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Llama 3.3 70B

Meta

85·Strong

Overall Trust Score

Meta's powerful 70B parameter Llama 3.3 model offering strong performance with open-source flexibility. Excellent balance of capability and resource efficiency for self-hosted deployments.

open-source
mathematics
self-hosted
privacy
balanced
70b-parameters
Version: 2024-12
Last Evaluated: November 8, 2025
Official Website →

Trust Vector

Performance & Reliability

78

Strong mathematical reasoning (77% MATH). Good balance for self-hosted deployments.

task accuracy code
74
Methodology
Coding benchmarks
Evidence
HumanEval Benchmark
45% pass rate (estimated)
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
task accuracy reasoning
82
Methodology
Mathematical benchmarks
Evidence
MATH Benchmark
77% on mathematical reasoning
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
task accuracy general
76
Methodology
Knowledge testing
Evidence
MMLU Benchmark
50.5% on multitask understanding
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
output consistency
77
Methodology
Internal testing
Evidence
Meta Internal Testing
Good consistency
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
latency p50
Value: 1.4s
Methodology
Median latency
Evidence
Community benchmarking
~1.4s on standard hardware
Date: 2024-12-20
Confidence: mediumLast verified: 2025-11-08
latency p95
Value: 2.8s
Methodology
95th percentile
Evidence
Community benchmarking
p95 ~2.8s
Date: 2024-12-20
Confidence: mediumLast verified: 2025-11-08
context window
Value: 128,000 tokens
Methodology
Official specification
Evidence
Meta Documentation
128K context
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
uptime
95
Methodology
Deployment-dependent
Evidence
Self-hosted model
User-controlled uptime
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08

Security

80

Good baseline security with self-hosted control.

prompt injection resistance
78
Methodology
Adversarial testing
Evidence
Meta Safety Testing
Good baseline resistance
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
jailbreak resistance
79
Methodology
Safety testing
Evidence
Meta Safety
Built-in safety
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
data leakage prevention
85
Methodology
Deployment analysis
Evidence
Self-hosted
Full data control
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
output safety
80
Methodology
Safety benchmarks
Evidence
Meta Safety
Safety training applied
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
api security
82
Methodology
Deployment review
Evidence
Deployment
User-controlled security
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08

Privacy & Compliance

95

Exceptional privacy with self-hosted deployment.

data residency
Value: User-controlled
Methodology
Deployment analysis
Evidence
Open-source
Full location control
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
training data optout
98
Methodology
Data flow analysis
Evidence
Self-hosted
No data sent to Meta
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
data retention
Value: User-controlled
Methodology
Deployment analysis
Evidence
Self-hosted
Full retention control
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
pii handling
92
Methodology
Architecture review
Evidence
Self-hosted
Full PII control
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
compliance certifications
94
Methodology
Deployment options
Evidence
Self-hosted
Compliance via deployment
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
zero data retention
98
Methodology
Deployment analysis
Evidence
Self-hosted
Complete control
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08

Trust & Transparency

87

Strong transparency as open-source model.

explainability
84
Methodology
Reasoning evaluation
Evidence
Model Behavior
Good explanations
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
hallucination rate
82
Methodology
Community evaluation
Evidence
Community Testing
Moderate hallucination
Date: 2024-12-20
Confidence: mediumLast verified: 2025-11-08
bias fairness
82
Methodology
Bias benchmarks
Evidence
Meta Responsible AI
Bias testing applied
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
uncertainty quantification
83
Methodology
Qualitative assessment
Evidence
Model Behavior
Good uncertainty
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
model card quality
91
Methodology
Documentation review
Evidence
Meta Model Card
Comprehensive card
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
training data transparency
87
Methodology
Technical documentation
Evidence
Meta Technical Report
Good transparency
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
guardrails
89
Methodology
Safety system review
Evidence
Open-source
Customizable safety
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08

Operational Excellence

85

Good operational maturity with mature Llama ecosystem.

api design quality
85
Methodology
API review
Evidence
Meta Documentation
Standard inference API
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
sdk quality
87
Methodology
SDK review
Evidence
Meta GitHub
Official libraries
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
versioning policy
88
Methodology
Versioning review
Evidence
Meta Releases
Clear versioning
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
monitoring observability
79
Methodology
Tool review
Evidence
Community tools
Deployment-dependent
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
support quality
83
Methodology
Support assessment
Evidence
Community Support
Active community
Date: 2024-12-15
Confidence: mediumLast verified: 2025-11-08
ecosystem maturity
88
Methodology
Ecosystem analysis
Evidence
Ecosystem
Mature ecosystem
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08
license terms
90
Methodology
License review
Evidence
Llama License
Permissive license
Date: 2024-12-15
Confidence: highLast verified: 2025-11-08

✨ Strengths

  • •Strong mathematical reasoning (77% MATH)
  • •Open-source with permissive licensing
  • •Complete data sovereignty via self-hosting
  • •Large 128K context window
  • •Mature Llama ecosystem and tooling
  • •Good balance of capability and efficiency

⚠️ Limitations

  • •Moderate general knowledge (50.5% MMLU)
  • •Limited coding capabilities compared to larger models
  • •Requires infrastructure for deployment
  • •No managed API from Meta
  • •Deployment expertise needed
  • •Uptime depends on hosting

📊 Metadata

pricing:
input: Self-hosted (infrastructure costs)
output: Self-hosted (infrastructure costs)
notes: Open-source. Typically $0.30-1.00 per 1M tokens with optimized deployment.
context window: 128000
languages:
0: English
1: Spanish
2: French
3: German
4: Italian
5: Portuguese
6: Japanese
7: Korean
8: Chinese
9: 100+ languages
modalities:
0: text
api endpoint: Self-hosted
open source: true
architecture: Transformer-based
parameters: 70B

Use Case Ratings

code generation

76

Moderate coding capabilities. Better options for complex development.

customer support

80

Good for customer support with privacy benefits.

content creation

79

Good content creation with large context window.

data analysis

84

Strong mathematical reasoning (77% MATH) for analysis.

research assistant

78

Good for research with solid knowledge base.

legal compliance

82

Good for legal with data sovereignty via self-hosting.

healthcare

86

Good for healthcare with self-hosted HIPAA compliance.

financial analysis

83

Strong math capabilities for financial modeling.

education

82

Good for education with strong mathematical reasoning.

creative writing

78

Adequate creative writing capabilities.

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