GODEL-v1_1-large-seq2seq
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85003
graphcodebert-base
MicrosoftGraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum sequence length for the model is 512. The model is trained on the CodeSearchNet dataset, which includes 2.3M functions with document pairs for six programming languages. More details can be found in the paper by Guo et. al. **Disclaimer:** The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face community members.
译文排队中 · 当前显示来源原文
输入 文本→ 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 ↗Graphormer
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗GridSFM_Open
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗GRIN-MoE
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 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 ↗GUI-Actor-2B-Qwen2-VL
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 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 ↗GUI-Actor-3B-Qwen2.5-VL
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 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 ↗GUI-Actor-7B-Qwen2.5-VL
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 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 ↗GUI-Actor-7B-Qwen2-VL
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 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 ↗GUI-Actor-Verifier-2B
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 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 ↗HARC
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 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 ↗HARC-Llama-3.1-8B-Instruct
Microsoft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。
输入 文本→ 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 ↗HARC-Qwen2.5-7B-Instruct
Microsoft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。
输入 文本→ 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 ↗harrier-oss-v1-0.6b
Microsoftharrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to **retrieval**, **clustering**, **semantic similarity**, **classification**, **bitext mining**, and **reranking**. The models achieve state-of-the-art results on the Multilingual MTEB v2 benchmark as of the release date. All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 向量
价格待核验多模态与渠道费用须分别核对
查看详情 ↗harrier-oss-v1-270m
Microsoftharrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to **retrieval**, **clustering**, **semantic similarity**, **classification**, **bitext mining**, and **reranking**. The models achieve state-of-the-art results on the Multilingual MTEB v2 benchmark as of the release date. All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 向量
价格待核验多模态与渠道费用须分别核对
查看详情 ↗harrier-oss-v1-27b
Microsoftharrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to **retrieval**, **clustering**, **semantic similarity**, **classification**, **bitext mining**, and **reranking**. The models achieve state-of-the-art results on the Multilingual MTEB v2 benchmark as of the release date. All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Have a look at config_sentence_transformers.json for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `bitext_query`. You can also use a custom instruction directly via e.g. `model.encode(queries, prompt="Instruct: Retrieve semantically similar text\nQuery: ")`.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 向量
价格待核验多模态与渠道费用须分别核对
查看详情 ↗InfoAgent
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗infoxlm-base
Microsoft**InfoXLM** (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
译文排队中 · 当前显示来源原文
输入 文本→ 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 ↗infoxlm-large
Microsoft**InfoXLM** (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
译文排队中 · 当前显示来源原文
输入 文本→ 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 ↗kosmos-2.5
MicrosoftKosmos-2.5 is a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared decoder-only auto-regressive Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models. Since this is a generative model, there is a risk of **hallucination** during the generation process, and it **CAN NOT** guarantee the accuracy of all OCR/Markdown results in the images.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 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 ↗kosmos-2.5-chat
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 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 ↗kosmos-2-patch14-224
MicrosoftThis Hub repository contains a HuggingFace's `transformers` implementation of the original Kosmos-2 model from Microsoft. This model is capable of performing different tasks through changing the prompts. Once you have the `entities`, you can use the following helper function to draw their bounding bboxes on the image:
译文排队中 · 当前显示来源原文
输入 图像→ 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 ↗latent-zoning-networks
Microsoft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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗layoutlm-base-cased
MicrosoftLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: LayoutLM: Pre-training of Text and Layout for Document Image Understanding Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, KDD 2020 Note that LayoutLM-Cased requires a different tokenizer, based on RobertaTokenizer. You can initialize it as follows:
译文排队中 · 当前显示来源原文
输入 待核验→ 输出 待核验
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。