从语言到影像，从开源到 API。读懂模型能力、比较真实成本，
为你的下一个想法，找到合适的智能引擎。
ONNX port of sentence-transformers/all-MiniLM-L6-v2 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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ONNX port of sentence-transformers/all-MiniLM-L6-v2 adjusted to return attention weights. This model is intended to be used for BM42 searches. Here's an example of performing inference using the model with FastEmbed.
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Quantized ONNX port of BAAI/bge-base-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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ONNX port of BAAI/bge-large-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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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。
Quantized ONNX port of BAAI/bge-small-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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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。
Repository with files to perform BM25 searches with FastEmbed. > Note: This model is supposed to be used with Qdrant. Vectors have to be configured with Modifier.IDF.
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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。
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
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
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
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
It creates 4-dimensional embeddings for each word stem, capturing the word's meaning. These meaning embeddings are combined into a bag-of-words (BoW) representation of the input text. The final sparse representation is calculated by weighting each word using the BM25 scoring formula. In the case of a word's absence in the miniCOIL vocabulary, word weight in sparse representation is purely based on the BM25 score. > Note: This model was designed with Qdrant's specifics in mind; miniCOIL sparse vectors in Qdrant have to be configured with Modifier.IDF. Otherwise, you'll have to personally calculate & scale the produced sparse representations by the IDF part of the BM25 formula.
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ONNX port of intfloat/multilingual-e5-large for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
译文排队中 · 当前显示来源原文
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
Quantized ONNX port of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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This repository contains an ONNX export of `Qwen/Qwen3-Embedding-0.6B`, converted by Qdrant for inference with FastEmbed. It encodes text into 1024-dimensional vectors, supports over 100 languages, and accepts up to 32768 input tokens. Embeddings are produced by taking the hidden state of the last non-padding token and L2-normalizing it, so the similarity score is the dot product of two embeddings. The quantized file stores its weights as 8-bit `MatMulNBits` and therefore requires `onnxruntime>=1.23`. Earlier versions only implement the 4-bit kernel and will fail to load it.
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This model is intended to be used for image classification and similarity searches. You can find the ONNX port implementation here Here's an example of performing inference using the model with FastEmbed.
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ONNX port of prithivida/Splade_PP_en_v1 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
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。
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。
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。
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。
