Llama 3.3 70B Instruct
Neural Network
Llama 3.3 70B Instruct is Meta's multilingual 70B parameter text model for dialogue and instruction-based tasks.
Max answer length
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Context size
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Prompt cost
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Answer cost
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How it works Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is a 70-billion parameter language model by Meta: pre-trained and instruction-tuned, it operates on a text-in, text-out basis and is designed for multilingual dialogue. In BotHub, Llama 3.3 is available without a VPN or foreign card: pay with a Russian card in rubles only for tokens used, not a subscription, and your balance never expires. Compare answers from other neural networks in the same window with a few clicks. The unified BotHub API allows you to connect Llama to your service and swap it for another model later without rewriting the integration. Data is transmitted encrypted, and companies have access to contracts, invoices, electronic document management, and an admin panel with limits. It is useful for building a support chat assistant, summarizing long documents or correspondence, rewriting and proofreading drafts, preparing responses to common customer questions, and running batch text processing via API.Frequently asked questions about Llama 3.3 70B 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 3.3 70B Instruct is a Meta text model for dialogue and instruction-based tasks: it writes and edits text, analyzes code, helps with refactoring and debugging, and solves logic and STEM problems. The 128,000-token context allows you to work with long correspondence, documentation, or entire large projects.
The model generates up to 16,384 tokens per response — enough for a lengthy article, a large documentation section, or several code files. If the material doesn't fit, just ask it to continue: the beginning remains in the 128,000-token context, and the model won't lose the thread.
A separate step-by-step reasoning mode is not noted in our data: the model usually answers immediately. However, you can get a detailed breakdown via a prompt — ask it to write out the solution step-by-step, compare options, and only then draw a conclusion. This technique works well for logic, math, and code analysis.
Yes. The model supports function calling and structured JSON output, making it easy to integrate into agents, chatbots, and pipelines: it selects the necessary tool and returns fields according to a specified schema. In BotHub, this works via a unified OpenAI-compatible API, so the model can be swapped without rewriting the integration.
You can upload images and documents — the model will analyze a screenshot, diagram, chart, or file and respond with text. Audio and video input are not noted in our data. Generation other than text is also not noted: create images and audio using other models in the same BotHub window.
Our data does not contain notes on languages, so we cannot promise a specific level of Russian. The easiest way to check is in practice: send your typical request and evaluate the response. If the wording doesn't suit you, switch to another model in the same window — tokens don't expire, and you pay only for what you use.