Qwen 3.5 vs Gemma 4 — Alibaba vs Google Open Models Compared (2026)
📢 Update: Qwen 3.6 is now available. See the Qwen 3.6 complete guide, how to run 3.6-27B locally, and Qwen 3.6 vs 3.5 comparison.
Qwen 3.5 and Gemma 4 are both Apache 2.0, both run locally, and both are genuinely good. But they’re built for different strengths. Here’s how to choose.
Quick comparison
| Qwen 3.5 | Gemma 4 | |
|---|---|---|
| Maker | Alibaba | Google DeepMind |
| License | Apache 2.0 | Apache 2.0 |
| Model range | 0.6B — 110B | 2.3B — 31B |
| Architecture | MoE + Dense | MoE + Dense |
| Max context | 128K | 256K |
| Multimodal | Text only (base) | Text + Image + Audio |
| Coding variants | ✅ Qwen 2.5 Coder | ❌ General only |
| Strongest at | Coding, multilingual | Edge/on-device, efficiency |
Model sizes compared
| Qwen 3.5 | Params | Gemma 4 | Params | |
|---|---|---|---|---|
| Flash | 0.6B | E2B | 2.3B (5.1B total) | |
| — | — | E4B | 4.5B (8B total) | |
| Plus | ~30B active (110B total) | 26B MoE | 3.8B active (26B total) | |
| — | — | 31B Dense | 31B |
Qwen 3.5 goes bigger (110B total) and smaller (0.6B). Gemma 4 has more options in the middle with its edge models. Neither family has a direct size-for-size competitor to the other.
The most interesting matchup is Qwen 3.5 Plus (~30B active) vs Gemma 4 26B MoE (3.8B active). Qwen activates 8x more parameters per inference — it’s more powerful but needs more hardware.
Benchmarks
General knowledge and reasoning
| Benchmark | Qwen 3.5 Plus | Gemma 4 26B | Gemma 4 31B |
|---|---|---|---|
| MMLU | 82.1 | 83.2 | 85.1 |
| ARC-C | 89.5 | 91.3 | 92.1 |
| GSM8K | 87.3 | 89.1 | 90.5 |
Gemma 4 leads on general reasoning — surprising given its lower active parameter count. The MoE routing is exceptionally efficient.
Coding
| Benchmark | Qwen 3.5 Plus | Qwen 2.5 Coder 32B | Gemma 4 26B |
|---|---|---|---|
| HumanEval | 76.8 | 84.2 | 78.5 |
| MBPP | 74.1 | 81.5 | 75.3 |
Qwen wins coding decisively — especially with the dedicated Qwen 2.5 Coder variant. If coding is your primary use case, Qwen is the clear choice. See our best AI models for coding locally ranking.
Multilingual
| Benchmark | Qwen 3.5 Plus | Gemma 4 26B |
|---|---|---|
| MGSM (multilingual math) | 88.9 | 82.4 |
| XWinograd | 85.3 | 79.1 |
Qwen dominates multilingual tasks. It was trained with a strong emphasis on CJK languages (Chinese, Japanese, Korean) and performs well across 100+ languages. Gemma 4 supports 140+ languages but doesn’t match Qwen’s depth in non-English tasks.
Hardware and efficiency
This is where Gemma 4 shines:
| Model | Active params | RAM (Q4) | Tokens/sec (laptop CPU) |
|---|---|---|---|
| Gemma 4 26B MoE | 3.8B | 8 GB | 8 tok/s |
| Qwen 3.5 Plus | ~30B | 16 GB | 3 tok/s |
| Gemma 4 31B Dense | 31B | 16 GB | 3 tok/s |
Gemma 4 26B runs on half the RAM and at 2-3x the speed of Qwen 3.5 Plus. If you’re on a laptop with 8 GB RAM, Gemma 4 is your only option from these two families.
For the absolute minimum hardware, Gemma 4 E2B fits in 2 GB — see best AI models under 4GB RAM. Qwen 3.5 Flash (0.6B) is even smaller but noticeably weaker.
Ecosystem
Ollama support
Both work with Ollama:
ollama run gemma4:26b
ollama run qwen3.5:plusAPI availability
| Free local | Free API | Paid API | |
|---|---|---|---|
| Qwen 3.5 | ✅ | — | DashScope, OpenRouter |
| Gemma 4 | ✅ | Google AI Studio | Vertex AI |
Gemma 4 has a free API through Google AI Studio. Qwen 3.5 requires a paid API provider. For API usage, see our Qwen 3.5 API guide.
Dedicated variants
Qwen has a significant advantage here:
- Qwen 2.5 Coder — dedicated coding model (comparison)
- Qwen 2.5 Math — dedicated math model
Gemma 4 is general-purpose only. No dedicated coding or math variants.
Fine-tuning community
Both have active fine-tuning communities on Hugging Face. Qwen has more fine-tunes available due to its longer time in market. Gemma 4 is catching up fast.
Which should you pick?
Pick Qwen 3.5 if:
- Coding is your primary use case
- You work in non-English languages (especially CJK)
- You want dedicated model variants (Coder, Math)
- You have 16+ GB RAM
Pick Gemma 4 if:
- You need to run on limited hardware (8 GB RAM or less)
- You need multimodal (text + image + audio)
- You want longer context (256K vs 128K)
- You’re deploying to edge/mobile devices
- Speed matters more than maximum quality
Use both if:
- Gemma 4 26B for quick daily tasks (fast, efficient)
- Qwen 2.5 Coder 32B for serious coding sessions (higher quality)
Further reading
- Gemma 4 setup guide
- Qwen 3.5 setup guide
- Gemma 4 vs Llama 4 vs Qwen 3.5 — three-way comparison
- Qwen 3.5 vs DeepSeek V3
- Qwen 3.5 vs MiMo V2 Pro
- Best free AI models 2026
Related: AI Coding Tools Pricing