Llama 3.1 405B Instruct

Llama 3.1 405B Instruct

Description of Llama 3.1 405B Instruct

Llama 3.1 405B Instruct is a large open-source language model developed by Meta. It has 405 billion parameters and supports a context window of 128,000 tokens, allowing it to process long documents and complex multi-part queries in a single pass. The model was released on July 23, 2024.

The model belongs to Meta's Llama family and is designed for both conversational scenarios and text generation across a variety of applied tasks. Thanks to its large parameter count and wide context window, it can handle materials that require incorporating a large amount of information, from long legal texts to extensive technical specifications.

Llama 3.1 405B Instruct is optimized for multilingual tasks and supports eight languages. It also delivers strong results in programming, mathematics, and logical reasoning, making it a versatile tool for both engineering and research tasks.

Llama 3.1 405B Instruct Specifications

Characteristic	Value
Parameters	405.0 billion
Context window	128.0 thousand tokens
Release date	July 23, 2024
Training tokens	15.0 trillion tokens
Average score	79.2%
Input price (per 1M tokens)	$3.50
Output price (per 1M tokens)	$3.50
Max input tokens	128.0 thousand
Max output tokens	128.0 thousand
License	llama_3_1_community_license
Supported capabilities	Function Calling, Structured Output, Code Execution, Web Search, Batch Inference, Fine-tuning

Who is Llama 3.1 405B Instruct for?

Developers and engineers

Thanks to support for code generation, code execution, and function calling, the model suits developers who need an assistant for writing, checking, and debugging program code directly in their workflow.

Researchers and analysts

The 128,000-token context window allows processing lengthy research papers, reports, and data, while its logical reasoning and math capabilities support research and analysis.

Companies and teams working with long texts

The model suits teams that need to work with large documents such as contracts, technical documentation, and multilingual texts. Fine-tuning extends its applicability to specific business tasks.

How to use Llama 3.1 405B Instruct

Through hosting platforms

The model is available through a number of platforms and hosting services, which simplifies access for users in different countries.

Via API with pay-per-token pricing

The model can be connected via an API at $3.50 per 1 million tokens for both input and output. This format is convenient for integrating the model into your own applications and services.

Considering supported languages

When working with the model, note that it supports eight languages. This places a limitation on using the model for tasks involving languages outside this list.

Key Features of Llama 3.1 405B Instruct

Long-context processing

The 128,000-token context window lets the model analyze and process large volumes of information in a single request, which is especially useful when working with long documents.

Multilingual support

The model supports eight languages and is optimized for multilingual conversational tasks, allowing users to communicate and generate text across languages.

Technical capabilities

The model supports Function Calling, Structured Output, Code Execution, Web Search, Batch Inference, and Fine-tuning. This makes it a full-featured tool for integration into complex software solutions.

Advantages of Llama 3.1 405B Instruct

Open source

The model is available as open source, allowing users to study its architecture, adapt it to their own needs, and use it in a wide variety of projects without the constraints of closed systems.

High performance

On standard industry benchmarks, the model outperforms many open-source alternatives and a number of proprietary chat models. This is confirmed by an average score of 79.2% on tests.

Accessibility and flexibility

The model is widely accessible through a range of platforms, while support for fine-tuning and function calling makes it a flexible tool for many applied tasks.

Disadvantages of Llama 3.1 405B Instruct

According to available sources, no obvious drawbacks have been identified. However, it should be noted that the model is one of the largest in the Llama family, with 405 billion parameters, which may require significant computing resources for self-hosting. In addition, the number of supported languages is limited to eight, which may matter for users working with other languages.

What tasks does Llama 3.1 405B Instruct solve?

The model solves a wide range of text-processing tasks. With support for code generation and execution, it handles programming and automation tasks. Structured Output makes it possible to get results in a specified format, which is convenient when integrating into information systems.

Thanks to its wide context window, the model effectively handles tasks that require analyzing long documents and considering large amounts of input data. Function calling and web search extend the range of tasks to full-fledged work with external services and databases. Multilingual support allows the model to be used for translation, localization, and conversational tasks in eight languages.

Llama 3.1 405B Instruct Pricing

The cost of using the model is $3.50 per 1 million input tokens and $3.50 per 1 million output tokens. Thus, the pricing is symmetric — the price does not depend on which token type is processed.

It is also worth noting that Meta sets prices for each model in the Llama family separately. For example, more compact models are significantly cheaper. In contrast, the price of Llama 3.1 405B Instruct reflects its scale and performance.

Terms of Use for Llama 3.1 405B Instruct

The model is distributed under the llama_3_1_community_license, which defines the terms of its use and distribution. There is no paid subscription required to work with the model — it is distributed free of charge.

A particular convenience is that the model can be used through a variety of platforms and hosting services, which makes it much easier to start working with the model for users in regions where access to certain external services is restricted.

Availability of Llama 3.1 405B Instruct

The model is available through a variety of platforms and hosting services, and no special access setup is required. This makes it accessible to a wide range of users without having to deal with regional access restrictions.

Regarding language availability, the model supports eight languages, covering the world's major languages for most tasks. However, it is important to keep this list in mind when planning projects that require working with specific languages.

How Llama 3.1 405B Instruct Differs from Alternatives

Within the Llama family

Within the Llama family, the model stands out for its scale — 405 billion parameters — significantly more than, for example, Llama 3.1 70B Instruct or Llama 3.3 70B Instruct. The larger parameter count allows the model to achieve higher results on standard benchmarks, including coding, mathematics, and logical reasoning.

Compared with other open-source and proprietary models

Compared with alternatives such as Qwen3 235B A22B, Mistral Large 2, Kimi K2 0905, and MiniMax M2, Llama 3.1 405B Instruct stands out for its combination of open-source code and high performance. It outperforms many available open-source models and a number of proprietary chat models on standard industry benchmarks, making it a competitive alternative even against commercial solutions.

Conclusion

Llama 3.1 405B Instruct is one of the largest and most capable open-source language models from Meta. With its 405 billion parameters, 128,000-token context window, and support for eight languages, it delivers strong results on many benchmarks, surpassing numerous open-source and proprietary counterparts. The model is available under an open-source license, and its availability through a variety of platforms makes it accessible to a wide audience. Thanks to support for function calling, code execution, web search, and fine-tuning, Llama 3.1 405B Instruct is a versatile and flexible tool for a wide range of tasks — from text generation and programming to complex research and analytical projects.