Llama 4 Maverick
v400BMeta
Meta's flagship open-weight model (released April 5, 2025): a mixture-of-experts with 400B total / 17B active parameters (128 experts), natively multimodal (text + image), with a 1M-token context window. Meta's last open-weight release: the company has since pivoted to closed models with Muse Spark (April 2026), and newer open models from DeepSeek, Qwen, and Moonshot have surpassed it on most benchmarks.
Trust Vector Analysis
Dimension Breakdown
🚀Performance & Reliability+
Excellent performance for an open-source model, approaching frontier proprietary models. Performance and latency depend heavily on deployment infrastructure.
Standard coding benchmarks
PhD-level reasoning benchmarks
Comprehensive knowledge testing
Community evaluation
Community deployment reports
Official specification
Deployment model analysis
🛡️Security+
Security is customer-controlled with self-hosting. Excellent for data sovereignty but requires in-house security expertise.
Community security testing
Safety evaluation
Deployment model analysis
Safety testing
Architecture analysis
🔒Privacy & Compliance+
Exceptional privacy - best-in-class. Self-hosting provides complete data control, enabling any compliance framework.
Deployment model analysis
Deployment architecture
Architecture analysis
Deployment model analysis
Deployment model analysis
Architecture analysis
👁️Trust & Transparency+
Excellent transparency as open-source model. Full access to weights and detailed documentation.
Capability evaluation
Community evaluation
Bias benchmark evaluation
Qualitative assessment
Documentation review
Research paper review
Safety mechanism review
⚙️Operational Excellence+
Strong operational maturity with massive open-source ecosystem. Requires in-house ML ops expertise.
Tooling review
SDK ecosystem review
Release policy review
Tooling analysis
Support channel assessment
Ecosystem analysis
License review
- +Best privacy and data sovereignty - complete on-premises control
- +Open-source with permissive commercial license
- +No recurring API costs - one-time infrastructure investment
- +Customizable and fine-tunable for specific domains
- +Excellent transparency with full model access
- +No vendor lock-in or rate limits
- +Best for highly regulated industries and government
- !Requires significant ML ops expertise and infrastructure
- !Performance and latency depend on hardware investment
- !Behind frontier proprietary models and newer open models (DeepSeek V4, Qwen3.5, Kimi K2.6) on benchmarks
- !No managed service or enterprise support from Meta
- !Requires customer implementation of safety guardrails
- !High upfront hardware costs (8x A100/H100 GPUs minimum)
- !Legacy status: Meta's last open-weight release; Meta pivoted to closed models (Muse Spark, April 2026), so no successor open weights are expected
Use Case Ratings
code generation
Strong coding for open-source model. Excellent for on-premises code assistance with data sovereignty.
customer support
Good for self-hosted customer support requiring data privacy.
content creation
Excellent for content creation with strong multilingual capabilities.
data analysis
Good for data analysis with complete data control for sensitive datasets.
research assistant
Excellent for research requiring data sovereignty. Transparent open-source nature aids reproducibility.
legal compliance
Excellent for legal work requiring on-premises deployment. Complete data control enables any compliance framework.
healthcare
Outstanding for healthcare with on-premises HIPAA compliance. Best data sovereignty of any option.
financial analysis
Strong for financial services requiring data residency and air-gapped deployment.
education
Excellent for education with strong multilingual capabilities and customizability.
creative writing
Good for creative writing with ability to fine-tune for specific styles.