Gemma 4 vs Qwen 3.5: E4B, 12B, 26B, 31B Benchmark Matchups

Two of the strongest open model families in 2026 — Google's Gemma 4 and Alibaba's Qwen 3.5 — both promise frontier-level AI that runs on your own hardware. Both ship under Apache 2.0 licenses. Both have models that fit on a laptop. And both have benchmark numbers that look impressive in isolation.

The useful question isn't which family is "better" in the abstract. It's which model wins at the size you can actually run. This article puts them head-to-head at four size classes that matter for local use: laptop (4B), the new multimodal middleweight (12B/9B), workstation dense (~30B), and efficient MoE.

The Matchups

Before comparing numbers, we need to match the right models against each other. These families have different naming conventions and architectures, so here's how the pairings work:

Use Case	Gemma 4	Qwen 3.5	Why This Pairing
Laptop / light local use	E4B (~4B effective)	4B	Smallest models practical for everyday laptop work
Multimodal middleweight	12B	9B	Closest size-matched dense pairing between the two families
Dense workstation	31B	27B	Largest dense model in each family
Efficient MoE	26B A4B (3.8B active)	35B-A3B (3B active)	Mid-size MoE with ~3–4B active parameters

A note on naming: Gemma 4's "E4B" means effective-4B — the model has 8B total parameters (including embeddings) but performs like a 4B during inference. Qwen 3.5's "35B-A3B" means 35B total, 3B active. Different naming, similar concepts.

Qwen 3.5 also has larger MoE variants (122B-A10B, 397B-A17B) that Gemma 4 doesn't match directly. We're focusing on the sizes you can run locally. Gemma 4's five-model lineup (E2B, E4B, 12B, 26B A4B, 31B) grew by one in June 2026 when Google added the 12B — see the dedicated matchup below.

A Note on Model Freshness (Updated July 2026)

Since this article's benchmarks were first gathered in April 2026, Alibaba has shipped two newer generations on top of Qwen 3.5: Qwen 3.6 (open-weight 27B dense and 35B-A3B MoE checkpoints, released April 16–22, 2026) and Qwen 3.7 (Max and Plus, hosted-API only with no downloadable weights, May–June 2026). Qwen 3.6's 27B/35B-A3B checkpoints are tuned for agentic coding and are not a clean upgrade — on the broader benchmark suite, Qwen 3.5 27B still out-scores Qwen 3.6-35B-A3B on more individual metrics than it loses (source). More importantly for the new section below: Alibaba has not released a Qwen 3.6 or 3.7 version of the 9B or 4B small models. Qwen 3.5 9B and 4B (March 2026) remain Alibaba's current small-model generation, so they're still the correct, current rivals to Gemma 4 E4B and Gemma 4 12B.

Two Ways to Measure: Benchmarks vs. Real-World Preference

This comparison draws on two types of evidence that don't always agree:

Static benchmarks — standardized tests like MMLU-Pro (knowledge), GPQA Diamond (science), LiveCodeBench (coding), and TAU2 (agentic tool use). These come from each family's official model cards, tested under controlled conditions. Numbers from Google's Model Card and Qwen's official cards on Hugging Face.

Arena AI — a third-party leaderboard where real users compare model outputs in blind head-to-head tests, then vote for which response they prefer. This captures something benchmarks miss: overall assistant quality, tone, helpfulness, and how the model handles real prompts.

The pattern across this comparison is consistent: static benchmarks tend to favor Qwen 3.5 on more individual rows, while Arena AI chat preference currently favors Gemma 4 at the top end. Keep that dynamic in mind as you read the tables below.

Dense Workstation: Gemma 4 31B vs. Qwen 3.5 27B

This is the marquee matchup — the largest dense model each family offers, and the most interesting one because the two types of evidence pull in slightly different directions.

Static Benchmarks

Benchmark	Gemma 4 31B	Qwen 3.5 27B	Edge
MMLU-Pro (knowledge & reasoning)	85.2%	86.1%	Qwen (+0.9)
GPQA Diamond (expert science)	84.3%	85.5%	Qwen (+1.2)
LiveCodeBench v6 (coding)	80.0%	80.7%	Qwen (+0.7)
TAU2 (agentic tool use)	76.9%	79.0%	Qwen (+2.1)
MMMLU (multilingual reasoning)	88.4%	85.9%	Gemma (+2.5)
MMMU-Pro (multimodal reasoning)	76.9%	75.0%	Gemma (+1.9)

By row count, Qwen 3.5 27B wins on text-heavy tasks: knowledge, science, coding, and agentic behavior. But the margins are tight — typically 1–2 percentage points. Gemma 4 31B pulls ahead on multilingual and multimodal reasoning, which matters if your workload involves non-English content or image understanding.

