Mixtral 8x7B Instruct
High-quality, efficient sparse model outperforming larger models in speed and benchmarks.
Mistral 
Amazon Bedrock Sparse mixture-of-experts model with open weights
Mixtral 8x7B Instruct is a sparse mixture-of-experts (SMoE) language model developed by Mistral AI and released under the Apache 2.0 license. It uses a routing mechanism that activates only a subset of its expert networks per token, allowing it to draw on a large total parameter count while keeping active computation lower than a dense model of equivalent size. The instruct variant has been fine-tuned to follow instructions and engage in conversational tasks.
The model has a context window of 4,096 tokens and was trained on data through September 2023. Its open-weight, permissive license makes it suitable for commercial and research use cases where model access and reproducibility matter. It is well-suited for tasks such as text generation, summarization, question answering, and general instruction following.
What Mixtral 8x7B Instruct supports
Instruction Following
Fine-tuned to respond to user instructions and conversational prompts, making it suitable for chat-style and task-oriented applications.
Text Generation
Generates coherent, contextually relevant text across a variety of formats including summaries, explanations, and long-form responses.
Sparse MoE Architecture
Uses a mixture-of-experts routing mechanism that activates only a subset of expert layers per token, reducing active compute relative to total parameter count.
Code Generation
Capable of generating and explaining code across common programming languages, consistent with Mixtral 8x7B's published benchmark performance on coding tasks.
Question Answering
Answers factual and reasoning-based questions using knowledge from its training data, with a knowledge cutoff of September 2023.
Multilingual Text
Supports multiple languages including English, French, Italian, German, and Spanish, as documented in Mistral's model release.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 38.7% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 29.2% |
| MATH-500 | Undergraduate and competition-level math problems | 29.9% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 6.6% |
| HLE | Questions that challenge frontier models across many domains | 4.5% |
| SciCode | Scientific research coding and numerical methods | 2.8% |
Common questions about Mixtral 8x7B Instruct
What is the context window for Mixtral 8x7B Instruct?
The model supports a context window of 4,096 tokens, meaning the combined length of your input and the model's output cannot exceed that limit.
What is the knowledge cutoff date for this model?
Mixtral 8x7B Instruct was trained on data through September 2023, so it does not have knowledge of events that occurred after that date.
What license does Mixtral 8x7B Instruct use?
The model is released under the Apache 2.0 license, which permits commercial and research use with relatively few restrictions.
What is the difference between Mixtral 8x7B and the Instruct variant?
The base Mixtral 8x7B model is a pretrained language model, while the Instruct variant has been fine-tuned specifically to follow instructions and handle conversational interactions.
What types of tasks is Mixtral 8x7B Instruct best suited for?
The model is well-suited for instruction-following tasks such as text generation, summarization, question answering, code generation, and multilingual text tasks in languages including English, French, Italian, German, and Spanish.
What people think about Mixtral 8x7B Instruct
The available Reddit thread does not directly discuss Mixtral 8x7B Instruct, making it difficult to summarize community sentiment specific to this model. The thread touches on broader topics about AI model behavior rather than Mixtral in particular.
No concrete praise, criticism, or use case patterns for Mixtral 8x7B Instruct can be reliably drawn from the available community data. Developers interested in community feedback may find more relevant discussions on the Mistral AI Discord or the model's Hugging Face page.
Parameters & options
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