LLaMa 4 Scout is an open-weight multimodal model optimized for speed and efficiency with native text and image understanding, 10M token context, and multilingual support across 12 languages.
Context tokens
131,072
Output tokens
8,192
Training cutoff
Aug 1, 2024
Released
Apr 5, 2025
Supported languages
Supported input media
Seclai Content Tools
Inspect source documents connected to your account. Includes tools for loading full content, reading character ranges, searching within documents, viewing stats, and listing available content sources. When a source_connection_content_version_id is provided in agent run metadata it is used as the default. Otherwise the model can discover content via list_content_sources.
Seclai Knowledge Base
Search your knowledge bases using semantic similarity. Includes search_knowledge_base and list_knowledge_bases. When a knowledge_base_id is provided in the prompt or agent run metadata it is used as the default. Otherwise the model can discover available knowledge bases at runtime.
| Type | Credits | Units |
|---|---|---|
| Input | 2.26 | Credits per 1k tokens |
| Output | 8.78 | Credits per 1k tokens |
No variants available for this model.
