Gemma 3 vs Llama 4
2026 - Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 has a 10M context window - 78x larger than Gemma 3's 128K. Better for long documents and large codebases.
Specs comparison
| Gemma 3 | Llama 4 | |
|---|---|---|
| Provider | Google DeepMind | Meta |
| Type | Open source | Open source |
| Context window | 128K | ✓10M |
| Input / 1M tokens | Free (self-host) | Free (self-host) |
| Output / 1M tokens | Free (self-host) | Free (self-host) |
| Release date | 2025-03 | 2025-04 |
Benchmarks
| Benchmark | Gemma 3 | Llama 4 |
|---|---|---|
| MMLU | ~76% | ~85% |
Scores sourced from official provider release posts.
Strengths
Gemma 3
- ✓Runs on consumer hardware (4B and 12B variants)
- ✓Multimodal input support
- ✓Strong benchmark performance relative to size
- ✓Tight Keras and JAX integration
- ✓Good instruction following out of the box
Llama 4
- ✓Fully open weights - no usage restrictions
- ✓10M context in Llama 4 Scout variant
- ✓Native multimodal support
- ✓Strong performance relative to size
- ✓Enormous ecosystem of community tools and fine-tunes
Which should you choose?
Choose Gemma 3 if you need...
- →On-device and edge inference
- →Low-resource environments
- →Prototyping with free Google AI Studio access
- →Researchers benchmarking small models
Choose Llama 4 if you need...
- →Self-hosted and on-premise deployments
- →Privacy-sensitive workloads
- →Custom fine-tuning
- →Researchers and open-source builders