Arena AI (Real-World Preference)

Model	Elo Score	Rank (Open Source)
Gemma 4 31B	1452 ± 9	#3
Qwen 3.5 27B	1404 ± 6	Lower

An Elo gap of ~48 points is significant. When real users compare outputs blind, they consistently prefer Gemma 4 31B's responses. This suggests Gemma's instruction tuning produces responses that feel more helpful, better structured, or more natural — even if the raw benchmark margins are slim.

Verdict for Dense ~30B

The static benchmarks are essentially a draw with slight Qwen edges on text tasks. But Arena AI strongly favors Gemma 4 for overall assistant quality. If you're building a general-purpose local assistant, Gemma 4 31B has the better real-world signal. If your workload is specifically text reasoning or agentic pipelines where TAU2-style structured tasks dominate, Qwen 3.5 27B is the safer bet.

Efficient MoE: Gemma 4 26B A4B vs. Qwen 3.5 35B-A3B

Both families offer a Mixture of Experts model that trades total parameters for inference speed. Gemma's version activates 3.8B out of 26B; Qwen's activates 3B out of 35B.

Static Benchmarks

Benchmark	Gemma 4 26B A4B	Qwen 3.5 35B-A3B	Edge
MMLU-Pro	82.6%	85.3%	Qwen (+2.7)
GPQA Diamond	82.3%	84.2%	Qwen (+1.9)
LiveCodeBench v6	77.1%	74.6%	Gemma (+2.5)
TAU2	68.2%	81.2%	Qwen (+13.0)
MMMLU	86.3%	85.2%	Gemma (+1.1)
MMMU-Pro	73.8%	75.1%	Qwen (+1.3)

Qwen 3.5 35B-A3B wins more rows here, and the TAU2 gap (13 points) is the largest in any matchup. If agentic tool use is your primary concern, the Qwen MoE has a clear advantage.

However, Gemma 4 26B A4B wins on LiveCodeBench (actual coding) and MMMLU (multilingual), which matters for developers doing multilingual coding work.

Arena AI

Model	Elo Score
Gemma 4 26B A4B	1441 ± 9
Qwen 3.5 35B-A3B	1400 ± 6

Again, Arena AI favors Gemma — a 41-point Elo gap suggests meaningfully better chat quality as judged by human voters.

Verdict for MoE

Qwen 3.5 35B-A3B is the stronger all-around model on static benchmarks, especially for agentic workflows. Gemma 4 26B A4B is better for coding and multilingual tasks, and significantly preferred by real users in blind tests. If you need a fast MoE for structured agent pipelines, lean Qwen. For a general-purpose efficient model, the Arena AI evidence favors Gemma.

Multimodal Middleweight: Gemma 4 12B vs. Qwen 3.5 9B

Google's June 3, 2026 release of Gemma 4 12B added a fifth model to the Gemma 4 family, and it lands in a size class Gemma didn't previously cover: bigger than the laptop-class E4B, smaller than the 26B MoE. Its natural size-matched rival is Qwen 3.5 9B, released three months earlier on March 2, 2026. (Google's announcement | Developer guide)

A note on naming, since search traffic pairs these models several different ways: Qwen 3.5 9B vs. Gemma 4 E4B is an apples-to-oranges comparison — E4B is Gemma's ~4B-effective laptop model, roughly half of Qwen's 9B. The size-matched rival to Qwen 3.5 9B is Gemma 4 12B (this section); the size-matched rival to Gemma 4 E4B is Qwen 3.5 4B, covered in the Laptop Class section below.

Static Benchmarks

Benchmark	Gemma 4 12B	Qwen 3.5 9B	Edge
MMLU-Pro (knowledge & reasoning)	77.2%	82.5%	Qwen (+5.3)
GPQA Diamond (expert science)	78.8%	81.7%	Qwen (+2.9)
LiveCodeBench v6 (coding)	72.0%	65.6%	Gemma (+6.4)
TAU2 (agentic tool use)	69.0%	79.1%	Qwen (+10.1)
MMMLU (multilingual reasoning)	83.4%	81.2%	Gemma (+2.2)
MMMU-Pro (multimodal reasoning)	69.1%	70.1%	Qwen (+1.0)

Sources: Gemma 4 official model card, Qwen3.5-9B model card on Hugging Face.

