Llama 4 Scout
v2025-04Meta
Meta's efficient Llama 4 model (released April 5, 2025): a natively multimodal mixture-of-experts with 109B total / 17B active parameters (16 experts) and an industry-leading 10M-token context window, deployable on a single H100-class GPU. Optimized for speed and cost-sensitive applications requiring open-weight flexibility. Now a legacy line: Meta has shipped no new open weights since Scout/Maverick and pivoted to closed models with Muse Spark (April 2026).
Trust Vector Analysis
Dimension Breakdown
🚀Performance & Reliability+
Efficient performance optimized for speed and resource usage. Good balance for edge deployment and cost-sensitive applications.
Industry-standard coding benchmarks
Mathematical reasoning benchmarks
Knowledge testing benchmarks
Internal testing with repeated prompts
Median latency on recommended hardware
95th percentile response time
Official specification
User-controlled deployment
🛡️Security+
Good baseline security with self-hosted deployment providing full control. Smaller model may have slightly lower resistance than Behemoth.
Testing against prompt injection attacks
Testing against adversarial prompts
Analysis of deployment model
Safety testing
Review of deployment practices
🔒Privacy & Compliance+
Exceptional privacy with self-hosted deployment. Full control over all data aspects.
Analysis of deployment model
Analysis of data flow
Analysis of deployment model
Review of deployment architecture
Review of deployment options
Analysis of deployment model
👁️Trust & Transparency+
Strong transparency as open-source model. Good documentation and customizable guardrails.
Evaluation of reasoning transparency
Community evaluation
Evaluation on bias benchmarks
Qualitative assessment
Review of documentation
Review of technical documentation
Review of safety systems
⚙️Operational Excellence+
Good operational maturity with strong ecosystem. Easier to deploy than Behemoth due to smaller size.
Review of API design
Review of SDKs
Review of versioning
Review of monitoring tools
Assessment of support
Analysis of ecosystem
Review of license
- +Fast inference (~0.6s p50) suitable for real-time applications
- +Fits on a single H100-class GPU (17B active parameters)
- +Industry-leading 10M-token native context window
- +Natively multimodal (text + image input) via early fusion
- +Complete data sovereignty with self-hosted deployment — no data retention or sharing concerns
- +Open weights with full transparency
- +Cost-effective for high-volume workloads
- !Moderate accuracy compared to larger models (Maverick, frontier proprietary)
- !Limited coding capabilities relative to coding-specialized models
- !Native 10M context rarely exposed by hosted providers (typically capped at 128K-1M)
- !Requires infrastructure for deployment
- !Less capable for complex reasoning tasks
- !No managed API service from Meta
- !Legacy status: Meta has shipped no new open weights since Llama 4 Scout/Maverick (April 2025) and pivoted to closed models (Muse Spark, April 2026)
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.