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**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.

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**Model Summary:** Granite-speech-3.2-8b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Granite-speech-3.2-8b uses a two-pass design, unlike integrated models that combine speech and language into a single pass. Initial calls to granite-speech-3.2-8b will transcribe audio files into text. To process the transcribed text using the underlying Granite language model, users must make a second call as each step must be explicitly initiated. The model was trained on a collection of public corpora comprising diverse datasets for ASR and AST as well as synthetic datasets tailored to support the speech translation task. Granite-speech-3.2 was trained by modality aligning granite-3.2-8b-instruct (https://huggingface.co/ibm-granite/granite-3.2-8b-instruct) to speech on publicly available open source corpora containing audio inputs and text targets. We evaluated granite-speech-3.2-8b alongside other speech-language models (SLMs) in the less than 8b parameter range as well as dedicated ASR and AST systems on standard benchmarks. The evaluation spanned multiple public benchmarks, with particular emphasis on English ASR tasks while also including AST for En-X translation.

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**Model Summary:** Granite-speech-3.3-2b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Granite-speech-3.3-2b uses a two-pass design, unlike integrated models that combine speech and language into a single pass. Initial calls to granite-speech-3.3-2b will transcribe audio files into text. To process the transcribed text using the underlying Granite language model, users must make a second call as each step must be explicitly initiated. The model was trained on a collection of public corpora comprising diverse datasets for ASR and AST as well as synthetic datasets tailored to support the speech translation task. Granite-speech-3.3-2b was trained by modality aligning granite-3.3-2b-instruct (https://huggingface.co/ibm-granite/granite-3.3-2b-instruct) to speech on publicly available open source corpora containing audio inputs and text targets. Compared to the initial release, revision 3.3.2 * supports multilingual speech inputs in English, French, German, Spanish and Portuguese, * provides transcription accuracy improvements for English ASR by using a deeper acoustic encoder and additional training data.

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语音识别
输入 音频→ 输出 文本
价格待核验多模态与渠道费用须分别核对
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**Model Summary:** Granite-speech-3.3-8b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Granite-speech-3.3-8b uses a two-pass design, unlike integrated models that combine speech and language into a single pass. Initial calls to granite-speech-3.3-8b will transcribe audio files into text. To process the transcribed text using the underlying Granite language model, users must make a second call as each step must be explicitly initiated. The model was trained on a collection of public corpora comprising diverse datasets for ASR and AST as well as synthetic datasets tailored to support the speech translation task. Granite-speech-3.3-8b was trained by modality aligning granite-3.3-8b-instruct (https://huggingface.co/ibm-granite/granite-3.3-8b-instruct) to speech on publicly available open source corpora containing audio inputs and text targets. We evaluated granite-speech-3.3-8b revision 3.3.2 alongside other speech-language models in the less than 8b parameter range as well as dedicated ASR and AST systems on standard benchmarks. The evaluation spanned multiple public benchmarks, with particular emphasis on English ASR tasks while also including multilingual ASR and AST for X-En and En-X translations.

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语音识别
输入 音频→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗
来源 / 文档 ↗

**Model Summary:** Granite Speech 4.1 2B is a compact and efficient speech-language model, specifically designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST) for English, French, German, Spanish, Portuguese and Japanese. The model was trained on 174,000 hours of audio from public corpora for ASR and AST as well as synthetic datasets tailored to support Japanese ASR, keyword-biased ASR and speech translation. Granite Speech 4.1 2B was trained by modality aligning an intermediate checkpoint of granite-4.0-1b-base to speech on publicly available open source corpora containing audio inputs and text targets. Compared to its predecessor granite-4.0-1b-speech, this model has the same parameter count (the new naming convention reflects actual instead of base LLM size) and provides additional capabilities and improvements: * Higher transcription accuracy for multilingual ASR due to a novel dual-head CTC encoder with both graphemic and BPE outputs and frame importance sampling to focus on informative parts of the audio * Punctuation and truecasing for ASR and AST in all languages (including German noun capitalization) with a simple prompt change * Better keyword list biasing capability for enhanced recognition of names, acronyms and technical jargon Two additional model variants explore different capabilities and inference optimization: * granite-speech-4.1-2b-plus adds speaker-attributed ASR and word-level time stamps * granite-speech-4.1-2b-nar introduces a novel non-autoregressive architecture for higher throughput

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语音识别
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价格待核验多模态与渠道费用须分别核对
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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