Mistral 7B Instruct
Mistral 7B Instruct is a text generation model from Mistral with a 4,096-token context window, available via Amazon Bedrock.
Mistral
Amazon BedrockCompact instruction-following text generation model
Mistral 7B Instruct is a 7-billion-parameter language model developed by Mistral AI and released in September 2023. It is the instruction-tuned variant of the base Mistral 7B model, fine-tuned to follow user instructions and engage in conversational exchanges. The model is hosted on Amazon Bedrock under the identifier mistral.mistral-7b-instruct-v0:2 and is currently marked as deprecated on that platform.
The model accepts text input and generates text output within a 4,096-token context window, with a maximum response size of 2,500 tokens. Its relatively compact parameter count makes it suitable for tasks such as summarization, question answering, and instruction-following where lower latency or resource efficiency is a consideration. Developers should note its deprecated status on Amazon Bedrock, which may affect long-term availability and support.
What Mistral 7B Instruct supports
Instruction Following
Fine-tuned to respond to explicit user instructions and prompts, making it suitable for task-oriented text generation and Q&A workflows.
Text Generation
Generates coherent text responses up to a maximum of 2,500 tokens per reply within a 4,096-token context window.
Conversational Dialogue
Supports multi-turn conversational exchanges using the instruct fine-tuning format introduced at release in September 2023.
Summarization
Can condense longer text passages into shorter summaries within the constraints of its 4,096-token context window.
Amazon Bedrock Integration
Deployed via Amazon Bedrock under the model ID mistral.mistral-7b-instruct-v0:2, enabling access through AWS infrastructure without managing model hosting.
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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 | 24.5% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 17.7% |
| MATH-500 | Undergraduate and competition-level math problems | 12.1% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 4.6% |
| HLE | Questions that challenge frontier models across many domains | 4.3% |
| SciCode | Scientific research coding and numerical methods | 2.4% |
Common questions about Mistral 7B Instruct
What is the context window size for Mistral 7B Instruct?
Mistral 7B Instruct has a context window of 4,096 tokens, with a maximum response size of 2,500 tokens per generation.
Is Mistral 7B Instruct still available on Amazon Bedrock?
The model is currently marked as deprecated on Amazon Bedrock (model ID: mistral.mistral-7b-instruct-v0:2). Deprecated status means it may have limited support or availability going forward.
What types of tasks is Mistral 7B Instruct designed for?
It is an instruction-tuned model designed for text generation tasks such as question answering, summarization, and conversational dialogue based on user-provided prompts.
Does Mistral 7B Instruct support image or video inputs?
No. Based on the available metadata, Mistral 7B Instruct accepts only text inputs and does not support image or video analysis.
When was Mistral 7B Instruct released?
Mistral 7B Instruct was released in September 2023 by Mistral AI.
What people think about Mistral 7B Instruct
Community discussions around Mistral 7B Instruct on r/LocalLLaMA tend to focus on its performance relative to other open-source models in the 7B–9B parameter range, with users running local benchmarks across a variety of tasks. It is frequently included in comparative evaluations due to its open availability and ease of local deployment.
Some discussions highlight hardware considerations such as GPU backend choices (ROCm vs. Vulkan) that affect inference performance on consumer hardware. Users also show interest in the model's behavioral characteristics, including work probing its hidden states to understand response tendencies, reflecting ongoing community interest in understanding smaller open-source models at a technical level.
I locally benchmarked 41 open-source LLMs across 19 tasks and ranked them
I measured the "personality" of 6 open-source LLMs (7B-9B) by probing their hidden states. Here's what I found.
ROCM vs Vulkan on IGPU
Parameters & options
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