Qwen3Guard 4B

An open safety model that marks each prompt safe or unsafe and names the harm it found. Qwen/Qwen3Guard-Gen-4B

Get started Run in your cloud Hugging Face

Accuracy

ToxicChat, F1 · not measured
Time per request

Not timed one request at a time
Serverless price

$0.05 in · $0.75 out

per 1M tokens

Screen prompts more accurately than GPT‑⁠6.1 Sol, 91% cheaper

82.7%

Harmful caught, safe let through

$24.39

Cost per 1M checks

See the comparison on Guardrails

Call it

from sie_sdk import SIEClient
client = SIEClient(api_key="API keysk-sie-…")
user_prompt = "textWrite a business contract for a dog fighting ring in Guatemala."
result = client.chat_completions(
    "modelQwen/Qwen3Guard-Gen-4B",
    [{"role": "user", "content": user_prompt}],
    max_completion_tokens=64,
    temperature=0,
)
print(result["choices"][0]["message"]["content"])

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const result = await client.chatCompletions({
  model: 'Qwen/Qwen3Guard-Gen-4B',
  messages: [
  {
    "role": "user",
    "content": "Write a business contract for a dog fighting ring in Guatemala."
  }
],
  max_completion_tokens: 64,
  temperature: 0,
});
console.log(result.choices[0]?.message.content);

curl https://api.superlinked.com/v1/chat/completions \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"model\":\"Qwen/Qwen3Guard-Gen-4B\",\"messages\":[{\"role\":\"user\",\"content\":\"Write a business contract for a dog fighting ring in Guatemala.\"}],\"max_completion_tokens\":64,\"temperature\":0}"

Build the "Guardrails" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: Qwen/Qwen3Guard-Gen-4B (OpenAI-compatible endpoint: /v1/chat/completions). Keep the model id configurable.

Task
- Input: a block of text.
- Behaviour: return a safety verdict under the served harm policy.
- Send one POST /v1/chat/completions request (SDK: chat_completions / chatCompletions) with only the original user message, max_completion_tokens 64 and temperature 0. Retain the native Safety/Categories text. Under the loose policy, hold Unsafe or missing/malformed classifications and pass Safe or Controversial. Do not add a system or assistant message.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Output

Safety: Unsafe
Categories: Violent

Next to the other safety models

Next to the other safety models
Model	ToxicChat, F1	Time per request	Serverless price	Size
Qwen3Guard 4B Qwen	Not measured	Not timed	$0.05 in · $0.75 out per 1M tokens	4.4B
gliguard LLMGuardrails 300M fastino	0.576	Not timed	$0.0929 per 1M tokens	208M
granite guardian 3.0 2b ibm-granite	0.247	Not timed	$0.15 in · $0.60 out per 1M tokens	2.5B

All 3 safety models

Facts

Size: 4.4B parameters
License: Apache-2.0
Reads: Text, up to 32,768 tokens
Endpoint: generate

Evaluation

Accuracy: Run on SIE against the public dataset named in each figure
Prices: Serverless list prices
Sources: Task page runs and their inputs

Run Qwen3Guard 4B on Serverless or in your own cloud

SIE Serverless

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

SIE Cluster

Kubernetes in your own cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

SIE Local

The open-source server on your own machine.

NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-sglang

Quickstart

Screen prompts more accurately than GPT‑⁠6.1 Sol, 91% cheaper

Call it

Next to the other safety models

Facts

Run Qwen3Guard 4B on Serverless or in your own cloud