G4
对比
Gemma 4 vs Qwen 3
Google 的 Gemma 4 与阿里巴巴的 Qwen 3（通义千问3）正面对比——两款强大的开源模型家族，用途有所重叠但各有所长。看看哪款模型在基准测试、部署便捷性和实际任务上胜出。
Benchmarks Multilingual Coding
快速对比
| Feature | Gemma 4 | Qwen 3 |
|---|---|---|
| Developer | Google DeepMind | Alibaba Cloud (Qwen Team) |
| Release | March 2026 | April 2025 (Qwen 2.5) / 2026 (Qwen 3) |
| License | Apache 2.0 | Apache 2.0 (most models) |
| Architecture | Dense + MoE, Hybrid Attention | Dense + MoE (Qwen3-MoE) |
| Multimodal | Text + Image + Audio | Text + Image (Qwen-VL series) |
| Multilingual | 140+ languages | 29 languages (strong CJK) |
| Model Sizes | E2B, E4B, 31B, 26B A4B | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B |
| Thinking Mode | Built-in (thinking tokens) | Built-in (QwQ / thinking variant) |
| Context Window | 128K–256K | 128K (32B) / 1M (72B) |
基准测试对比
同等规模模型（约30B范围）
| Benchmark | Gemma 4 31B | Gemma 4 26B A4B | Qwen 3 32B | Qwen 2.5 72B |
|---|---|---|---|---|
| MMLU Pro | 85.2% | 82.6% | ~83.0% | 85.0% |
| MATH (AIME) | 89.2% | 88.3% | ~85.0% | ~72.0% |
| GPQA Diamond | 84.3% | 82.3% | ~71.0% | ~59.0% |
| LiveCodeBench | 80.0% | 77.1% | ~68.0% | ~55.0% |
| HumanEval | ~92% | ~90% | 92.7% | 88.4% |
| Multilingual MMLU | 88.4% | 86.3% | ~79.0% | 82.3% |
Gemma 4 在科学推理（GPQA）和数学方面领先。Qwen 3 32B 在编程和通用知识上具有竞争力。
小型/边缘模型（8B以下）
| Benchmark | Gemma 4 E4B | Gemma 4 E2B | Qwen 3 7B | Qwen 3 3B |
|---|---|---|---|---|
| MMLU Pro | 69.4% | 60.0% | ~66.0% | ~54.0% |
| MATH | 42.5% | 37.5% | ~58.0% | ~45.0% |
| LiveCodeBench | 52.0% | 44.0% | ~50.0% | ~38.0% |
在小规模模型中，Qwen 3 7B 在数学上稍有优势；Gemma 4 E4B 在科学和多语言任务上领先。
各模型优势所在
Gemma 4 的优势
- Science reasoning: leads on GPQA Diamond across all sizes
- Multimodal: audio support on edge models is unique
- Breadth of languages: 140+ vs Qwen's 29
- Edge deployment: E2B runs on 3 GB VRAM or CPU
- Apache 2.0 purity: zero commercial restrictions anywhere
- Ollama support: first-class, easy to set up
Qwen 3 的优势
- Chinese language: best-in-class for Chinese text tasks
- Math at small sizes: Qwen 3 7B punches above Gemma E4B on math
- Model variety: 0.5B to 72B+, fine-grained size selection
- Long context: 1M token window available at 72B
- Coding: strong HumanEval scores at all sizes
- Wider community fine-tunes: large pool of Qwen-based derivatives
中文能力表现
对于中文应用场景，这是最重要的因素：
| Task | Gemma 4 | Qwen 3 | Winner |
|---|---|---|---|
| Chinese MMLU | Good (140-lang training) | Excellent (native Chinese) | Qwen 3 |
| Chinese creative writing | Adequate | Native quality | Qwen 3 |
| Chinese code comments | Good | Excellent | Qwen 3 |
| Chinese + English mixing | Very good | Excellent | Qwen 3 |
| Chinese + image analysis | Good | Qwen-VL series | Tie |
对于中文用户：如果您的主要用途涉及中文文本，Qwen 3 是更好的选择。Gemma 4 对中文支持良好，但 Qwen 3 将中文作为第一优先级语言来构建。
部署对比
Gemma 4
# Ollama (easiest)
ollama pull gemma4:e4b
ollama run gemma4:e4b
# Python
pip install transformers torch
# Load google/gemma-4-E4B-itQwen 3
# Ollama
ollama pull qwen3:8b
ollama run qwen3:8b
# Python
pip install transformers torch
# Load Qwen/Qwen3-8B-Instruct两款模型都可以轻松通过 Ollama 或 Hugging Face Transformers 部署。主要实际差异在显存：Gemma 4 E4B 在4位量化下需要5GB，而同等级别的 Qwen 3 7B 需要约7GB。
总结
选择 Gemma 4 适用于：
- Maximum science/reasoning quality at any size
- Audio + image multimodal tasks
- Tight VRAM constraints (especially <6 GB)
- Non-Chinese multilingual applications
- Fully unrestricted Apache 2.0 commercial use
选择 Qwen 3 适用于：
- Chinese-language applications
- Finer-grained size selection (0.5B–72B)
- Long-context tasks needing 1M+ tokens
- Math-heavy tasks at <10B scale
- Large established fine-tune community