jina-embedding-l-en-v1
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jina-embedding-s-en-v1
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查看详情 ↗jina-embedding-t-en-v1
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查看详情 ↗The easiest way to starting using `jina-embeddings-v2-base-code` is to use Jina AI's Embedding API. `jina-embeddings-v2-base-code` is an multilingual **embedding model** speaks **English and 30 widely used programming languages**. Same as other jina-embeddings-v2 series, it supports **8192** sequence length. `jina-embeddings-v2-base-code` is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone `jina-bert-v2-base-code` is pretrained on the github-code dataset. The model is further trained on Jina AI's collection of more than 150 millions of coding question answer and docstring source code pairs. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
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查看详情 ↗The easiest way to starting using `jina-embeddings-v2-base-de` is to use Jina AI's Embedding API. `jina-embeddings-v2-base-de` is a German/English bilingual text **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed German-English input without bias. Additionally, we provide the following embedding models: `jina-embeddings-v2-base-de` ist ein zweisprachiges **Text Embedding Modell** für Deutsch und Englisch, welches Texteingaben mit einer Länge von bis zu **8192 Token unterstützt**. Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT, welche mithilfe einer symmetrische Variante von ALiBi längere Eingabetexte erlaubt. Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert, gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren. Des Weiteren stellen wir folgende Embedding-Modelle bereit:
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查看详情 ↗The easiest way to starting using `jina-embeddings-v2-base-en` is to use Jina AI's Embedding API. `jina-embeddings-v2-base-en` is an English, monolingual **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone `jina-bert-v2-base-en` is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
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查看详情 ↗`jina-embeddings-v2-base-es` is a Spanish/English bilingual text **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Spanish-English input without bias. Additionally, we provide the following embedding models: `jina-embeddings-v2-base-es` es un modelo (embedding) de texto bilingüe Inglés/Español que admite una longitud de secuencia de 8192. Se basa en la arquitectura BERT (JinaBERT) que incorpora la variante bi-direccional simétrica de ALiBi para permitir una mayor longitud de secuencia. Hemos diseñado este modelo para un alto rendimiento en aplicaciones monolingües y bilingües, y está entrenando específicamente para admitir entradas mixtas de español e inglés sin sesgo. Adicionalmente, proporcionamos los siguientes modelos (embeddings): `mean pooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an `encode` function to deal with this.
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查看详情 ↗`jina-embeddings-v2-base-zh` is a Chinese/English bilingual text **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Chinese-English input without bias. Additionally, we provide the following embedding models: `jina-embeddings-v2-base-zh` 是支持中英双语的**文本向量**模型，它支持长达**8192字符**的文本编码。 该模型的研发基于BERT架构(JinaBERT)，JinaBERT是在BERT架构基础上的改进，首次将ALiBi应用到编码器架构中以支持更长的序列。 不同于以往的单语言/多语言向量模型，我们设计双语模型来更好的支持单语言（中搜中）以及跨语言（中搜英）文档检索。 除此之外，我们也提供其它向量模型: `mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an `encode` function to deal with this.
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查看详情 ↗The easiest way to starting using `jina-embeddings-v2-small-en` is to use Jina AI's Embedding API. `jina-embeddings-v2-small-en` is an English, monolingual **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone `jina-bert-v2-small-en` is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
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查看详情 ↗jina-embeddings-v3
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查看详情 ↗jina-embeddings-v3-hf
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查看详情 ↗jina-embeddings-v4
jinaai`jina-embeddings-v4` is a universal embedding model for multimodal and multilingual retrieval. The model is specially designed for complex document retrieval, including visually rich documents with charts, tables, and illustrations. Built on Qwen/Qwen2.5-VL-3B-Instruct, `jina-embeddings-v4` features: - **Unified embeddings** for text, images, and visual documents, supporting both dense (single-vector) and late-interaction (multi-vector) retrieval. - **Multilingual support** (30+ languages) and compatibility with a wide range of domains, including technical and visually complex documents. - **Task-specific adapters** for retrieval, text matching, and code-related tasks, which can be selected at inference time. - **Flexible embedding size**: dense embeddings are 2048 dimensions by default but can be truncated to as low as 128 with minimal performance loss.
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查看详情 ↗> [!IMPORTANT] > We highly recommend to first read this blog post for more technical details and customized llama.cpp build. `jina-embeddings-v4` is a cutting-edge universal embedding model for multimodal multilingual retrieval. It's based on `qwen2.5-vl-3b-instruct` with three LoRA adapters: `retrieval` (optimized for retrieval tasks), `text-matching` (optimized for sentence similarity tasks), and `code` (optimized for code retrieval tasks). It is also heavily trained for visual document retrieval and late-interaction style multi-vector output. We removed the visual components of `qwen2.5-vl` and merged all LoRA adapters back into the base language model. This results in three task-specific v4 models with 3.09B parameters, downsized from the original jina-embeddings-v4 3.75B parameters:
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查看详情 ↗> [!IMPORTANT] > We highly recommend to first read this blog post for more technical details and customized llama.cpp build. `jina-embeddings-v4` is a cutting-edge universal embedding model for multimodal multilingual retrieval. It's based on `qwen2.5-vl-3b-instruct` with three LoRA adapters: `retrieval` (optimized for retrieval tasks), `text-matching` (optimized for sentence similarity tasks), and `code` (optimized for code retrieval tasks). It is also heavily trained for visual document retrieval and late-interaction style multi-vector output. We removed the visual components of `qwen2.5-vl` and merged all LoRA adapters back into the base language model. This results in three task-specific v4 models with 3.09B parameters, downsized from the original jina-embeddings-v4 3.75B parameters:
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查看详情 ↗This repository hosts a vLLM-compatible version of `jina-embeddings-v4` with the **retrieval** adapter merged into the base `Qwen2.5-VL` weights. This architecture modification enables native compatibility with vLLM without requiring custom adapter-handling code.
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查看详情 ↗*Average score vs. parameter count across image (MIEB-Lite), video (MMEB-V), and audio (MAEB) benchmarks — `jina-v5-omni-nano` and `jina-v5-omni-small` define the open-weight frontier (Table 1 in the ArXiv report).* `jina-embeddings-v5-omni-nano` is a multimodal embedding model that accepts **text, images, video, and audio** and produces embeddings in a shared vector space aligned with the text-only `jinaai/jina-embeddings-v5-text-nano` — so you can index with text and query with any modality, no reindexing. For higher performance at a larger size, see `jinaai/jina-embeddings-v5-omni-small`. This is the **base** repository — it holds all task adapters (retrieval, classification, clustering, text-matching). For a single task, pre-merged task-specific variants are also available: - `jinaai/jina-embeddings-v5-omni-nano-retrieval` — query–document semantic search and RAG (raw-transformers users prepend `Query: ` / `Document: ` to text; sentence-transformers users call `encode_query()` / `encode_document()`). - `jinaai/jina-embeddings-v5-omni-nano-classification` — assigning labels via embedding similarity — zero-shot and few-shot classification across modalities. - `jinaai/jina-embeddings-v5-omni-nano-clustering` — grouping semantically similar items — topic discovery, deduplication, exploratory analysis. - `jinaai/jina-embeddings-v5-omni-nano-text-matching` — symmetric pairwise similarity scoring — STS, paraphrase and near-duplicate detection.
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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。