Long Context Window
Processes up to 256,000 tokens in a single request, allowing the model to handle long documents, codebases, or extended conversations without truncation.
Mistral
Ministral 3 8B
Ministral 3 8B is a text generation model developed by Mistral AI, part of the Ministral 3 model family. It is open source and designed with edge deployment in mind, meaning it is optimized to run efficiently across a range of hardware configurations, including local setups without cloud infrastructure. The model supports a 256,000-token context window, enabling it to process and reason over long documents in a single pass. Ministral 3 8B is well-suited for developers and organizations that need a capable language model deployable on-device or in resource-constrained environments. Its 8-billion parameter size makes it practical for local inference while still handling a broad range of text generation tasks. The open-source availability means it can be downloaded, fine-tuned, and self-hosted without requiring API access.
A specific training data cutoff date is not provided in the available metadata for this model.
256,000 context
16,000 tokens output
Long Context Window
Edge Deployment
Text Generation
Open Source
Local Inference
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.
A specific training data cutoff date is not provided in the available metadata for this model.
Knowledge Cut-off Date
When the model's knowledge was last updated.
A specific training data cutoff date is not provided in the available metadata for this model.
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 8B
A fuller summary of positioning, capabilities, and source-specific details for Ministral 3 8B.
Ministral 3 8B is a text generation model developed by Mistral AI, part of the Ministral 3 model family. It is open source and designed with edge deployment in mind, meaning it is optimized to run efficiently across a range of hardware configurations, including local setups without cloud infrastructure. The model supports a 256,000-token context window, enabling it to process and reason over long documents in a single pass.
Ministral 3 8B is well-suited for developers and organizations that need a capable language model deployable on-device or in resource-constrained environments. Its 8-billion parameter size makes it practical for local inference while still handling a broad range of text generation tasks. The open-source availability means it can be downloaded, fine-tuned, and self-hosted without requiring API access.
Capabilities
What Ministral 3 8B supports
Edge Deployment
Optimized to run on diverse hardware including local machines, making it suitable for on-device inference without relying on cloud infrastructure.
Text Generation
Generates coherent, contextually relevant text across tasks such as summarization, question answering, and instruction following.
Open Source
Released as an open-source model, allowing developers to download, self-host, and fine-tune the weights without proprietary restrictions.
Local Inference
Supports running entirely on local hardware setups, enabling private, offline use cases without sending data to external servers.
Pricing for Ministral 3 8B
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.15
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
|
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
|
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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 8B
Ministral 3 8B discussions are most active in r/LocalLLaMA, r/SillyTavernAI, r/MistralAI.
Top Reddit threads cluster around benchmark and model-comparison threads, safety and censorship questions, coding workflow discussions. The strongest match in this snapshot has 872 upvotes and 114 comments.
[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)
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.
I noticed there's almost no talk about it's recent release here or on other sub's recently.
I've dabbled with 8b and 14b non-reasoning GGUF's and was curious if anyone else has tried it out and their experience?
