Comparison

Gemma 4 vs Qwen-3.5

Two open-weights families with very different priorities. Gemma 4 is built on Gemini's research lineage; Qwen-3.5 is Alibaba's open answer with strong multilingual and long-context credentials. Here's how they compare.

Feature	Gemma 4	Qwen-3.5 (Alibaba Cloud)
Model type	Open weights from Google DeepMind	Open weights from Alibaba Cloud
License	Gemma license — research & commercial use	Tongyi Qianwen license — commercial use with usage thresholds
Self-hosting	✓ Anywhere — laptops to TPU pods	✓ Anywhere — broad GPU support
Fine-tuning	✓ SFT, LoRA, QLoRA, DPO, RLHF	✓ SFT, LoRA, QLoRA, DPO
Model sizes	2B, 9B, 27B, 70B (+ 26B A4B MoE)	0.5B, 1.8B, 7B, 14B, 32B, 72B, 110B
Context window	128K tokens	128K – 1M tokens (long-context variants)
Multimodal	Native text + images across the family	Vision via Qwen-VL siblings; audio via Qwen-Audio
Reasoning mode	✓ Thinking variants for step-by-step	✓ Qwen-Reasoner thinking mode
Languages	140+ with balanced multilingual training	100+ with strong Chinese, English, and CJK coverage
Tooling	JAX, PyTorch, Keras, llama.cpp, Ollama, vLLM	PyTorch, llama.cpp, vLLM, Ollama, ModelScope
Safety work	Google's safety stack, red-teaming, public model cards	Alibaba safety policies and responsible-use guidance
Best for	Western enterprises, regulated workloads, on-device AI	Chinese-language workloads, ultra-long-context tasks

Choose Gemma 4 if you…

Want a model rooted in the Gemini research stack
Need strong English + 140-language balance
Care about multimodal-by-default across every size
Prefer Google DeepMind safety tooling and model cards

Choose Qwen-3.5 if you…

Need the absolute strongest Chinese-language quality
Want a >128K (up to 1M) context window today
Are already deployed on the ModelScope ecosystem
Need very small (sub-2B) sizes for tight devices

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