Ministral 3 14B
Optimized for local deployment, it delivers high performance across diverse hardware, including local setups.
Mistral Large local model with 256K context window
Ministral 3 14B is the largest model in the Ministral 3 family, developed by Mistral AI. It is an open-source text generation model with a 256,000-token context window, designed to handle long-form inputs and extended conversations. The model is released under an open license, making it available for local deployment and self-hosted use cases.
The model is optimized for running on diverse hardware configurations, including consumer-grade local setups, which makes it suitable for developers and researchers who prefer on-device inference. Its 14 billion parameter count positions it as the largest variant in the Ministral 3 series. Common use cases include text generation, summarization, instruction following, and tasks that benefit from a large context window without requiring cloud-based infrastructure.
What Ministral 3 14B supports
Long Context Window
Supports up to 256,000 tokens of context, enabling processing of long documents, codebases, or extended multi-turn conversations in a single pass.
Text Generation
Generates coherent, instruction-following text across a range of tasks including summarization, Q&A, and creative writing.
Local Deployment
Optimized to run on diverse local hardware configurations, including consumer-grade setups, without requiring cloud infrastructure.
Open Source Access
Released as an open-source model, allowing developers to download, modify, and self-host the weights directly.
Instruction Following
Trained to follow natural language instructions, supporting chat-style interactions and task-oriented prompting.
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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 | 69.3% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 57.2% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 35.1% |
| HLE | Questions that challenge frontier models across many domains | 4.6% |
| SciCode | Scientific research coding and numerical methods | 23.6% |
Common questions about Ministral 3 14B
What is the context window size for Ministral 3 14B?
Ministral 3 14B supports a context window of 256,000 tokens, allowing it to process long documents or extended conversations in a single request.
Is Ministral 3 14B open source?
Yes, Ministral 3 14B is released as an open-source model by Mistral AI, meaning the weights are publicly available for download and local use.
Can I run Ministral 3 14B on my own hardware?
Yes, the model is specifically optimized for local deployment across diverse hardware configurations, including consumer-grade setups.
What is the training data cutoff for Ministral 3 14B?
The training date is listed as not available in the current metadata. Check Mistral AI's official documentation for the most up-to-date information on training data.
How does Ministral 3 14B relate to other models in the Ministral 3 family?
Ministral 3 14B is the largest model in the Ministral 3 family. According to Mistral AI, its performance is described as comparable to the larger Mistral Small 3.2 24B model.
What people think about Ministral 3 14B
Community discussions on r/LocalLLaMA show general interest in the Ministral 3 release, with the announcement thread receiving 282 upvotes and 61 comments shortly after launch. Users in the llama.cpp benchmarks thread (71 upvotes) engaged with concrete performance data for local inference scenarios.
A recurring theme across threads is the model's suitability as a local base model, with some users evaluating it for on-device use cases. The broader context of Mistral releasing multiple models in a short period (871-upvote thread) generated discussion about the pace of releases, though specific limitations of Ministral 3 14B were not a dominant focus.
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