llm-jp-3-7.2b-instruct3
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85001
llm-jp-3-980m
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查看详情 ↗llm-jp-3-980m-instruct3
llm-jp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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查看详情 ↗LLM-jp-4.1 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. This repository provides the **llm-jp-4.1-32b-a3b-thinking** model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to: - LLM-jp-4.1 Models Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning.
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查看详情 ↗llm-jp-4.1-33b-thinking
llm-jp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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗LLM-jp-4.1 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. This repository provides the **llm-jp-4.1-33b-thinking** model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to: - LLM-jp-4.1 Models Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning.
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查看详情 ↗llm-jp-4.1-8b-thinking
llm-jpLLM-jp-4.1 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. This repository provides the **llm-jp-4.1-8b-thinking** model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to: - LLM-jp-4.1 Models Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning.
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查看详情 ↗LLM-jp-4.1 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. This repository provides the **llm-jp-4.1-8b-thinking** model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to: - LLM-jp-4.1 Models Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning.
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查看详情 ↗llm-jp-4-33b-base
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查看详情 ↗llm-jp-4-33b-thinking
llm-jp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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查看详情 ↗LLM-jp-4 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. This repository provides the **llm-jp-4-33b-thinking-gguf**. For an overview of the LLM-jp-4 models across different parameter sizes, please refer to: - LLM-jp-4 Models Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning. > [!NOTE] > While the **thinking** variants are trained with both SFT and DPO, this **instruct** model is trained using SFT only, without DPO.
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查看详情 ↗llm-jp-4-8b-base
llm-jpLLM-jp-4 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. This repository provides the **llm-jp-4-8b-base** model. For an overview of the LLM-jp-4 models across different parameter sizes, please refer to: - LLM-jp-4 Models Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning.
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查看详情 ↗llm-jp-4-8b-instruct
llm-jpLLM-jp-4 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. This repository provides the **llm-jp-4-8b-instruct** For an overview of the LLM-jp-4 models across different parameter sizes, please refer to: - LLM-jp-4 Models Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning. > [!NOTE] > While the **thinking** variants are trained with both SFT and DPO, this **instruct** model is trained using SFT only, without DPO.
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查看详情 ↗llm-jp-4-8b-thinking
llm-jpLLM-jp-4 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. This repository provides the **llm-jp-4-8b-thinking** model. For an overview of the LLM-jp-4 models across different parameter sizes, please refer to: - LLM-jp-4 Models Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning.
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查看详情 ↗llm-jp-4-vl-9b-beta
llm-jp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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查看详情 ↗llm-jp-modernbert-base
llm-jpThis model is based on the modernBERT-base architecture with llm-jp-tokenizer. It was trained using the Japanese subset (3.4TB) of the llm-jp-corpus v4 and supports a max sequence length of 8192. For detailed information on the training methods, evaluation, and analysis results, please visit at llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length This model was trained with a max_seq_len of 1024 in stage 1, and then with a max_seq_len of 8192 in stage 2.
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查看详情 ↗llm-jp-moshi-v1
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查看详情 ↗robust-llama3-8b-instruct
LLM-LAT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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查看详情 ↗Fabliq-8B-Agent-FromBase-Reasoning
LLM-OS-Models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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查看详情 ↗Fabliq-8B-Agent-Reasoning
LLM-OS-Models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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
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
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
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。