Llama 4 Scout
MetaLarge Language ModelOpen SourceMeta's efficient open-source model using mixture-of-experts architecture. 17B active parameters from 109B total, offering strong performance at low compute cost.
Abilities
Use Cases
Available in Tools
Availability
How to Use
Pros
- Open source with permissive license — full freedom for commercial use
- 10M context window — one of the longest available in open-source models
- Efficient MoE architecture — only 17B active per token from 109B total
- Supports vision — processes images alongside text
- Strong multilingual support across many languages
Cons
- MoE architecture requires more total memory than dense models of similar active size
- Less refined than proprietary frontier models on the hardest tasks
- Newer model with smaller community than Llama 3 series
- Requires significant hardware for full precision — quantization recommended
What to Use It For
Perfect For
10M context window with open weights — unmatched for private long-document processing
Massive context plus open weights makes it ideal for retrieval-augmented generation pipelines
Run entirely on your infrastructure — no data leaves your network
Good For
Strong multilingual training enables quality output across many languages
Not Recommended
Proprietary frontier models still lead on the hardest benchmarks
Try instead: Claude Opus 4
109B total parameters need significant memory even with quantization
Try instead: Phi-4
Do Not Use For
Self-hosting requires infrastructure expertise — API providers are much simpler
Try instead: GPT-4o
Even quantized, 109B total params is too large for phones or edge devices
Try instead: Phi-4