Llama 3.1 405B

vllama-3.1-405b

Meta

Modelmetaopen-source
87
Strong
About This Model

Meta's largest open-source model with 405 billion parameters, offering complete transparency, self-hosting capabilities, and competitive performance with proprietary models. Remains one of Meta's legacy open models: Meta has shipped no new open weights since Llama 4 Scout/Maverick (April 2025) and pivoted to closed models in 2026.

Last Evaluated: June 10, 2026
Official Website

Trust Vector Analysis

Dimension Breakdown

🚀Performance & Reliability
+
task accuracy code

Industry-standard coding benchmarks

Evidence
HumanEval — 80.5% pass rate
highVerified: 2025-11-07
task accuracy reasoning

Mathematical and scientific reasoning benchmarks

Evidence
MATH — 64.4% accuracy
GPQA — 51.2% on graduate-level science
highVerified: 2025-11-07
task accuracy general

Comprehensive knowledge testing

Evidence
MMLU — 85.2% on graduate-level knowledge
highVerified: 2025-11-07
output consistency

Community evaluation and testing

Evidence
Community Testing — Good consistency reported in community testing
mediumVerified: 2025-11-07
latency p50

Third-party hosting performance

Evidence
Together AI — ~3.5s via hosted API (hardware dependent for self-hosting)
mediumVerified: 2025-11-07
latency p95

Third-party hosting performance

Evidence
Together AI — ~7.0s via hosted API
mediumVerified: 2025-11-07
uptime sla

Deployment model analysis

Evidence
Self-hosted — User-controlled uptime for self-hosted deployments
highVerified: 2025-11-07
context window

Official model specifications

Evidence
Meta Documentation — 128K token context window
highVerified: 2025-11-07
multimodal support

Official model capabilities

Evidence
Meta Documentation — Text-only model
highVerified: 2025-11-07
🛡️Security
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jailbreak resistance

Safety testing and red teaming

Evidence
Meta Safety Report — Safety fine-tuning applied, but open model allows modification
mediumVerified: 2025-11-07
prompt injection defense

Community security testing

Evidence
Community Testing — Standard defenses, user-configurable
mediumVerified: 2025-11-07
data leakage prevention

Architecture review

Evidence
Self-hosted Model — Complete data isolation when self-hosted
highVerified: 2025-11-07
adversarial robustness

Adversarial testing by Meta

Evidence
Meta Safety Testing — Robust to common adversarial attacks in testing
mediumVerified: 2025-11-07
content filtering

Safety tooling review

Evidence
Llama Guard — Llama Guard available for content moderation
mediumVerified: 2025-11-07
🔒Privacy & Compliance
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data retention

Deployment model analysis

Evidence
Self-hosted Model — Complete control over data retention when self-hosted
highVerified: 2025-11-07
gdpr compliance

Privacy architecture review

Evidence
Self-hosted Model — Full GDPR compliance control in self-hosted setup
highVerified: 2025-11-07
hipaa eligible

Healthcare compliance assessment

Evidence
Self-hosted Model — HIPAA compliance possible with proper self-hosted infrastructure
highVerified: 2025-11-07
soc2 certified

Deployment architecture review

Evidence
Self-hosted Model — SOC 2 depends on hosting infrastructure
highVerified: 2025-11-07
data sovereignty

Deployment model analysis

Evidence
Self-hosted Model — Complete data sovereignty with self-hosting
highVerified: 2025-11-07
encryption at rest

Deployment architecture review

Evidence
Self-hosted Model — User-controlled encryption at rest
highVerified: 2025-11-07
encryption in transit

Deployment architecture review

Evidence
Self-hosted Model — User-controlled TLS configuration
highVerified: 2025-11-07
👁️Trust & Transparency
+
model documentation

Documentation completeness review

Evidence
Meta Model Card — Comprehensive model card with detailed documentation
highVerified: 2025-11-07
training data transparency

Public documentation review

Evidence
Meta Documentation — Training data composition and size disclosed (15T tokens)
highVerified: 2025-11-07
safety testing transparency

Safety documentation review

Evidence
Meta Safety Report — Detailed safety evaluations published
highVerified: 2025-11-07
bias evaluation

Bias benchmarks review

Evidence
Meta Model Card — Bias testing results disclosed
highVerified: 2025-11-07
decision explainability

Model accessibility assessment

Evidence
Open Weights — Complete model transparency with open weights
highVerified: 2025-11-07
versioning changelog

Version management review

Evidence
Meta Releases — Clear versioning with detailed release notes
highVerified: 2025-11-07
⚙️Operational Excellence
+
deployment flexibility

Deployment options review

Evidence
Open Model — Self-host anywhere, cloud, on-prem, edge, or via APIs
highVerified: 2025-11-07
api reliability

Third-party API monitoring

Evidence
Third-party APIs — ~99.5% uptime via major providers
mediumVerified: 2025-11-07
rate limits

Deployment model analysis

Evidence
Self-hosted Model — No rate limits when self-hosted
highVerified: 2025-11-07
cost efficiency

Cost analysis

Evidence
Together AI Pricing — $3.00 per 1M input tokens via API, infrastructure costs for self-hosting
mediumVerified: 2025-11-07
monitoring observability

Tooling availability assessment

Evidence
Self-hosted Model — User-implemented monitoring for self-hosted
mediumVerified: 2025-11-07
support quality

Support channels review

Evidence
Community Support — Community support via GitHub and forums, no official support
mediumVerified: 2025-11-07
Strengths
  • +Complete transparency with open weights
  • +Best-in-class data sovereignty and privacy control
  • +Maximum deployment flexibility (cloud, on-prem, edge)
  • +No vendor lock-in or rate limits when self-hosted
  • +Excellent documentation and model cards
  • +Competitive performance with proprietary models
  • +Strong community support and ecosystem
Limitations
  • !Requires significant infrastructure for self-hosting (8x H100 GPUs minimum)
  • !No official commercial support
  • !Text-only (no native vision)
  • !Safety guardrails can be modified (security consideration)
  • !Higher latency compared to smaller models
  • !Complex deployment and maintenance
  • !Legacy status: Meta has shipped no new open weights since Llama 4 Scout/Maverick (April 2025) and pivoted to closed models in 2026, so future open updates are unlikely
Metadata
license: Llama 3.1 Community License (open for commercial use)
architecture: Transformer with Grouped-Query Attention
parameters: 405 billion
training cutoff: December 2023
languages supported: Multilingual (8 languages optimized)
function calling: true
json mode: true
streaming: true

Use Case Ratings

code generation

Strong coding capabilities with complete control

customer support

Good performance, self-hosting ideal for sensitive data

content creation

Strong creative capabilities with full customization

data analysis

Good analytical capabilities

research assistant

128K context with complete data privacy

healthcare

Self-hosting ideal for HIPAA compliance and sensitive data

legal compliance

Complete confidentiality with self-hosting

education

Good capabilities with full control over content

creative writing

Good creative capabilities with customization options