AI Model Profile
Llama 4 Scout
Llama 4 Scout is a Meta Llama 4 mixture-of-experts model optimized for multimodal understanding, multilingual tasks, coding, tool-calling, and agentic systems.
Verification & Sources
- Status
- Verified
- Source links
- 1
- Freshness
- Verified June 18, 2026
- Last verified
- June 18, 2026
- Last updated
- June 17, 2026
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Benchmark Caveat
Provider documentation and provider-published capability notes are directional, not universal rankings. Real results depend on prompts, tools, latency targets, pricing tier, safety filters, context length, and workload mix.
Best for
Candidate for open-weight and local/private evaluations where Llama 4 Scout hardware requirements fit.
Skip if
Skip if your team needs independently benchmarked performance claims or contractual guarantees not present in the official provider documentation.
Strengths
Official docs list multimodal input, coding, tool-calling, agentic use, and very large context.
Weaknesses
Hardware, quantization, and deployment choices strongly affect practicality and cost.
Agent suitability
Candidate for agentic systems based on official Llama 4 positioning.
Kingy AI take
Use this profile as a source-backed reference point, not a ranking. Re-check official provider docs before making production or budget decisions.
Full Model Notes
Llama 4 Scout is a Meta Llama 4 mixture-of-experts model optimized for multimodal understanding, multilingual tasks, coding, tool-calling, and agentic systems.
Coding notes
Official docs list coding as an optimized task.
Reasoning notes
Evaluate reasoning quality locally before making claims.
Creative notes
Supports text plus image input according to official Llama docs.
Research notes
Candidate for long-context experiments where hardware and inference stack are controlled.
License notes
Open-weight model under Meta Llama terms; review official Meta Llama documentation and license before use or redistribution.
Hardware requirements
Official Llama docs note single-GPU inference for an INT4-quantized Scout variant on 1xH100 GPU.