The pattern from the other size classes repeats: Qwen 3.5 9B wins knowledge, science, and agentic tool-use benchmarks by clear margins (the TAU2 gap is the second-largest in this whole comparison, after the 4B tier). Gemma 4 12B claims the practical coding benchmark and multilingual reasoning. MMMU-Pro is close enough to call a tie.

We could not find a published Arena AI (LMArena) Elo score for either Gemma 4 12B or Qwen 3.5 9B — the leaderboard currently tracks the flagship-size models compared elsewhere in this article, not this middle tier. We're not going to invent a number here; if Arena adds both models, we'll update this section.

Multimodal and Audio: The Real Differentiator

This is where the two models genuinely diverge. Gemma 4 12B is an encoder-free, unified multimodal model that natively handles text, image, and audio in a single decoder — including automatic speech recognition and speaker diarization — with a 35M-parameter vision embedder feeding directly into the language model's embedding space (developer guide). On Google's own audio benchmarks it scores 38.5 on CoVoST (speech translation) and a 0.069 word-error rate on FLEURS, both excluding Chinese (Gemma 4 model card).

Qwen 3.5 9B is vision-language only — strong image and video understanding (MMBench 90.1%, VideoMME 84.5%) but no native audio input or output (Hugging Face model card). If your project needs voice input, transcription, or an audio-in/text-out pipeline without bolting on a separate ASR model, Gemma 4 12B is the only one of the two that does it natively — this is the same audio advantage Gemma 4 E4B has over Qwen 3.5 4B at the smaller size, now available at a more capable tier.

Memory Footprint on 16 GB Machines

Model	Q4 quantization	Practical fit
Gemma 4 12B	~6.6–6.7 GB VRAM	Runs on an 8 GB GPU; 16 GB recommended for the full 256K context
Qwen 3.5 9B	~5 GB (min. 5 GB RAM, 8.4 GB recommended)	Comfortable on 8 GB machines

Sources: Techsy.io Gemma 4 12B guide (Q4_K_M VRAM), CanIRun.ai Qwen3.5-9B page (RAM requirements).

Both are comfortably 16 GB-machine citizens at 4-bit quantization — this is exactly the tier Google targeted with the 12B launch. Qwen 3.5 9B's smaller parameter count gives it a bit more headroom for long-context work or running alongside other processes on the same machine.

Context windows are close: Gemma 4 12B ships with 256K tokens natively; Qwen 3.5 9B ships with 262K, extensible to roughly 1M via YaRN.

Licensing

Both ship under Apache 2.0 — no restrictions on commercial use, fine-tuning, or redistribution. This matches every other size class in this article: licensing is a wash across the board.

Verdict: Which One for What

• Need native audio (voice input, transcription, ASR pipelines)? Gemma 4 12B is the only option here — Qwen 3.5 9B has no audio modality at all.
• Pure text, knowledge, or agentic tool-use workload with no audio or image needs? Qwen 3.5 9B's benchmark lead — especially the 10-point TAU2 gap — makes it the stronger pick, with a slightly smaller footprint to match.
• Coding-heavy, multilingual, or general-purpose assistant work where you don't yet know exactly what you'll throw at it? Gemma 4 12B's LiveCodeBench and MMMLU edges, plus its unified multimodal design, make it the safer default.
• Strict 8 GB budget? Qwen 3.5 9B's lower minimum RAM requirement gives it a slight edge, though both fit.

Laptop Class: Gemma 4 E4B vs. Qwen 3.5 4B

This is the tier most people will actually run on a laptop with 8–16 GB of RAM.

Static Benchmarks

Benchmark	Gemma 4 E4B	Qwen 3.5 4B	Edge
MMLU-Pro	69.4%	79.1%	Qwen (+9.7)
GPQA Diamond	58.6%	76.2%	Qwen (+17.6)
LiveCodeBench v6	52.0%	55.8%	Qwen (+3.8)
TAU2	42.2%	79.9%	Qwen (+37.7)
MMMLU	76.6%	76.1%	Gemma (+0.5)
MMMU-Pro	52.6%	66.3%	Qwen (+13.7)

This is the most lopsided matchup. Qwen 3.5 4B wins on nearly every metric, and the margins are often double-digits. The TAU2 gap alone (37.7 points) is striking.

