Gemma 3 vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 is meaningfully stronger at long context (95 vs 75).
- •Llama 4 has a Up to 10M tokens (Scout); ~1M tokens (Maverick) context window vs 128K tokens (32K for the 1B variant) - better for whole-repo or long-document work.
Specs comparison
| Gemma 3 | Llama 4 | |
|---|---|---|
| Provider | Google DeepMind | Meta |
| Type | Open source | Open source |
| Context window | 128K tokens (32K for the 1B variant) | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) |
| 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 |
|---|---|---|
| MATH (27B) | 89% | - |
| MMMU (27B, multimodal) | 64.9% | - |
| Scout context window | - | 10M tokens |
| Scout size | - | 17B active / 109B total (16 experts) |
| Maverick size | - | 17B active / 400B total (128 experts) |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose Gemma 3 if...
- →You need an open, self-hostable model with a size to match your hardware
- →Multilingual or multimodal tasks on-prem
- →Privacy-sensitive or offline deployments
- →Fine-tuning on your own data
Choose Llama 4 if...
- →You need extremely long context in an open model (Scout's 10M window)
- →Self-hosted or on-prem multimodal deployment
- →You want an efficient MoE that activates few parameters per token
- →Fine-tuning or full control over the model