and operating local LLM infrastructure — quantization, serving, and the agent tooling around it — is its own discipline. If you're standing up a self-hosted model stack and need the engineering capacity to do it right, Codersera helps you hire vetted remote developers and extend your team with engineers who've shipped production LLM systems, so you can move from prototype to reliable deployment without the long recruiting cycle.

FAQ

Is Gemma 4 or Qwen 3.5 better for coding?

Qwen 3.5 is generally the stronger coder, especially for agentic tool use and repository-scale edits — its 27B dense tier leads open dense coders in its class. Gemma 4's 31B is competitive, and its 26B MoE is excellent for fast, low-VRAM local copilots.

Are both Gemma 4 and Qwen 3.5 free to use commercially?

Yes. Both ship under the Apache 2.0 license, which permits unrestricted commercial use, modification, and redistribution. Always confirm the exact license file on the model's Hugging Face page before shipping in production.

Which model needs less VRAM to run locally?

At comparable tiers they're close. Gemma 4's small models (E2B/E4B) are very frugal (~1.5–5 GB), and its 26B MoE is efficient because it activates only ~3.8B parameters per token. Qwen 3.5's 9B is a standout mid-tier at ~6–8 GB. For a single 24 GB GPU, both the Gemma 4 26B and Qwen 3.5 27B fit at 4-bit.

Which has the longer context window?

Qwen 3.5 offers 262K tokens natively across sizes (extensible toward ~1M), slightly ahead of Gemma 4's 256K on its 26B/31B tiers. Gemma 4's small models cap at 128K.

Which is better for non-English languages?

Qwen 3.5, by a clear margin — it supports 200+ languages with particularly strong Chinese and Asian-language performance, versus Gemma 4's 140+. For a global or non-English-first product, Qwen 3.5 is the safer default.

Can I run either on a Mac?

Yes. Use Ollama for the simplest setup (it uses Metal automatically), or MLX-format builds for maximum throughput on Apple Silicon. A 16 GB Mac handles the ~4B tiers and Qwen 3.5 9B; 32 GB handles Gemma 4 26B or Qwen 3.5 27B at 4-bit.

Should I just pick one, or run both?

Many teams run both — Gemma 4 for fast, cheap inference on laptops or edge (and multimodal tasks), and Qwen 3.5 for heavy coding, agents, and multilingual work. Since both are Apache 2.0 and both run on Ollama, the operational overhead of supporting two is low.