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.
Mistral
Ministral 3 14B
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.
Unknown
256,000 context
16,000 tokens output
Long Context Window
Text Generation
Local Deployment
Open Source Access
Instruction Following
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Mistral
Input Context Window
The number of tokens supported by the input context window.
256,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
16,000 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Unknown
Knowledge Cut-off Date
When the model's knowledge was last updated.
Unknown
API Providers
The providers that offer this model. This is not an exhaustive list.
Mistral API, Hugging Face
Modalities
Types of data this model can process.
Text
What is Ministral 3 14B
A fuller summary of positioning, capabilities, and source-specific details for Ministral 3 14B.
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.
Capabilities
What Ministral 3 14B supports
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.
Pricing for Ministral 3 14B
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.20
Per million tokens
Output tokens
N/A
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
1
maxResponseSize
16,000 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Mistral API
Hugging Face
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
|
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HLE
Questions that challenge frontier models across many domains
|
|
|
LiveCodeBench
Real-world coding tasks from recent competitions
|
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
|
|
SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about Ministral 3 14B
Ministral 3 14B discussions are most active in r/LocalLLaMA, r/MistralAI, r/AIToolsPerformance.
Top Reddit threads cluster around benchmark and model-comparison threads, safety and censorship questions, coding workflow discussions. The strongest match in this snapshot has 873 upvotes and 114 comments.
45 minutes and 33K tokens of thinking about making html tetris (1 line prompt):
https://preview.redd.it/jzjcom93105g1.png?width=500&format=png&auto=webp&s=8d67b1b895715d2dfbb927db0bc2bc485b28b819
Tool calling breaks all the time:
https://preview.redd.it/02edr424105g1.png?width=314&format=png&auto=webp&s=67cccfd1b1fdaa59da095b9bd31ef09f1ec1c184
Also at some point it stopped using the \[think\] tags altogether and just started thinking out loud. I'll leave it running for a couple of hours and see if it eventually manages to build the HTML Tetris.
Hello everyone,
Mistral AI just released a new lineup of their Mistral and Ministral models. However, there's a bit of confusion I personally noticed when I was looking around to find the Ministral model of the highest quality.
Please check out the screenshot I attached.
On the top of the screenshot you can see the model with standard name "Ministral-3-14B-Instruct-2512". This model sits in their standard collection named "Ministral 3". One would think this is the one with the best quality, because that's how it usually works, but not here - check out the note below it, it says FP8. In other words, it's a half precision of what the full quality offers!
However, when you enter the collection called "Ministral 3 - Additional Checkpoints", you will find another model named "Ministral-3-14B-Instruct-2512-BF16" as shown on the bottom of the screenshot. When you check the note below this one, it says "Standard BF16". This is the full precision model and if you're thinking about fine-tuning the model or quantizing it, this model on the bottom of the screenshot is probably the one you should be using, otherwise your final model may suffer severe quality loss!
Enjoy, have fun.
[https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512](https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512)
[https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512)
[https://huggingface.co/mistralai/Ministral-3-14B-Base-2512](https://huggingface.co/mistralai/Ministral-3-14B-Base-2512)
The largest model in the Ministral 3 family, **Ministral 3 14B** offers frontier capabilities and performance comparable to its larger [Mistral Small 3.2 24B](https://huggingface.co/mistralai/Mistral-Small-3.2-Instruct-2506) counterpart. A powerful and efficient language model with vision capabilities.
[https://huggingface.co/mistralai/Ministral-3-8B-Reasoning-2512](https://huggingface.co/mistralai/Ministral-3-8B-Reasoning-2512)
[https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512)
[https://huggingface.co/mistralai/Ministral-3-8B-Base-2512](https://huggingface.co/mistralai/Ministral-3-8B-Base-2512)
A balanced model in the Ministral 3 family, **Ministral 3 8B** is a powerful, efficient tiny language model with vision capabilities.
[https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512)
[https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512)
[https://huggingface.co/mistralai/Ministral-3-3B-Base-2512](https://huggingface.co/mistralai/Ministral-3-3B-Base-2512)
The smallest model in the Ministral 3 family, **Ministral 3 3B** is a powerful, efficient tiny language model with vision capabilities.
