Shieldstral
3B open-weights multimodal safety classifier that scores text and images against policies you write in plain English, on a single 16GB GPU.
Updated 2026-08-05
Yes. Shieldstral is free to use.
Listed pricing: Free open weights (Apache 2.0) — self-host only.
Overview
Shieldstral is Mistral's content-safety classifier: a 3-billion-parameter multimodal model that takes a piece of text or an image, plus a moderation policy written in ordinary English, and returns a verdict on whether the content violates that policy. It shipped on August 4, 2026 as Apache 2.0 open weights with an accompanying technical report, and Mistral puts the hardware floor at a single 16GB GPU. There is no hosted API SKU at launch — you run it yourself or you don't run it.
The design choice that separates it from the moderation endpoints most teams currently call is where the policy lives. OpenAI's moderation API and similar services score against a category taxonomy fixed in the weights; changing what counts as a violation means filing a request with a vendor or fine-tuning your own model. Shieldstral reads the rule at inference time, so a children's education app and a security research forum can load identical weights and enforce opposite definitions of "instructions for making something dangerous." Changing enforcement becomes editing a string. The flip side is that the policy text rides along in every request, so prompt length grows with your rule count, and small rewordings can shift classifier behaviour in ways nobody predicts from reading the diff — your policy file now needs the same review discipline as the code around it.
The headline benchmark claim is that Shieldstral matches or beats models up to seven times its size across text safety, refusal detection, policy adaptability and multimodal tests. Classification is exactly the workload where small specialised models close the gap on general-purpose giants, so the shape is plausible, but all four numbers were measured by Mistral on a task mix Mistral assembled, and "policy adaptability" is a dimension introduced in the same document as the model built to win it. Treat that one as marketing until someone outside the lab reproduces it. Worth it if you're already running your own inference and want user content to stay inside your process; skip it if you handle a few hundred messages a day, where renting a per-call moderation API beats paying rent on an idle 16GB card.
Is Shieldstral free?
Yes. Shieldstral is free to use.
What the free tier covers: Everything is free: the weights are Apache 2.0 with no fee, no seat limit and no usage cap. There is no hosted API or managed endpoint at launch, so the real cost is compute — a 16GB-class GPU running inline with your own model. Check Mistral's site for any later hosted SKU.
Listed pricing: Free open weights (Apache 2.0) — self-host only.
Pricing on this page has not been re-verified. The entry was last edited on , and no separate pricing check has been run since. Treat the figures as a record of what was published then and confirm on the official site.
Shieldstral pricing
| Plan | Price | What's included |
|---|---|---|
| Open weights | Free (Apache 2.0) | Full model weights, commercial use, modification and redistribution permitted, explicit patent grant, no user or request ceiling. Self-hosting only — you supply the GPU. |
Full model weights, commercial use, modification and redistribution permitted, explicit patent grant, no user or request ceiling. Self-hosting only — you supply the GPU.
Pricing on this page has not been re-verified. The entry was last edited on , and no separate pricing check has been run since. Treat the figures as a record of what was published then and confirm on the official site.
Is Shieldstral worth it?
You can test that on the free tier before paying anything. The recorded trade-offs are listed below, and any one of them can settle the question on its own.
The 8.2/10 AI Score is an editorial read of published capability, price and shipping pace. Nobody here has hands-on hours with Shieldstral. How we verify.
Worth it if
The strengths recorded against this entry.
- Apache 2.0 with an explicit patent grant — commercial use, modification and redistribution with nothing to sign
- Moderation policy is supplied as text at inference time, so changing enforcement is a string edit rather than a fine-tune
- Scores text and images in one 3B model instead of requiring a separate vision moderation path
- Runs on a single 16GB GPU per Mistral's stated floor, keeping user content off any third-party endpoint
- Attractive for EU teams and regulated products where data residency rules out a US moderation API
Not worth it if
Any one of these blocks your use case.
- Every published benchmark is Mistral's own, on a task mix Mistral assembled — no independent reproduction exists a day after launch
- No hosted API at launch: you own the GPU, the scaling and the on-call, which prices out low-volume products
- Runs inline, so its latency lands on the critical path — screening both input and output means two extra passes per turn
- Mistral has not published the numeric precision its 16GB floor assumes, and a plain-text policy is an unversioned behavioural surface that can drift without a code review
Key features
Policy-as-prompt classification
Moderation rules are supplied as plain-English text at inference time rather than baked into the weights as a fixed category list. Redefining a violation is a config edit, not a fine-tune or a vendor ticket.
Multimodal scoring
Screens images as well as text in a single 3B model, so user-uploaded pictures and generated visuals go through the same classifier as chat messages instead of a separate vision pipeline.
Apache 2.0 open weights
Commercial use, modification and redistribution are permitted with an explicit patent grant and no user-count ceiling — a step beyond the community licences that most open-weight safety models ship under.
Single-GPU deployment
Mistral states a 16GB GPU floor, which puts it on one consumer-class card next to your own model. User content never leaves your infrastructure for a third-party moderation endpoint.
How it compares
| Tool | Best for | Pricing | Score |
|---|---|---|---|
| Shieldstral | — | Free open weights (Apache 2.0) — self-host only | 8.2/10 |
| Perplexity AI vs Perplexity AI → | Knowledge workers and researchers who want cited, synthesized answers instead of a list of links to click through. | Freemium | 9.4/10 |
| NotebookLM vs NotebookLM → | Students, researchers, and professionals who need answers grounded strictly in the specific documents they upload. | Free | 9.1/10 |
| Inkling-Small | — | Free — open weights download; Tinker Playground access | 8.8/10 |
Compare head-to-head
Comparison explorer Put Shieldstral up against any three tools Opens with Shieldstral already loaded. Add up to three more from the full index and read pricing, features, pros and cons in one table. →Related reading
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