> ## Documentation Index
> Fetch the complete documentation index at: https://docs.qualifire.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# SLM Judges

> Purpose-built Small Language Models for real-time AI evaluation, each fine-tuned for a specific task.

## Why Small Language Models?

General-purpose LLMs are expensive, slow, and not optimized for evaluation tasks. Qualifire's SLM judges solve this by providing purpose-built models that are fine-tuned for specific evaluation tasks — delivering higher accuracy at a fraction of the cost and latency.

<CardGroup cols={3}>
  <Card title="99.6% Faster" icon="bolt">
    \~100ms latency vs seconds for general-purpose LLMs
  </Card>

  <Card title="97% Cheaper" icon="coins">
    $0.01/M tokens vs $1.25–\$3.00 for frontier LLMs
  </Card>

  <Card title="Higher Accuracy" icon="bullseye">
    Fine-tuned models outperform general-purpose LLMs on targeted evaluation tasks
  </Card>
</CardGroup>

***

## Omni — Multi-Task Evaluation Model

Omni is Qualifire's flagship 14B parameter model, capable of handling multiple evaluation tasks in a single inference call. It delivers frontier-model accuracy at SLM speed and cost.

| Property       | Value                                                                                                                       |
| -------------- | --------------------------------------------------------------------------------------------------------------------------- |
| **Parameters** | 14B                                                                                                                         |
| **Latency**    | \~100ms                                                                                                                     |
| **Cost**       | \$0.01 / 1M tokens                                                                                                          |
| **Tasks**      | Prompt Injection Detection, Safety, Grounding, Hallucination Detection, Policy Enforcement, Tool Use Quality, Topic Scoping |

### Benchmarks

Omni matches or exceeds the performance of frontier models like GPT-5, Claude Sonnet 4.5, and Gemini 3 Pro across evaluation tasks — at 60x lower latency and 125–300x lower cost.

<Tabs>
  <Tab title="Prompt Injection">
    Detects prompt injection and jailbreak attempts targeting your AI system.

    | Model                 | Creator       | Avg F1    | Latency      | Cost/1M tokens |
    | --------------------- | ------------- | --------- | ------------ | -------------- |
    | **Sentinel v2**       | **Qualifire** | **0.957** | **\~0.038s** | **\$0.005**    |
    | **Omni**              | **Qualifire** | **0.936** | **\~0.1s**   | **\$0.01**     |
    | Qwen3Guard 8B         | Qwen          | 0.882     | \~0.76s      | —              |
    | Qwen3Guard 4B         | Qwen          | 0.877     | \~0.48s      | —              |
    | Qwen3Guard 0.6B       | Qwen          | 0.858     | \~0.27s      | —              |
    | GPT OSS Safeguard 20B | OpenAI        | 0.803     | \~10s        | —              |
    | Llama Guard 3 8B      | Meta          | 0.628     | \~0.21s      | —              |
    | Llama Guard 3 1B      | Meta          | 0.475     | \~0.09s      | —              |
  </Tab>

  <Tab title="Hallucination Detection">
    Identifies when AI generates information not supported by the provided context.

    | Model                | Creator       | Avg Accuracy | Latency    | Cost/1M tokens |
    | -------------------- | ------------- | ------------ | ---------- | -------------- |
    | Gemini 3 Pro         | Google        | 0.901        | \~6s       | \$2.00         |
    | Claude Sonnet 4.5    | Anthropic     | 0.898        | \~8s       | \$3.00         |
    | **Omni**             | **Qualifire** | **0.893**    | **\~0.1s** | **\$0.01**     |
    | GPT-5.1              | OpenAI        | 0.883        | \~3.5s     | \$1.25         |
    | GPT-5                | OpenAI        | 0.872        | \~2.5s     | \$1.25         |
    | Patronus Lynx 8B     | PatronusAI    | 0.810        | —          | —              |
    | Quotient Detections  | QuotientAI    | 0.807        | —          | —              |
    | Bespoke Minicheck 7B | Minicheck     | 0.791        | —          | —              |
    | Ragas Faithfulness   | Ragas         | 0.781        | —          | —              |
    | Vectara HHEM-2.1     | Vectara       | 0.780        | —          | —              |
    | Azure Groundedness   | Microsoft     | 0.778        | —          | —              |
  </Tab>

  <Tab title="Grounding">
    Verifies that responses are anchored in provided reference material.