https://preview.redd.it/471e4lma6t4g1.png?width=1078&format=png&auto=webp&s=c23d37e6a361041132ccec451c0a03921acc6e13
https://preview.redd.it/c2szd14b6t4g1.png?width=1210&format=png&auto=webp&s=3d97fc5e8626f25f8c13a5b159e6351976f45de5
[https://huggingface.co/unsloth/Ministral-3-14B-Reasoning-2512-GGUF](https://huggingface.co/unsloth/Ministral-3-14B-Reasoning-2512-GGUF)
[https://huggingface.co/unsloth/Ministral-3-14B-Instruct-2512-GGUF](https://huggingface.co/unsloth/Ministral-3-14B-Instruct-2512-GGUF)
[https://huggingface.co/unsloth/Ministral-3-8B-Reasoning-2512-GGUF](https://huggingface.co/unsloth/Ministral-3-8B-Reasoning-2512-GGUF)
[https://huggingface.co/unsloth/Ministral-3-8B-Instruct-2512-GGUF](https://huggingface.co/unsloth/Ministral-3-8B-Instruct-2512-GGUF)
[https://huggingface.co/unsloth/Ministral-3-3B-Reasoning-2512-GGUF](https://huggingface.co/unsloth/Ministral-3-3B-Reasoning-2512-GGUF)
[https://huggingface.co/unsloth/Ministral-3-3B-Instruct-2512-GGUF](https://huggingface.co/unsloth/Ministral-3-3B-Instruct-2512-GGUF)
Below you’ll find a benchmark comparison of Ministral-3-14B-Reasoning-2512 against 10 other large language models.
**LiveCodeBench:**
|Model|LiveCodeBench|
|:-|:-|
|GLM-4.5-Air|70.7%|
|Gemini 2.5 Pro Preview|69.0%|
|Llama 3.1 Nemotron Ultra|66.3%|
|Qwen3 32B|65.7%|
|MiniMax M1 80K|65.0%|
|**Ministral 3 (14B Reasoning)**|**64.6%**|
|QwQ-32B|63.4%|
|Qwen3 30B A3B|62.6%|
|MiniMax M1 40K|62.3%|
|Ministral 3 (8B Reasoning)|61.6%|
|DeepSeek R1 Distill Llama|57.5%|
**GPQA:**
|Model|GPQA|
|:-|:-|
|o1-preview|73.3%|
|Qwen3 VL 32B Thinking|73.1%|
|Claude Haiku 4.5|73.0%|
|Qwen3-Next-80B-A3B-Instruct|72.9%|
|GPT OSS 20B|71.5%|
|**Ministral 3 (14B Reasoning)**|**71.2%**|
|GPT-5 nano|71.2%|
|Magistral Medium|70.8%|
|Qwen3 VL 30B A3B Instruct|70.4%|
|GPT-4o|70.1%|
|MiniMax M1 80K|70.0%|
**AIME 2024:**
|**Model**|**AIME 2024**|
|:-|:-|
|Grok-3|93.3%|
|Gemini 2.5 Pro|92.0%|
|o3|91.6%|
|DeepSeek-R1-0528|91.4%|
|GLM-4.5|91.0%|
|**Ministral 3 (14B Reasoning 2512)**|**89.8%**|
|GLM-4.5-Air|89.4%|
|Gemini 2.5 Flash|88.0%|
|o3-mini|87.3%|
|DeepSeek R1 Zero|86.7%|
|DeepSeek R1 Distill Llama 70B|86.7%|
**AIME 2025:**
|**Model**|**AIME 2025**|
|:-|:-|
|Qwen3-Next-80B-A3B-Thinking|87.8%|
|DeepSeek-R1-0528|87.5%|
|Claude Sonnet 4.5|87.0%|
|o3|86.4%|
|GPT-5 nano|85.2%|
|**Ministral 3 (14B Reasoning 2512)**|85.0%|
|Qwen3 VL 32B Thinking|83.7%|
|Qwen3 VL 30B A3B Thinking|83.1%|
|Gemini 2.5 Pro|83.0%|
|Qwen3 Max|81.6%|
|Qwen3 235B A22B|81.5%|
All benchmark results are sourced from this page: [https://llm-stats.com/benchmarks/llm-leaderboard-full](https://llm-stats.com/benchmarks/llm-leaderboard-full)
Hey folks,
Back with another series of abilitered (uncensored) models, this time Ministral 3 with Vision capability. These models lost all their refusal with minimal damage.
As bonus, this time I also quantized them instead of waiting for community.
[https://huggingface.co/collections/coder3101/ministral-3-reasoning-heretic](https://huggingface.co/collections/coder3101/ministral-3-reasoning-heretic)
Series contains:
\- Ministral 3 4B Reasoning
\- Ministral 3 8B Reasoning
\- Ministral 3 14B Reasoning
All with Q4, Q5, Q8, BF16 quantization with MMPROJ for Vision capabilities.
FAQ
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.
More models from Mistral
Continue browsing adjacent models from the same provider.