> ## 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>


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