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LatestMistral AI1.0
Mistral AIVision-Language

Mistral's Shieldstral brings safety checks to images

A compact 3B open-weights classifier flags unsafe text and visual content, and ships under Apache 2.0.

Jul 16, 2026
NotableApache 2.0
Shieldstral 1.0 3B

Mistral AI has released Shieldstral 1.0 3B, a compact multimodal classifier built to moderate both text and images. At 3 billion parameters, it is small enough to run cheaply as a guardrail alongside larger generative systems, and it arrives under a permissive Apache 2.0 license on Hugging Face.

Unlike a chat model, Shieldstral is purpose-built to judge content rather than produce it. Its multimodal design means it can evaluate visual inputs as well as text, a capability that matters as image generation and vision-language systems become more common in production. Mistral positions the model as a content-moderation and safety tool, according to its announcement.

Why it matters

Safety classifiers are increasingly treated as infrastructure: teams deploy them at the input and output layers of an application to catch policy-violating prompts and responses. A few points stand out here:

  • Open weights, so developers can inspect, fine-tune, and self-host the classifier.
  • Multimodal coverage, extending moderation beyond text to images.
  • Small footprint at 3B parameters, keeping latency and cost low for a component that runs on every request.

The permissive license is notable in a category where safety tooling is often gated or API-only. By putting a vision-capable moderation model in the open, Mistral gives builders an alternative they can adapt to their own policies rather than relying on a hosted service. Details on supported categories and evaluation are available on the model's Hugging Face page.

Sources

  • mistralai/Shieldstral-1.0-3B

    Hugging Face

    Visit
  • Mistral's Shieldstral: 3B open-weights model for multimodal moderation

    Hacker News

    Visit
OlderinclusionAI ships LLaDA2.2-flash diffusion LLMinclusionAI · Text / LLM · 4 months agoNewerInternLM Previews 397B Vision-Language ModelInternlm · Vision-Language · 4 months ago

Get the model

Hugging FaceHacker News

Specs

Parameters3B
Context window262K tokens
Size7.7 GB
PrecisionBF16
ArchitectureMistral3ForConditionalGeneration
LicenseAPACHE-2.0
Downloads32.7K
Likes314

Can you run it?

Runs on a laptop — about 4.2 GB at 4-bit.

  • BF16 (as published)

    12 GB GPU (RTX 3060 / 4070) · 16 GB Mac

    9.6 GB
  • 8-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    6 GB
  • 4-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    4.2 GB
Your machine
GGUF builds
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMVision-Language

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