MODEL PROFILE

granite-guardian-4.1-8b

ibm-granite · granite-guardian-4.1-8b

**Granite Guardian 4.1 8B** introduces improved **Bring Your Own Criteria (BYOC)** support, enabling users to define arbitrary judging criteria beyond the pre-baked safety and hallucination detectors. The model can now faithfully evaluate complex, multi-part requirements such as formatting rules, length constraints, and domain-specific instructions. Key improvements over Granite Guardian 3.3: - **BYOC capability**: Large gains on instruction-following benchmarks. For example, IFEval multi-constraint BAcc improves from 0.458 to 0.844 (no-think), InfoBench (Human) from 0.535 to 0.706, InfoBench (GPT-4) from 0.585 to 0.726. - **Best-of-N reward model**: When used as a reward model for best-of-N selection on the verifiable tasks in the JETTS benchmark, Granite Guardian 4.1 8B achieves an overall score of 70.29, outperforming all tested reward models up to 70B parameters. - **Hybrid thinking**: Supports both thinking mode (with detailed reasoning traces) and non-thinking mode (low-latency yes/no judgements). - **Function calling**: Stronger hallucination detection in agentic workflows: BAcc improves from 0.74 to 0.79 (no-think). - **Maintained safety and groundedness**: Competitive with prior releases on OOD safety (F1 0.79 no-think) and RAG groundedness (Avg BAcc 0.76 think). **Granite Guardian 4.1 8B** is a specialized safety model fine-tuned from ibm-granite/granite-4.1-8b, designed to judge if the input prompts and the output responses of an LLM-based system meet specified criteria. The model comes pre-baked with certain criteria including but not limited to: jailbreak attempts, profanity, and hallucinations related to tool calls and retrieval augmented generation in agent-based systems. Additionally, the model also allows users to bring their own criteria and tailor the judging behavior to specific use cases.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:15:51
OVERVIEW

Model overview

**Granite Guardian 4.1 8B** introduces improved **Bring Your Own Criteria (BYOC)** support, enabling users to define arbitrary judging criteria beyond the pre-baked safety and hallucination detectors. The model can now faithfully evaluate complex, multi-part requirements such as formatting rules, length constraints, and domain-specific instructions. Key improvements over Granite Guardian 3.3: - **BYOC capability**: Large gains on instruction-following benchmarks. For example, IFEval multi-constraint BAcc improves from 0.458 to 0.844 (no-think), InfoBench (Human) from 0.535 to 0.706, InfoBench (GPT-4) from 0.585 to 0.726. - **Best-of-N reward model**: When used as a reward model for best-of-N selection on the verifiable tasks in the JETTS benchmark, Granite Guardian 4.1 8B achieves an overall score of 70.29, outperforming all tested reward models up to 70B parameters. - **Hybrid thinking**: Supports both thinking mode (with detailed reasoning traces) and non-thinking mode (low-latency yes/no judgements). - **Function calling**: Stronger hallucination detection in agentic workflows: BAcc improves from 0.74 to 0.79 (no-think). - **Maintained safety and groundedness**: Competitive with prior releases on OOD safety (F1 0.79 no-think) and RAG groundedness (Avg BAcc 0.76 think). **Granite Guardian 4.1 8B** is a specialized safety model fine-tuned from ibm-granite/granite-4.1-8b, designed to judge if the input prompts and the output responses of an LLM-based system meet specified criteria. The model comes pre-baked with certain criteria including but not limited to: jailbreak attempts, profanity, and hallucinations related to tool calls and retrieval augmented generation in agent-based systems. Additionally, the model also allows users to bring their own criteria and tailor the judging behavior to specific use cases.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。