Mixtral | Free AI tool


Supports Mixtral 7B and 8x7B. Mixtral AI's next-generation conversational AI uses intelligent Q&A capabilities to solve your tough questions.



Mixtral is an innovative AI chat assistant application designed to provide intelligent and real-time question-answering and interactive experiences for users. Whether you need an online assistant for queries or want to engage in conversations with a professional chatbot anytime and anywhere, Mixtral can meet your needs.

Key Features
Context Window: 8k tokens
Cost: Free
Intelligent Responses: Mixtral features a groundbreaking "Prompt template" that replaces user input with corresponding variables and generates responses aligned with user expectations using a powerful LLM model. Whether you have a question or need help solving a problem, Mixtral provides professional and personalized answers.

Real-time Interactions: Interacting with Mixtral is a fun and highly interactive experience. You can ask questions, seek advice, and share ideas, and Mixtral will promptly respond with immediate answers and engage in ongoing conversation.

Multi-domain Knowledge: Mixtral possesses a vast knowledge base and delivers accurate and useful information across multiple domains. Whether you are seeking travel recommendations, professional consultations, academic knowledge, or any other domain-specific query, Mixtral offers precise answers.

User-friendly Experience: Mixtral is designed with a simple and intuitive user interface. Just input your question or topic and click send to start the conversation with Mixtral. It quickly provides the desired information without the need for complex operations or learning curves.
Use Cases
Mixtral is suitable for various use cases, providing a comprehensive and personalized online communication experience.
Mixtral FAQs
What is Mixtral?
Answer: Mixtral is a high-quality sparse mixture-of-experts model (SMoE) developed by Mistral AI. It stands out for its efficiency and performance, being particularly strong in multiple language handling and code generation. Licensed under Apache 2.0, Mixtral is designed for both developers and researchers in the AI field​​​​.

How does Mixtral compare to other AI models like GPT-3.5?
Answer: Mixtral has shown the capability to match or outperform other prominent models like Llama 2 70B and GPT-3.5 in most benchmarks. Specifically, Mixtral Instruct outperforms all other open-access models on MT-Bench and is comparable in performance to GPT-3.5​​​​.

What languages can Mixtral handle?
Answer: Mixtral is proficient in multiple languages, including English, French, Italian, German, and Spanish. This multilingual capability is coupled with strong performance in code generation​​.

What is the architectural design of Mixtral?
Answer: Mixtral is a decoder-only model that utilizes a sparse mixture-of-experts network. It features a feedforward block that chooses from a set of 8 distinct groups of parameters (experts) for processing each token, combining their outputs additively​​.

How many parameters does Mixtral have?
Answer: Although Mixtral has a total of 46.7 billion parameters, it uses only 12.9 billion parameters per token. This design allows it to operate with the speed and cost efficiency of a 12.9 billion parameter model​​.

How can developers use or deploy Mixtral?
Answer: Mixtral supports deployment with an open-source stack, including integration with Megablocks CUDA kernels for efficient inference. Developers can deploy it on various cloud instances through Skypilot. Additionally, Mixtral models can be run using the pipeline() function of Transformers or with Text Generation Inference for advanced features​​​​.

Is there a demo or interactive platform for Mixtral?
Answer: Yes, the Mixtral Instruct model is available on Hugging Face Chat, allowing users to interact and experiment with the model in a conversational format​​.

Are there any limitations or open questions about Mixtral?
Answer: While Mixtral represents a significant advancement in AI models, there are still open questions regarding the size and composition of the dataset used for its pretraining, as well as details about its fine-tuning datasets and hyperparameters​​.