Gemma 4 (by Google DeepMind) and Qwen 3.5 (by Alibaba Cloud) are two of the most capable open-source model families available in 2026. Both offer multimodal understanding, long context windows, and competitive benchmark scores — but they differ in architecture, licensing, and ecosystem support.
This comparison covers benchmarks, architecture, features, and practical deployment considerations to help you choose the right model for your use case.
| Feature | Gemma 4 31B | Qwen 3.5 32B |
|---|---|---|
| Developer | Google DeepMind | Alibaba Cloud |
| Parameters | 31B (Dense), 26B (MoE) | 32B (Dense) |
| License | Apache 2.0 | Apache 2.0 |
| Context Window | 256K tokens | 128K tokens |
| Modalities | Text, Image, Video, Audio | Text, Image, Video |
| Languages | 140+ | 100+ |
| Model Variants | 4 (E2B, E4B, 26B MoE, 31B) | 3+ variants |
| MoE Variant | Yes (26B A4B, 128 experts) | Separate MoE models |
| Function Calling | Native | Native |
Head-to-head benchmark scores (31B/32B class models):
| Benchmark | Gemma 4 31B | Qwen 3.5 32B |
|---|---|---|
| AIME 2026 | 89.2% | ~86% |
| LiveCodeBench v6 | 80.0% | ~78% |
| GPQA Diamond | 84.3% | ~82% |
| MMMLU | 85.2% | ~84% |
| HumanEval | ~88% | ~90% |
Scores are based on official reports and community reproductions. Testing conditions may vary. Both models deliver competitive performance across all categories.
140+ language support and 256K context give Gemma 4 an edge for global applications.
Both models excel in Chinese. Qwen has slight advantages in some Chinese-specific tasks, while Gemma 4 offers broader multilingual coverage.
The 26B A4B MoE variant activates only 4B parameters per inference, delivering near-31B quality at a fraction of the compute cost.
The E2B (2B) and E4B (4B) variants are purpose-built for edge devices with minimal resource requirements.
89.2% on AIME 2026 and 84.3% on GPQA Diamond demonstrate superior mathematical and scientific capabilities.
Both models score in the 78-90% range across coding benchmarks. Choose based on your preferred ecosystem.
Neither model is universally better. Gemma 4 leads in math reasoning, multimodal breadth (audio support), context length (256K), and language coverage (140+). Qwen 3.5 is competitive in code generation and Chinese-specific tasks. Both use Apache 2.0 licensing.
Both models perform excellently in Chinese. Qwen 3.5 has a slight advantage in some Chinese-specific benchmarks due to Alibaba's training focus, but Gemma 4's MMMLU score of 85.2% demonstrates strong Chinese capabilities as well.
Gemma 4's 26B A4B MoE variant is uniquely efficient — activating only 4B of its 26B parameters per inference. This gives it near-flagship quality at E4B-level compute. Qwen 3.5 doesn't have an equivalent MoE offering in the same family.
Yes. Both models are available through Ollama, Hugging Face, and standard inference frameworks. If you're using an OpenAI-compatible API (via vLLM or Ollama), switching is as simple as changing the model name.
Both have active communities. Gemma 4 benefits from Google's ecosystem (AI Studio, Vertex AI, Kaggle). Qwen 3.5 has strong support in China via ModelScope and Alibaba Cloud. Both are widely available on Hugging Face.
Yes. Both Gemma 4 and Qwen 3.5 use the Apache 2.0 license, allowing free commercial and non-commercial use, modification, and distribution.
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