Llama 4 Scout
Meta
Overall Trust Score
Meta's efficient Llama 4 model optimized for speed and resource efficiency. Designed for edge deployment and cost-sensitive applications requiring open-source flexibility.
Trust Vector
Performance & Reliability
Efficient performance optimized for speed and resource usage. Good balance for edge deployment and cost-sensitive applications.
task accuracy code72
task accuracy reasoning74
task accuracy general77
output consistency75
latency p50Value: 0.6s
latency p95Value: 1.2s
context windowValue: 64,000 tokens
uptime95
Security
Good baseline security with self-hosted deployment providing full control. Smaller model may have slightly lower resistance than Behemoth.
prompt injection resistance78
jailbreak resistance79
data leakage prevention85
output safety80
api security82
Privacy & Compliance
Exceptional privacy with self-hosted deployment. Full control over all data aspects.
data residencyValue: User-controlled
training data optout98
data retentionValue: User-controlled
pii handling92
compliance certifications94
zero data retention98
Trust & Transparency
Strong transparency as open-source model. Good documentation and customizable guardrails.
explainability82
hallucination rate80
bias fairness81
uncertainty quantification83
model card quality90
training data transparency87
guardrails88
Operational Excellence
Good operational maturity with strong ecosystem. Easier to deploy than Behemoth due to smaller size.
api design quality85
sdk quality86
versioning policy88
monitoring observability80
support quality84
ecosystem maturity89
license terms90
✨ Strengths
- •Fast inference (~0.6s p50) suitable for real-time applications
- •Lower resource requirements enable edge deployment
- •Complete data sovereignty with self-hosted deployment
- •Open-source with full transparency
- •No data retention or sharing concerns
- •Cost-effective for high-volume workloads
⚠️ Limitations
- •Moderate accuracy (57.2% MMLU) compared to larger models
- •Limited coding capabilities (42% HumanEval estimated)
- •Smaller context window (64K tokens)
- •Requires infrastructure for deployment
- •Less capable for complex reasoning tasks
- •No managed API service from Meta
📊 Metadata
Use Case Ratings
code generation
Adequate for basic coding tasks. Fast inference makes it suitable for development tools.
customer support
Well-suited for customer support with fast response times and privacy benefits.
content creation
Good for content creation with balanced quality and speed.
data analysis
Adequate for basic data analysis. Not suitable for complex mathematical tasks.
research assistant
Good for basic research tasks. 57.2% MMLU shows solid general knowledge.
legal compliance
Good for basic legal tasks with data sovereignty benefits.
healthcare
Good for healthcare with self-hosted HIPAA compliance. Basic clinical tasks.
financial analysis
Adequate for basic financial tasks. Not suitable for complex modeling.
education
Good for educational content. Fast inference suitable for interactive learning.
creative writing
Adequate creative writing for typical use cases.