[https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-GGUF](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-GGUF)
[https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512-GGUF](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512-GGUF)
Here are the GGUF links to Mistral AI’s "collected works" from the past week – all ready for local use:
**Cutting-edge coding models:**
\- 24B parameters: [https://huggingface.co/bartowski/mistralai\_Devstral-Small-2-24B-Instruct-2512-GGUF](https://huggingface.co/bartowski/mistralai_Devstral-Small-2-24B-Instruct-2512-GGUF)
\- 123B parameters: [https://huggingface.co/bartowski/mistralai\_Devstral-2-123B-Instruct-2512-GGUF](https://huggingface.co/bartowski/mistralai_Devstral-2-123B-Instruct-2512-GGUF)
**Top-tier reasoning models – perfectly sized for consumer hardware:**
\- 3B parameters: [https://huggingface.co/bartowski/mistralai\_Ministral-3-3B-Reasoning-2512-GGUF](https://huggingface.co/bartowski/mistralai_Ministral-3-3B-Reasoning-2512-GGUF)
\- 8B parameters: [https://huggingface.co/bartowski/mistralai\_Ministral-3-8B-Reasoning-2512-GGUF](https://huggingface.co/bartowski/mistralai_Ministral-3-8B-Reasoning-2512-GGUF)
\- 14B parameters: [https://huggingface.co/bartowski/mistralai\_Ministral-3-14B-Reasoning-2512-GGUF](https://huggingface.co/bartowski/mistralai_Ministral-3-14B-Reasoning-2512-GGUF)
**Powerful instruct models for local setups:**
\- 3B parameters: [https://huggingface.co/bartowski/mistralai\_Ministral-3-3B-Instruct-2512-GGUF](https://huggingface.co/bartowski/mistralai_Ministral-3-3B-Instruct-2512-GGUF)
\- 8B parameters: [https://huggingface.co/bartowski/mistralai\_Ministral-3-8B-Instruct-2512-GGUF](https://huggingface.co/bartowski/mistralai_Ministral-3-8B-Instruct-2512-GGUF)
\- 14B parameters: [https://huggingface.co/bartowski/mistralai\_Ministral-3-14B-Instruct-2512-GGUF](https://huggingface.co/bartowski/mistralai_Ministral-3-14B-Instruct-2512-GGUF)
**Mistral’s most advanced instruct model:**
\- 675B parameters: [https://huggingface.co/bartowski/mistralai\_Mistral-Large-3-675B-Instruct-2512-GGUF](https://huggingface.co/bartowski/mistralai_Mistral-Large-3-675B-Instruct-2512-GGUF)
**Licensing:** All models under Apache 2.0, Devstral 2 with a modified MIT license.
What an insane achievement for a company that’s still small compared to OpenAI! Huge thanks to Mistral AI! <3
Today, we announce Mistral 3, the next generation of Mistral models. Mistral 3 includes three state-of-the-art small, dense models (14B, 8B, and 3B) and Mistral Large 3 – our most capable model to date – a sparse mixture-of-experts trained with 41B active and 675B total parameters. All models are released under the Apache 2.0 license. Open-sourcing our models in a variety of compressed formats empowers the developer community and puts AI in people’s hands through distributed intelligence. The Ministral models represent the best performance-to-cost ratio in their category. At the same time, Mistral Large 3 joins the ranks of frontier instruction-fine-tuned open-source models.
Learn more [here](https://mistral.ai/news/mistral-3).
# Ministral 3
A collection of edge models, with Base, Instruct and Reasoning variants, in 3 different sizes: **3B, 8B and 14B**. All with vision capabilities - **All Apache 2.0**.
* **Ministral 3 14B**: 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 counterpart. A powerful and efficient language model with vision capabilities.
* **Ministral 3 8B**: A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
* **Ministral 3 3B**: The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.
Weights [here](https://huggingface.co/collections/mistralai/ministral-3), with already quantized variants [here](https://huggingface.co/collections/mistralai/ministral-3-additional-checkpoints).
# Large 3
A state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture - with a Base and Instruct variants. **All Apache 2.0**. Mistral Large 3 is deployable on-premises in:
* [FP8](https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512) on a single node of B200s or H200s.
* [NVFP4](https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512-NVFP4) on a single node of H100s or A100s.
# Key Features
Mistral Large 3 consists of two main architectural components:
* **A Granular MoE Language Model with 673B params and 39B active**
* **A 2.5B Vision Encoder**
Weights [here](https://huggingface.co/collections/mistralai/mistral-large-3).
FAQ
Common questions about Ministral 3 8B
What is the context window size for Ministral 3 8B?
Ministral 3 8B supports a context window of 256,000 tokens, allowing it to process very long inputs in a single pass.
Is Ministral 3 8B open source?
Yes, Ministral 3 8B is released as an open-source model, meaning the weights can be downloaded and used or fine-tuned independently.
What hardware can Ministral 3 8B run on?
The model is built for edge deployment and is designed to run across diverse hardware configurations, including local setups and devices without dedicated cloud infrastructure.
Who developed Ministral 3 8B?
Ministral 3 8B was developed by Mistral AI and is part of the Ministral 3 model family.
What is the training data cutoff for Ministral 3 8B?
A specific training data cutoff date is not provided in the available metadata for this model.
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