Gemma 4 E4B's only advantage is a 0.5-point edge on multilingual reasoning — and native audio input, which Qwen 3.5 4B doesn't support.

Verdict for 4B Class

On pure benchmark performance, Qwen 3.5 4B is clearly stronger at this size. The gaps are too large to be explained by methodology differences.

That said, Gemma 4 E4B has structural advantages that don't show up in these tables: native audio processing, deeper Google mobile/edge ecosystem integration, and 128K context. If you specifically need on-device audio or are building in the Android ecosystem, Gemma E4B still has a role. For everything else at this size, Qwen 3.5 4B is the better performer.

Hardware Requirements for Local Use

Both model families run locally through tools like Ollama and LM Studio. Here's how their memory footprints compare at 4-bit quantization:

Matchup	Gemma 4 (Q4)	Qwen 3.5 (Q4)	Notes
E4B vs 4B	~5 GB	~4 GB	Both fit on 8 GB machines
12B vs 9B	~6.6–6.7 GB	~5 GB	Both fit 16 GB machines; Qwen is lighter, Gemma adds native audio
26B A4B vs 35B-A3B	~15.6 GB	~19–22 GB	Gemma is more memory-efficient
31B vs 27B	~17.4 GB	~16–17 GB	Very close; both need 24 GB GPU comfortably

A notable practical difference: Qwen 3.5 uses a hybrid attention architecture (Gated DeltaNet + full attention in a 3:1 ratio) that results in a roughly 75% smaller KV cache than traditional transformers. In practice, this means Qwen's memory usage scales more slowly with long conversations and large context windows. Gemma 4 uses its own hybrid sliding window attention to manage memory, but Qwen has the edge at very long context lengths.

Context windows also differ:

• Gemma 4 E4B: 128K tokens | Qwen 3.5 4B: up to 262K tokens
• Gemma 4 12B: 256K tokens | Qwen 3.5 9B: 262K tokens (extensible to ~1M via YaRN)
• Gemma 4 31B / 26B: 256K tokens | Qwen 3.5 27B / 35B: up to 262K tokens (extendable further)

At the larger sizes, context windows are comparable. At the small end, Qwen offers more room.

The Bigger Picture

Here's an honest summary of where each family stands:

Gemma 4 is stronger when:

• Overall assistant quality matters (Arena AI preference)
• You need multilingual reasoning (MMMLU advantages)
• Multimodal understanding is important (MMMU-Pro edges at 31B)
• You need native audio input — available on E2B, E4B, and now the 12B (Qwen 3.5 has no audio modality on any size)
• You're in the Google/Android ecosystem

Qwen 3.5 is stronger when:

• Static benchmark performance per-parameter matters
• You need the strongest 4B-class model for a laptop
• Agentic tool use is critical (TAU2 advantages)
• You want a broader model lineup, including sizes Gemma doesn't match (122B-A10B, 397B-A17B)
• You need maximum context length on smaller models

They're essentially tied when:

• You compare the dense ~30B models on static benchmarks (margins of 1–2 points)
• Licensing flexibility (both Apache 2.0)
• Local deployment tooling (both work with Ollama, LM Studio, llama.cpp, vLLM)

Which Should You Pick?

If you're running on a laptop with limited RAM and need the best small model: start with Qwen 3.5 4B. The benchmark gap at this size is clear.

If you want a multimodal middleweight with native audio: Gemma 4 12B is the only model at this size that handles voice input natively — its June 2026 release fills exactly this gap. For a pure text/agentic workload in the same size class with no audio needs, Qwen 3.5 9B posts the stronger raw benchmarks, especially on agentic tool use.

If you want a dense workstation model that excels as a general-purpose assistant: Gemma 4 31B has the strongest real-world preference signal and better multilingual/multimodal balance.

If you want an efficient MoE for fast inference: both are strong choices. Qwen 3.5 35B-A3B for agent-heavy workloads, Gemma 4 26B A4B for coding and multilingual tasks.

The real answer, of course, is to test both on your actual prompts. Both families are free, both run on the same tools, and switching between them is as simple as changing a model name. The benchmarks tell you where to start looking — but your use case decides the winner.

What's Next?

• Set up Gemma 4 locally: Run Gemma 4 with Ollama or LM Studio
• Check your hardware: Gemma 4 Hardware Requirements
• Dive deeper into Gemma 4's numbers: Gemma 4 Benchmarks: Full Breakdown