Meta: Llama 3.1 405B Instruct

Text input Text output
Author's Description

The highly anticipated 400B class of Llama3 is here! Clocking in at 128k context with impressive eval scores, the Meta AI team continues to push the frontier of open-source LLMs. Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 405B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong performance compared to leading closed-source models including GPT-4o and Claude 3.5 Sonnet in evaluations. To read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).

Key Specifications
Cost
$$$
Context
32K
Parameters
405B
Released
Jul 22, 2024
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Tools Tool Choice Response Format Stop Seed Min P Top P Max Tokens Frequency Penalty Temperature Presence Penalty
Features

This model supports the following features:

Tools Response Format
Performance Summary

Meta's Llama 3.1 405B Instruct model, released on July 22, 2024, demonstrates strong capabilities as a high-quality dialogue optimized LLM. It consistently ranks among the fastest models across nine benchmarks and offers competitive pricing, placing in the 46th percentile. The model exhibits exceptional reliability with a 97% success rate, indicating minimal technical failures. In terms of performance, Llama 3.1 405B Instruct shows particular strength in ethical reasoning and classification tasks, achieving perfect 100% accuracy in both Ethics and Email Classification benchmarks. It also performs well in mitigating hallucinations, with a 96.0% accuracy in the Hallucinations (Baseline) test. While its General Knowledge and Coding scores are in the lower percentiles (31st), its Mathematics accuracy is moderate at 82.0%. A notable weakness is observed in one instance of the Instruction Following benchmark, where it scored 0.0% accuracy, though it achieved 60.0% in another, suggesting variability or a specific challenge in highly complex, multi-layered instructions. Overall, the model presents a compelling option for dialogue-centric applications, balancing speed, reliability, and strong performance in critical areas.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.8
Completion $0.8

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
DeepInfra
DeepInfra | meta-llama/llama-3.1-405b-instruct 32K $0.8 / 1M tokens $0.8 / 1M tokens
Lambda
Lambda | meta-llama/llama-3.1-405b-instruct 131K $0.8 / 1M tokens $0.8 / 1M tokens
Nebius
Nebius | meta-llama/llama-3.1-405b-instruct 131K $1 / 1M tokens $3 / 1M tokens
Fireworks
Fireworks | meta-llama/llama-3.1-405b-instruct 131K $3 / 1M tokens $3 / 1M tokens
Together
Together | meta-llama/llama-3.1-405b-instruct 130K $3.5 / 1M tokens $3.5 / 1M tokens
Hyperbolic
Hyperbolic | meta-llama/llama-3.1-405b-instruct 131K $4 / 1M tokens $4 / 1M tokens
SambaNova
SambaNova | meta-llama/llama-3.1-405b-instruct 16K $0.8 / 1M tokens $0.8 / 1M tokens
Google
Google | meta-llama/llama-3.1-405b-instruct 128K $5 / 1M tokens $16 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Strategy Free Executions Accuracy Cost Duration
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