Gemma 4 vs Qwen 3.5: Benchmarks and Local Performance Compared
A fair, data-driven comparison of Google's Gemma 4 and Alibaba's Qwen 3.5 — covering benchmark scores, Arena AI rankings, hardware requirements, and practical differences for models you can run on laptops and desktop workstations.
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 three size classes that matter for local use: laptop (4B), 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 |
| 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 a 9B model and larger MoE variants (122B-A10B, 397B-A17B) that Gemma 4 doesn't match directly. We're focusing on the sizes you can run locally.
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.
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 |
| 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 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 want native audio input on smaller models
- 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 (9B, 122B, 397B options)
- 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 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