G4
对比
Gemma 4 vs Llama 4
Google 的 Gemma 4 和 Meta 的 Llama 4 是 2026 年两大旗舰开源 AI 模型家族。两者都具备 MoE 架构、多模态能力和长上下文窗口，但在设计理念、许可证和硬件要求上存在显著差异。
Benchmarks Architecture Deployment
快速对比
| Feature | Gemma 4 | Llama 4 |
|---|---|---|
| Developer | Google DeepMind | Meta AI |
| Release | March 2026 | April 2026 |
| License | Apache 2.0 (fully open) | Llama 4 Community License |
| Architecture | Dense + MoE variants | Primarily MoE (Scout/Maverick) |
| Multimodal | Text + Image + Audio (edge models) | Text + Image (all models) |
| Max Context | 256K tokens (31B/26B) | 10M tokens (Scout) |
| Smallest Model | E2B (2B active params) | Scout 17B-16E (3.6B active) |
| Largest Open Model | 31B dense | Maverick 17B-128E |
| Local Deployment | Excellent — runs on 4 GB VRAM | Harder — 17B+ models require 20+ GB |
基准测试对比
中等规模模型（约30B参数内最佳质量）
| Benchmark | Gemma 4 31B | Gemma 4 26B A4B | Llama 4 Maverick |
|---|---|---|---|
| MMLU Pro | 85.2% | 82.6% | 80.5% |
| MATH (AIME 2026) | 89.2% | 88.3% | ~73.0% |
| GPQA Diamond | 84.3% | 82.3% | 69.8% |
| LiveCodeBench v6 | 80.0% | 77.1% | ~65.0% |
| MMMU Pro (vision) | 76.9% | 73.8% | 73.4% |
| LMSYS ELO | 1452 | 1441 | 1417 |
Gemma 4 在推理、数学和编程方面领先。Llama 4 Maverick 在视觉任务上具有竞争力。
架构深度解析
Gemma 4 架构
- Hybrid attention: interleaved local (sliding window) + global layers
- PLE (Per-Layer Embeddings): edge models encode context efficiently without dense matmul
- p-RoPE: proportional rotary embeddings for long context stability
- MoE variant: 26B A4B — 128 experts, 8 active per token
- Vision encoder: ~150M params (edge) / ~550M params (full)
- Audio encoder: ~300M params (E2B/E4B only)
Llama 4 架构
- iRoPE: interleaved RoPE layers for ultra-long context (up to 10M)
- Pure MoE: Scout (16 experts) and Maverick (128 experts)
- Early fusion: vision tokens merged with text at input stage
- Smaller active params: ~3.6B active / 17B total for Scout
- No audio: text + image only across all variants
- Shared embedding: uniform embeddings across all layers
该选哪个？
选择 Gemma 4，如果...
- You need to run on limited hardware (4–16 GB VRAM)
- You need audio processing (speech recognition, translation)
- Your use case requires math or coding at the highest level
- You need Apache 2.0 license with zero restrictions
- You want the easiest Ollama setup
- You need thinking mode for complex reasoning chains
选择 Llama 4，如果...
- You need extremely long context (100K–10M tokens)
- You need document processing over very long texts
- You have access to Meta's ecosystem and tools
- You prefer the Meta community and fine-tune ecosystem
- You need efficient server-side throughput with MoE Scout
本地部署对比
| Scenario | Gemma 4 | Llama 4 |
|---|---|---|
| 4 GB VRAM | E2B (4-bit) — yes | Not feasible |
| 8 GB VRAM | E4B (4-bit) — great | Scout 4-bit — borderline |
| 16 GB VRAM | E4B BF16 or 31B (4-bit) | Scout 4-bit — comfortable |
| 24 GB VRAM | 31B (4-bit) | Maverick 4-bit — borderline |
| Ollama support | Native — ollama pull gemma4 | Limited — community builds only |
| vLLM support | Full native support | Full native support |
Gemma 4 在消费级硬件上有决定性优势。边缘模型（E2B/E4B）可在笔记本、手机和树莓派上运行。
许可证对比
Gemma 4 — Apache 2.0
- Use commercially with zero restrictions
- No usage caps (any number of monthly active users)
- Modify, redistribute, sell derivatives freely
- No attribution required in products
- Compatible with closed-source products
Llama 4 — 社区许可证
- Free for commercial use under 700M monthly users
- Must credit Meta in products
- Cannot use to train other large language models
- Restrictions on high-MAU commercial use
- Separate license required above threshold
总结
对于大多数开发者来说，Gemma 4 是 2026 年的更佳选择。Apache 2.0 许可证消除了所有法律歧义，边缘模型可在消费级硬件上运行，推理/编程基准分数领先开源领域。音频能力（Gemma 4 E2B/E4B 独有）增加了 Llama 4 无法匹敌的多模态深度。
如果你需要超长上下文窗口（100万以上 token）进行文档处理，或者已深度融入 Meta/Llama 生态系统，则选择 Llama 4 Scout。