Mixtral 8x7B Instruct
Mixtral 8x7B Instruct is a text generation model from Mistral using a sparse mixture-of-experts architecture with a 4096 token context window.
Mistral
Amazon BedrockSparse mixture-of-experts instruct model
Mixtral 8x7B Instruct is a text generation model developed by Mistral AI and released in December 2023. It uses a sparse mixture-of-experts (SMoE) architecture consisting of 8 expert feed-forward layers per transformer block, with only 2 experts active per token during inference. This design allows the model to draw on a larger total parameter count while keeping active computation per token lower than a dense model of equivalent size.
The model is an instruction-tuned variant of the base Mixtral 8x7B, fine-tuned to follow user instructions and engage in conversational tasks. It is suited for tasks such as text summarization, question answering, and general instruction following in English and several other European languages. On MindStudio, it is served through Amazon Bedrock under the identifier mistral.mixtral-8x7b-instruct-v0:1, with a maximum response size of 2500 tokens. Note that this model has been marked as deprecated.
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 interactions.
Text Summarization
Condenses longer passages into concise summaries within the 4096 token context window.
Multilingual Text Generation
Supports text generation in English, French, Italian, German, and Spanish based on Mistral's published model details.
Code Generation
Capable of generating and explaining code snippets across common programming languages, as documented by Mistral for the Mixtral 8x7B family.
Sparse MoE Inference
Uses a mixture-of-experts architecture that activates only 2 of 8 expert layers per token, reducing active computation relative to total parameter count.
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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 size for Mixtral 8x7B Instruct?
The model has a context window of 4096 tokens, meaning the combined length of your input and output cannot exceed this limit.
What is the maximum response size?
The maximum response size is 2500 tokens per generation.
Is Mixtral 8x7B Instruct still available on MindStudio?
The model is currently marked as deprecated on MindStudio, which means it may no longer be actively supported or recommended for new projects.
Does this model support image or video inputs?
No. Mixtral 8x7B Instruct is a text-only model and does not support image or video analysis.
What is the knowledge cutoff for Mixtral 8x7B Instruct?
Mistral has not published an exact knowledge cutoff date for this model. Given its release in December 2023, training data likely extends to sometime in 2023, but a precise cutoff has not been officially confirmed.
What provider serves this model on MindStudio?
This model is served through Amazon Bedrock, under the full model identifier mistral.mixtral-8x7b-instruct-v0:1.
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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