    | Model                 | Creator       | Avg Score | Latency    | Cost/1M tokens |
    | --------------------- | ------------- | --------- | ---------- | -------------- |
    | Claude Sonnet 4.5     | Anthropic     | 82.56     | \~7.5s     | \$3.00         |
    | **Omni**              | **Qualifire** | **82.48** | **\~0.1s** | **\$0.01**     |
    | Gemini 3 Pro          | Google        | 82.42     | \~6s       | \$2.00         |
    | Gemini 2.5 Flash      | Google        | 80.59     | \~2.5s     | \$0.30         |
    | GPT-5.1               | OpenAI        | 79.62     | \~1.2s     | \$1.25         |
    | GPT OSS Safeguard 20B | OpenAI        | 79.50     | \~10s      | —              |
    | Paladin Mini (3.8B)   | Qualifire     | 79.31     | \~0.064s   | \$0.01         |
    | Bespoke MiniCheck 7B  | MiniCheck     | 77.87     | \~0.17s    | —              |
    | GPT-5                 | OpenAI        | 77.47     | \~1.2s     | \$1.25         |
  </Tab>

  <Tab title="Policy Enforcement">
    Evaluates compliance with custom-defined policies and assertions.

    | Model             | Avg Accuracy | Latency    | Cost/1M tokens |
    | ----------------- | ------------ | ---------- | -------------- |
    | Gemini 3 Pro      | 0.895        | \~5.5s     | \$2.00         |
    | **Omni**          | **0.871**    | **\~0.1s** | **\$0.01**     |
    | Claude Sonnet 4.5 | 0.870        | \~7s       | \$3.00         |
    | GPT-5             | 0.806        | \~1s       | \$1.25         |
    | GPT-5.1           | 0.798        | \~1s       | \$1.25         |
  </Tab>

  <Tab title="Tool Use Quality">
    Evaluates whether AI agents correctly select and invoke tools.

    | Model             | Avg Binary Acc | Latency    | Cost/1M tokens |
    | ----------------- | -------------- | ---------- | -------------- |
    | Gemini 3 Pro      | 0.939          | \~5.5s     | \$2.00         |
    | Claude Sonnet 4.5 | 0.936          | \~10s      | \$3.00         |
    | **Omni**          | **0.932**      | **\~0.1s** | **\$0.01**     |
    | GPT-5             | 0.930          | \~1.2s     | \$1.25         |
    | GPT-5.1           | 0.912          | \~1.2s     | \$1.25         |
    | Gemini 2.5 Flash  | 0.858          | \~4.9s     | \$0.30         |
  </Tab>

  <Tab title="Topic Scoping">
    Detects when conversations drift outside intended scope.

    | Model             | Avg Accuracy | Latency    | Cost/1M tokens |
    | ----------------- | ------------ | ---------- | -------------- |
    | **Omni**          | **0.972**    | **\~0.1s** | **\$0.01**     |
    | Claude Sonnet 4.5 | 0.966        | \~6s       | \$3.00         |
    | Gemini 3 Flash    | 0.963        | \~6s       | \$0.50         |
    | GPT-5.2           | 0.937        | \~6s       | \$1.75         |
  </Tab>

  <Tab title="Safety">
    Filters harmful content across multiple safety categories.

    | Model                 | Creator       | Params  | Avg F1    | Latency      | Cost/1M tokens |
    | --------------------- | ------------- | ------- | --------- | ------------ | -------------- |
    | **Cleric v2 Mini**    | **Qualifire** | 0.6B    | 0.886     | \~0.038s     | \$0.01         |
    | GPT OSS Safeguard 20B | OpenAI        | 20B     | 0.867     | \~10s        | —              |
    | **Omni**              | **Qualifire** | **14B** | **0.857** | **\~0.087s** | **\$0.01**     |
    | Qwen3Guard 8B         | Qwen          | 8B      | 0.811     | \~0.76s      | —              |
    | Llama Guard 3 8B      | Meta          | 8B      | 0.785     | \~0.21s      | —              |
  </Tab>
</Tabs>

***

## Specialist Models

In addition to Omni, Qualifire provides fine-tuned specialist models optimized for single tasks where maximum accuracy or minimal latency is required.

