Llama 3.1 8B Instruct
Neural Network
Llama 3.1 8B Instruct is a compact Meta model from the Llama 3.1 line for fast conversational tasks and API usage.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct is a compact Meta model from the Llama 3.1 line, released in several sizes. The 8-billion parameter version is fine-tuned to follow instructions and optimized for speed: it responds quickly and uses resources efficiently, making it suitable for high-volume request tasks. In BotHub, the model is accessible from Russia without a VPN or foreign card, and you can pay in rubles on a pay-as-you-go basis for tokens used, which do not expire. Over 250 other neural networks are available in the same window: it's easy to compare responses and switch between them. For developers, there is a unified OpenAI-compatible API — connect it once and switch models in the multi-model system without rewriting the integration, with chats protected by AES-GCM encryption. It is useful if you are building a chatbot or support assistant, processing large volumes of similar texts, preparing drafts and summaries, prototyping scenarios before running them on a heavier model, or embedding a fast language layer into an internal service.Frequently asked questions about Llama 3.1 8B Instruct
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
llama-3.1-8b-instruct is a compact instruction-tuned model from Meta for everyday text tasks: answering questions, rewriting and summarizing texts, data extraction, simple scripts, and code edits. It accepts documents and images as input, works with a context of up to 131,072 tokens, and is suitable for mass repetitive requests.
The output limit is 117,964 tokens per response: this is enough for a long document, a detailed instruction, or a large code file. The total context is 131,072 tokens, so your request, chat history, and the response itself must fit within this volume together.
A separate reasoning mode is not noted in our data, so you shouldn't count on hidden step-by-step thinking. However, you can directly ask the model to write out the solution step-by-step in the response text — this is usually enough for simple tasks. For complex logic, switch to a reasoning model in the same window.
Yes, function calling and structured JSON output are supported. You describe the tools in the request, the model returns the call arguments, and your code executes them. Through the unified OpenAI-compatible BotHub API, this connects without rewriting the integration, and the response schema is easy to fix for parsing.
You can upload images and documents — the model accepts them as input and responds with text. Audio and video as input formats are not noted in our data, so for audio or video analysis, choose a specialized model in BotHub: switching happens in the same window and via the same API.
The language of requests and responses is not fixed in our data, so we cannot promise specific quality for Russian — test it with your own phrasing, it won't take long. If the result is unsatisfactory, switch to another model in the same BotHub window without rewriting the integration, and you will pay only for the tokens used.