Llama 3.1 405B Instruct
Meta's largest open foundation model with 405 billion dense parameters, rivaling leading closed frontier models.
128k
Tokens
8.192k
Output limit
Dense Transformer
Model family
405B
Total / Active
$1.79
Per 1M tokens
$1.79
Per 1M tokens
Model Overview
Llama 3.1 405B Instruct is the first openly available model of its scale, offering frontier-class knowledge, reasoning, and coding capabilities. Ideal for synthetic data generation and distilling smaller models.
Developer Implementation Notes
Requires an 8x H100 or 8x A100 GPU cluster (minimum ~230GB in FP8, 810GB in FP16) for self-hosting. Available on serverless cloud providers.
Key Strengths
- Largest openly accessible foundation model in the world
- Gold standard for synthetic data generation and teacher distillation
- Exceptional multilingual understanding across 8+ languages
Limitations & Boundaries
- Massive hardware requirements (8x 80GB GPUs minimum)
- High cloud hosting cost compared to 70B MoE models
Capabilities & Modalities
Best Production Use Cases
- Model distillation and synthetic dataset curation
- Frontier research on open weights
- Complex multilingual reasoning
Verified Benchmark Results
Standardized evaluations with methodology notes and authoritative citation links.
Pricing & Inference Cost Calculator
Token Cost Estimator – Llama 3.1 405B Instruct
Calculate projected inference spend with prompt caching
Quick Workload Presets
$3.5800
2.00M input tokens
$0.8950
0.50M output tokens
$4.47
Avg: $0.00447 / req
Compare with Similar Models
DeepSeek-R1
Open-weights frontier reasoning model trained via large-scale reinforcement learning without supervised cold start.
Codestral 2501
Mistral AI's dedicated code generation model with 256k context window and fill-in-the-middle (FIM) capabilities.
DeepSeek-V3
Frontier open-weights 671B MoE base and chat model with multi-head latent attention (MLA) and dual-pipe training.
Data Accuracy & Verification Notice
AI model specifications, pricing records, and benchmark metrics published on this platform are compiled directly from authoritative sources (official provider documentation, research papers, and verified evaluation harnesses). Benchmark results reflect specific test harnesses and prompting methodologies; scores are not directly comparable across differing evaluation setups.