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Llama 4 Maverick

Meta

89·Strong

Overall Trust Score

Meta's flagship open-source model with 400B parameters, native multimodal capabilities, and state-of-the-art performance. Best-in-class open-source option for on-premises deployment.

open-source
self-hosted
data-sovereignty
on-premises
customizable
fine-tunable
cost-effective-at-scale
Version: 400B
Last Evaluated: November 7, 2025
Official Website →

Trust Vector

Performance & Reliability

91

Excellent performance for an open-source model, approaching frontier proprietary models. Performance and latency depend heavily on deployment infrastructure.

task accuracy code
89
Methodology
Standard coding benchmarks
Evidence
HumanEval
86.7% on HumanEval
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
task accuracy reasoning
90
Methodology
PhD-level reasoning benchmarks
Evidence
GPQA Diamond
65.2% on PhD-level questions
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
task accuracy general
92
Methodology
Comprehensive knowledge testing
Evidence
MMLU-Pro
76.8% on graduate knowledge
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
output consistency
89
Methodology
Community evaluation
Evidence
Community Testing
Good consistency reported by community
Date: 2025-08-01
Confidence: mediumLast verified: 2025-11-07
latency p50
Value: Depends on hardware
Methodology
Community deployment reports
Evidence
Self-hosted deployments
Latency varies by infrastructure (typically 2-5s)
Date: 2025-08-01
Confidence: lowLast verified: 2025-11-07
Note: Self-hosted so latency depends entirely on hardware and optimization
latency p95
Value: Depends on hardware
Methodology
Community reports
Evidence
Community reports
Varies significantly
Date: 2025-08-01
Confidence: lowLast verified: 2025-11-07
context window
Value: 128,000 tokens
Methodology
Official specification
Evidence
Llama 4 Documentation
128K token context window
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
uptime
Value: Self-hosted
Methodology
Deployment model analysis
Evidence
Model Architecture
Uptime controlled by deployment infrastructure
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
Note: Uptime is under customer control

Security

80

Security is customer-controlled with self-hosting. Excellent for data sovereignty but requires in-house security expertise.

prompt injection resistance
78
Methodology
Community security testing
Evidence
Community Security Testing
Moderate resistance, requires additional guardrails
Date: 2025-08-15
Confidence: mediumLast verified: 2025-11-07
Note: Open-source nature enables security hardening but requires customer implementation
jailbreak resistance
75
Methodology
Safety evaluation
Evidence
Meta Safety Card
Basic safety training, additional tuning recommended
Date: 2025-07-15
Confidence: mediumLast verified: 2025-11-07
data leakage prevention
95
Methodology
Deployment model analysis
Evidence
Self-hosted deployment
Complete data control with on-premises deployment
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
Note: Self-hosting eliminates external data leakage risk
output safety
82
Methodology
Safety testing
Evidence
Meta Safety Evaluation
Safety training included, additional guardrails recommended
Date: 2025-07-15
Confidence: mediumLast verified: 2025-11-07
api security
Value: Customer controlled
Methodology
Architecture analysis
Evidence
Deployment model
Security entirely under customer control
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07

Privacy & Compliance

95

Exceptional privacy - best-in-class. Self-hosting provides complete data control, enabling any compliance framework.

data residency
Value: Customer controlled
Methodology
Deployment model analysis
Evidence
Self-hosted deployment
Complete control over data location
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
training data optout
100
Methodology
Deployment architecture
Evidence
Self-hosted model
No data sent to Meta for training
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
data retention
Value: Customer controlled
Methodology
Architecture analysis
Evidence
Self-hosted deployment
Complete control over data retention
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
pii handling
95
Methodology
Deployment model analysis
Evidence
On-premises deployment
Customer implements PII handling
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
compliance certifications
90
Methodology
Deployment model analysis
Evidence
Customer infrastructure
Compliance depends on customer infrastructure (enables HIPAA, etc.)
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
zero data retention
100
Methodology
Architecture analysis
Evidence
Self-hosted model
No external data transmission
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07

Trust & Transparency

94

Excellent transparency as open-source model. Full access to weights and detailed documentation.

