Mixtral 8x22B Instruct
Neural Network
Mixtral 8x22B Instruct is a Mistral MoE model with 39B active parameters out of 141B, suitable for coding and math.
Max answer length
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Prompt cost
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How it works Mixtral 8x22B Instruct?
Mixtral 8x22B Instruct is the official instruct version of Mistral's Mixtral 8x22B, built on a sparse MoE architecture: 39 billion out of 141 billion parameters are used per request, providing high computational efficiency for a model of this size. The vendor highlights its strengths in math and coding, while instruct fine-tuning ensures accurate adherence to instructions. In BotHub, the model is accessible without a VPN or foreign card; you can pay with Russian cards in rubles, pay-as-you-go, and tokens do not expire. With over 250 models in one window, comparing answers is easy, and a unified OpenAI-compatible API allows switching models without rewriting integrations. Companies can connect via contract with invoicing, EDI, and an admin panel with limits. Use Mixtral 8x22B Instruct to write and refactor code, analyze third-party modules, debug, solve math problems with step-by-step breakdowns, build assistants for internal services via API, or process large volumes of similar text requests.Frequently asked questions about Mixtral 8x22B 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.
Mistral AI's mixtral-8x22b-instruct works with text and documents: it writes and edits materials, analyzes code, helps with refactoring and debugging, and answers questions based on uploaded files. The 65,536-token context allows you to handle long instructions, correspondence, and entire technical documents.
The single response limit is 52,428 tokens, which is dozens of pages of text. It is enough for a large document, detailed analysis, or a bulky code module in one pass. If the material doesn't fit, ask it to continue: the model maintains a 65,536-token context.
Our data does not indicate a separate reasoning mode for the model, so we cannot promise hidden step-by-step analysis. In practice, a simple trick helps: ask in the prompt to break down the solution step-by-step and show the thought process — answers regarding logic and STEM become noticeably more accurate.
Yes. The model supports function calling and structured JSON output, making it easy to integrate into agents and services: it selects the necessary tool itself and provides the response in the specified schema. In BotHub, this works via a unified OpenAI-compatible API.
In addition to text, the model accepts documents — files can be uploaded and analyzed natively without separate conversion. Input for images, audio, and video is not stated in our data: for such tasks in BotHub, switch to a multimodal model in the same window without changing the integration.
There are no separate measurements for Russian in the model's data, so we do not provide figures. Mistral AI is developing Mixtral as a multilingual line, and in most scenarios, Russian queries are processed correctly. The best way is to test it on your task and compare the response with another model in BotHub.