<AccordionGroup>
  <Accordion title="Sentinel — Prompt Injection Detection" icon="user-secret">
    Detects prompt injection and jailbreak attempts that try to manipulate your AI into ignoring its instructions.

    | Property       | Value               |
    | -------------- | ------------------- |
    | **Avg F1**     | 0.957               |
    | **Latency**    | \~38ms              |
    | **Parameters** | 596M                |
    | **Cost**       | \$0.005 / 1M tokens |

    **Benchmark comparison (Prompt Injection):**

    | Model                 | Creator       | Avg F1    | Latency      | Cost/1M tokens |
    | --------------------- | ------------- | --------- | ------------ | -------------- |
    | **Sentinel v2**       | **Qualifire** | **0.957** | **\~0.038s** | **\$0.005**    |
    | Qwen3Guard 8B         | Qwen          | 0.882     | \~0.76s      | —              |
    | Qwen3Guard 4B         | Qwen          | 0.877     | \~0.48s      | —              |
    | Qwen3Guard 0.6B       | Qwen          | 0.858     | \~0.27s      | —              |
    | GPT OSS Safeguard 20B | OpenAI        | 0.803     | \~10s        | —              |
    | Llama Guard 3 8B      | Meta          | 0.628     | \~0.21s      | —              |
  </Accordion>

  <Accordion title="Cleric — Content Safety Moderation" icon="shield-exclamation">
    Evaluates content for harmful or inappropriate material across multiple safety categories (dangerous content, harassment, hate speech, sexually explicit).

    | Property       | Value              |
    | -------------- | ------------------ |
    | **Avg F1**     | 0.886              |
    | **Latency**    | \~38ms             |
    | **Parameters** | 0.6B               |
    | **Cost**       | \$0.01 / 1M tokens |
  </Accordion>

  <Accordion title="Paladin — Context Grounding" icon="crosshairs">
    Verifies that responses are accurately grounded in provided reference material.

    | Property       | Value               |
    | -------------- | ------------------- |
    | **Avg Score**  | 79.31               |
    | **Latency**    | \~64ms              |
    | **Parameters** | 3.8B                |
    | **Cost**       | \$0.016 / 1M tokens |

    Paladin Mini is optimized for speed-critical applications. For higher accuracy, use Omni.
  </Accordion>

  <Accordion title="Ranger — Tool Use Quality" icon="wrench">
    Evaluates MCP tool selection quality for AI agents — correct tool selection, parameters, and values.

    | Property    | Value              |
    | ----------- | ------------------ |
    | **F1**      | 0.945              |
    | **Latency** | \~90ms             |
    | **Cost**    | \$0.01 / 1M tokens |
  </Accordion>

  <Accordion title="Sage — Hallucination Detection" icon="brain-circuit">
    Uses reasoning to identify inaccurate outputs and logic faults.

    | Property    | Value              |
    | ----------- | ------------------ |
    | **F1**      | 0.834              |
    | **Latency** | \~250ms            |
    | **Cost**    | \$0.01 / 1M tokens |
  </Accordion>

  <Accordion title="Hunter — PII Detection" icon="user-check">
    Identifies and flags personally identifiable information to prevent data leaks.

    | Property    | Value              |
    | ----------- | ------------------ |
    | **F1**      | 0.834              |
    | **Latency** | \~40ms             |
    | **Cost**    | \$0.01 / 1M tokens |
  </Accordion>

  <Accordion title="Magistrate — Policy Enforcement" icon="gavel">
    Enforces custom rules, standards, and policies using natural language assertions.

    | Property    | Value              |
    | ----------- | ------------------ |
    | **F1**      | 0.835              |
    | **Latency** | \~100ms            |
    | **Cost**    | \$0.01 / 1M tokens |
  </Accordion>
</AccordionGroup>

***

## Deployment Options

Qualifire SLMs can be deployed in the way that fits your infrastructure and compliance requirements.

<CardGroup cols={3}>
  <Card title="SaaS" icon="cloud">
    Fully managed by Qualifire. No infrastructure to maintain — just send API requests.
  </Card>

  <Card title="Your Cloud" icon="server">
    Deploy in your own cloud environment (AWS, GCP, Azure) for data residency and compliance needs.
  </Card>

  <Card title="On-Premise" icon="building">
    Run entirely on your infrastructure for maximum control and air-gapped environments.
  </Card>
</CardGroup>

<Info>
  Qualifire models can be fine-tuned for your specific domain and policies. Contact our team to discuss custom model training for your use case.
</Info>

***

## Getting Started

<CardGroup cols={2}>
  <Card title="Evaluations" icon="magnifying-glass" href="/essentials/evaluations">
    Learn how to use these models through Qualifire's evaluation system
  </Card>

  <Card title="SDK" icon="code" href="/essentials/sdk">
    Integrate SLM judges into your application with the Qualifire SDK
  </Card>
</CardGroup>