explainability
88
Methodology
Capability evaluation
Evidence
Model capabilities
Good reasoning explanations
Date: 2025-07-15
Confidence: mediumLast verified: 2025-11-07
hallucination rate
84
Methodology
Community evaluation
Evidence
Community testing
Moderate hallucination rate, similar to other models
Date: 2025-08-01
Confidence: mediumLast verified: 2025-11-07
bias fairness
82
Methodology
Bias benchmark evaluation
Evidence
Meta Responsible AI
Bias testing and mitigation included
Date: 2025-07-15
Confidence: mediumLast verified: 2025-11-07
uncertainty quantification
85
Methodology
Qualitative assessment
Evidence
Model behavior
Reasonable uncertainty expression
Date: 2025-07-15
Confidence: mediumLast verified: 2025-11-07
model card quality
95
Methodology
Documentation review
Evidence
Llama 4 Model Card
Comprehensive open-source model card
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
training data transparency
92
Methodology
Research paper review
Evidence
Llama 4 Paper
Detailed training data description in paper
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
guardrails
78
Methodology
Safety mechanism review
Evidence
Meta Safety Tools
Basic guardrails, customer can add more
Date: 2025-07-15
Confidence: mediumLast verified: 2025-11-07

Operational Excellence

85

Strong operational maturity with massive open-source ecosystem. Requires in-house ML ops expertise.

api design quality
Value: Customer implemented
Methodology
Tooling review
Evidence
Deployment tools
Standard inference libraries available
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
sdk quality
88
Methodology
SDK ecosystem review
Evidence
Hugging Face Transformers
Excellent community SDK support
Date: 2025-08-01
Confidence: highLast verified: 2025-11-07
versioning policy
90
Methodology
Release policy review
Evidence
Meta Release Process
Clear versioning with model checkpoints
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07
monitoring observability
80
Methodology
Tooling analysis
Evidence
Customer implementation
Customer must implement monitoring
Date: 2025-07-15
Confidence: mediumLast verified: 2025-11-07
support quality
82
Methodology
Support channel assessment
Evidence
Community Support
Active community, Meta eng engagement
Date: 2025-08-01
Confidence: highLast verified: 2025-11-07
ecosystem maturity
92
Methodology
Ecosystem analysis
Evidence
Open Source Ecosystem
Massive ecosystem (Hugging Face, vLLM, etc.)
Date: 2025-08-01
Confidence: highLast verified: 2025-11-07
license terms
95
Methodology
License review
Evidence
Llama 4 License
Permissive license for commercial use
Date: 2025-07-15
Confidence: highLast verified: 2025-11-07

✨ Strengths

  • 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

⚠️ Limitations

  • Requires significant ML ops expertise and infrastructure
  • Performance and latency depend on hardware investment
  • Slightly behind frontier proprietary models 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)

📊 Metadata

pricing:
input: $0 (self-hosted)
output: $0 (self-hosted)
notes: Free model, infrastructure costs only (8x H100 GPUs ~$200K+). No API fees.
context window: 128000
languages:
0: English
1: Spanish
2: French
3: German
4: Italian
5: Portuguese
6: Japanese
7: Korean
8: Chinese
9: Arabic
10: Hindi
11: 100+ languages
modalities:
0: text
1: vision
2: audio
api endpoint: Self-hosted
open source: true
architecture: Transformer-based, 400B parameters, mixture-of-experts
parameters: 400B total, ~60B active

Use Case Ratings

code generation

88

Strong coding for open-source model. Excellent for on-premises code assistance with data sovereignty.

customer support

85

Good for self-hosted customer support requiring data privacy.

content creation

89

Excellent for content creation with strong multilingual capabilities.

data analysis

87

Good for data analysis with complete data control for sensitive datasets.

research assistant

90

Excellent for research requiring data sovereignty. Transparent open-source nature aids reproducibility.

legal compliance

92

Excellent for legal work requiring on-premises deployment. Complete data control enables any compliance framework.

healthcare

93

Outstanding for healthcare with on-premises HIPAA compliance. Best data sovereignty of any option.

financial analysis

90

Strong for financial services requiring data residency and air-gapped deployment.

education

91

Excellent for education with strong multilingual capabilities and customizability.

creative writing

88

Good for creative writing with ability to fine-tune for specific styles.