Comparison
Gemma 4 vs Llama 4
Google's Gemma 4 and Meta's Llama 4 are the two flagship open-source AI model families of 2026. Both feature MoE architectures, multimodal capabilities, and long context windows — but differ significantly in design philosophy, licensing, and hardware requirements.
Quick Summary
| 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 |
Benchmark Comparison
Mid-Range Models (best quality within ~30B params)
| 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 leads on reasoning, math, and coding. Llama 4 Maverick is competitive on vision tasks.
Architecture Deep Dive
Gemma 4 Architecture
- 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 Architecture
- 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
Which One Should You Use?
Choose Gemma 4 If...
- 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
Choose Llama 4 If...
- 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
Local Deployment Comparison
| 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 wins decisively on consumer hardware. The edge models (E2B/E4B) run on laptops, phones, and Raspberry Pi.
License Comparison
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 — Community License
- 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
Verdict
For most developers, Gemma 4 is the better choice in 2026. The Apache 2.0 license removes all legal ambiguity, the edge models run on cheap consumer hardware, and the reasoning/coding benchmark scores lead the open-source field. The audio capability (unique to Gemma 4 E2B/E4B) adds multimodal depth that Llama 4 cannot match.
Choose Llama 4 Scout if you need ultra-long context windows (1M+ tokens) for document processing, or if you are already deeply integrated in the Meta/Llama ecosystem.