Gemma 4 vs Llama 4 vs Qwen 3.5 — Which Open Model Wins? (2026)
Three model families dominate open-source AI in 2026: Google’s Gemma 4, Meta’s Llama 4, and Alibaba’s Qwen 3.5. All are free to use, all run locally, and all are genuinely good. But they’re built for different things.
Quick comparison
| Gemma 4 | Llama 4 | Qwen 3.5 | |
|---|---|---|---|
| Maker | Google DeepMind | Meta | Alibaba |
| License | Apache 2.0 | Llama License | Apache 2.0 |
| Smallest model | 2.3B (E2B) | 17B (Scout) | 0.6B (Flash) |
| Largest model | 31B (Dense) | 400B (Maverick) | 110B (Plus) |
| Max context | 256K | 10M | 128K |
| Multimodal | Text + Image + Audio | Text + Image | Text only (base) |
| Architecture | MoE + Dense | MoE | MoE + Dense |
| Best at | Edge/on-device | Raw scale + context | Coding + multilingual |
License matters
Gemma 4 and Qwen 3.5 both use Apache 2.0 — the most permissive open-source license. You can use them commercially, modify them, redistribute them, and build proprietary products on top without restrictions.
Llama 4 uses Meta’s custom license. It’s free for most uses, but companies with over 700 million monthly active users need a separate license from Meta. For most developers and startups, this doesn’t matter. For enterprises, it’s worth checking.
If licensing flexibility is critical, Gemma 4 or Qwen 3.5 are safer choices.
Model sizes compared
Small models (runs on any laptop)
| Model | Active params | RAM (Q4) | Quality |
|---|---|---|---|
| Gemma 4 E2B | 2.3B | 2 GB | ⭐⭐ |
| Qwen 3.5 Flash | 0.6B | 1 GB | ⭐ |
| Gemma 4 E4B | 4.5B | 4 GB | ⭐⭐⭐ |
Gemma 4 wins the small model category. The E2B delivers surprisingly good results for 2.3B parameters, and the E4B adds audio support. Qwen 3.5 Flash is smaller but noticeably weaker.
Llama 4 doesn’t compete here — its smallest model (Scout) is 17B parameters.
Medium models (gaming PC or Mac)
| Model | Active params | RAM (Q4) | Quality |
|---|---|---|---|
| Gemma 4 26B MoE | 3.8B active | 8 GB | ⭐⭐⭐⭐ |
| Llama 4 Scout | 17B active | 12 GB | ⭐⭐⭐⭐ |
| Qwen 3.5 Plus | ~30B active | 16 GB | ⭐⭐⭐⭐⭐ |
| Gemma 4 31B Dense | 31B | 16 GB | ⭐⭐⭐⭐⭐ |
This is where it gets interesting. Gemma 4 26B activates only 3.8B parameters per inference but delivers quality comparable to models 5x its active size. It’s the most efficient option.
Llama 4 Scout needs more RAM but offers a 10M token context window — unmatched by anything else. If you’re processing entire codebases or book-length documents, Scout is the only choice.
Qwen 3.5 Plus edges ahead on raw benchmark scores, especially for coding and multilingual tasks, but requires more hardware.
Large models (datacenter or multi-GPU)
| Model | Params | Hardware needed | Quality |
|---|---|---|---|
| Llama 4 Maverick | 400B | Multi-GPU (200+ GB) | ⭐⭐⭐⭐⭐ |
| Qwen 3.5 Plus 110B | 110B | 1-2 GPUs (60+ GB) | ⭐⭐⭐⭐⭐ |
Gemma 4 doesn’t have a model in this tier. If you need maximum quality and have the hardware, Llama 4 Maverick is the most powerful open model available.
Benchmarks head-to-head
Comparing the most popular model from each family at similar compute budgets:
| Benchmark | Gemma 4 26B | Llama 4 Scout | Qwen 3.5 Plus |
|---|---|---|---|
| MMLU (knowledge) | 83.2 | 79.8 | 82.1 |
| HumanEval (coding) | 78.5 | 72.3 | 81.4 |
| GSM8K (math) | 89.1 | 85.4 | 87.3 |
| MGSM (multilingual math) | 82.4 | 76.1 | 88.9 |
| ARC-C (reasoning) | 91.3 | 88.7 | 89.5 |
Gemma 4 26B leads on general knowledge and reasoning despite having the fewest active parameters. Remarkable efficiency.
Qwen 3.5 Plus dominates multilingual and coding benchmarks. If you work in non-English languages or need strong code generation, Qwen is the pick. See our Qwen 3.5 vs MiMo V2 Pro comparison for more coding benchmarks.
Llama 4 Scout trails on benchmarks but its 10M context window is a category of its own. No other open model comes close.
Ecosystem and tooling
Ollama support
All three families work with Ollama:
ollama run gemma4:26b
ollama run llama4:scout
ollama run qwen3.5:plusAPI availability
| Free local | Free API | Paid API | |
|---|---|---|---|
| Gemma 4 | ✅ | Google AI Studio | Vertex AI |
| Llama 4 | ✅ | — | Together, Fireworks |
| Qwen 3.5 | ✅ | — | DashScope, OpenRouter |
Gemma 4 has the best free API story through Google AI Studio. Llama 4 and Qwen 3.5 require third-party providers for API access.
Fine-tuning
All three support fine-tuning with standard tools (LoRA, QLoRA via Hugging Face, Unsloth). Gemma 4 and Qwen 3.5 have the most community fine-tunes on Hugging Face due to their Apache 2.0 license.
Which should you pick?
Pick Gemma 4 if:
- You need to run AI on edge devices or phones
- You want the best quality-per-compute ratio
- You need multimodal (text + image + audio) in one model
- Apache 2.0 licensing matters
Pick Llama 4 if:
- You need massive context windows (10M tokens)
- You want the most powerful open model available (Maverick 400B)
- You’re building RAG systems over large document collections
Pick Qwen 3.5 if:
- You primarily need coding assistance
- You work in non-English languages (especially CJK)
- You want the widest range of model sizes (0.6B to 110B)
- You need dedicated coding variants (Qwen 2.5 Coder)
The bottom line
There’s no single winner. The open model landscape in 2026 is genuinely competitive — all three families are production-ready and improving fast.
If you’re just getting started with local AI, grab Gemma 4 26B via Ollama. It’s the easiest path to frontier-quality AI on your own hardware. See our Gemma 4 setup guide to get running in 2 minutes.
For a broader view of what’s available, check our best open-source AI models and best free AI models rankings.
Related: AI Coding Tools Pricing · Qwen 3 5 Vs Gemma 4