LLM Reference

Llama 3.1 405B Instruct

Released
2024-07-23
Last refreshed
2026-05-16
Status
Researched 46d ago
Open SourceRAGLong contextClassificationJSON / Tool use

Llama 3.1 405B Instruct is worth evaluating for rag, long context, and classification when its provider route and context window match the workload.

Use it for

  • Teams evaluating rag, long context, and classification
  • Workloads that can use a 128k context window
  • Buyers comparing 4 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads
Specifications
Family
Llama 3.1
Released
2024-07-23
Context
128k
Parameters
405B
Architecture
Decoder Only
Knowledge cutoff
2023-12
Specialization
general
Training
finetuned
Created by

Large-scale open-source AI for social technologies.

Menlo Park, California, United States
Founded 2013
Website
Pricing
Output / 1M
$2.40
Input / 1M
$2.40

Cheapest of 11 routes · AWS Bedrock

About

Llama 3.1 405B Instruct is Meta's advanced large language model released on July 23, 2024, featuring 405 billion parameters. It utilizes an optimized transformer architecture with supervised fine-tuning and reinforcement learning for enhanced instruction-following capabilities. The model supports multiple languages, was trained on 15 trillion tokens, and fine-tuned with 25 million synthetic examples. It excels in multilingual dialogue and text generation, making it ideal for assistant-like applications. Llama 3.1 incorporates robust safety measures and ethical considerations, outperforming many existing models on various industry benchmarks. AI engineers can access the model via its Hugging Face page for implementation in diverse NLP tasks.

Llama 3.1 405B Instruct is an open-source model in the Llama 3.1 family. The structured metadata tracks a 128k-token context window and structured outputs. This page tracks provider routes through OctoAI API (Deprecated), Together AI, Fireworks AI, and 8 more, with the cheapest tracked route listed at $2.4 input and $2.4 output per 1M tokens. Headline tracked benchmarks include Massive Multitask Language Understanding 88.6.

Top use-case fit

RAG

Included by capability and metadata signals in the decision map.

Long context

Included by capability and metadata signals in the decision map.

Classification

Q/$ D

1 relevant benchmark in the decision map.

Provider price ladder

Compare all 11

Compare API pricing across 4 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
AWS Bedrock$2.40$2.40
Serverless
Fireworks AI$3.00$3.00
Serverless
Hyperbolic AI Inference$4.00$4.00
Serverless
IBM watsonx$3.00$9.00
Serverless

Capabilities

Structured Outputs

Benchmark peer barsfor Classification

Benchmark scores(1)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
Massive Multitask Language Understanding88.65-shothttps://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

Migration checks

No linked migration route is available for this model yet.

Rankings & picks(9)

Comparison and alternatives

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