`jina-embeddings-v5-text-nano` is the fifth generation of Jina AI's multilingual embedding models, released on February 18, 2026. For higher performance at a larger size, see jina-embeddings-v5-text-small. `jina-embeddings-v5-text-nano` scores 71.0 average on MTEB English v2 and 65.5 on MMTEB with only 239M parameters, matching or exceeding all other sub-500M embedding models including KaLM-mini-v2.5 (494M) and Gemma-300M (308M). Built on EuroBERT-210M and trained by combining embedding distillation from Qwen3-Embedding-4B with task-specific contrastive losses, it supports multilingual text up to 32K tokens and produces embeddings robust under truncation and binary quantization. | Feature | Value | | --- | --- | | Parameters | 239M | | Supported Tasks | `retrieval`, `text-matching`, `clustering`, `classification` | | Max Sequence Length | 8192 | | Embedding Dimension | 768 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768 | | Pooling Strategy | Last-token pooling | | Base Model | EuroBERT/EuroBERT-210m | For training details and evaluation results, see our technical report.
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`jina-embeddings-v5-text-nano-classification` is a compact, high-performance text embedding model designed for classification. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-small, for better performance at a bigger size. Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-nano-classification` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 239M | | Supported Tasks | `classification` | | Max Sequence Length | 8192 | | Embedding Dimension | 768 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768 | | Pooling Strategy | Last-token pooling | | Base Model | jinaai/jina-embeddings-v5-text-nano |
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`jina-embeddings-v5-text-nano-retrieval` is a compact, high-performance text embedding model designed for information retrieval. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-small, for better performance at a bigger size. Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-nano-retrieval` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 239M | | Supported Tasks | `retrieval` | | Max Sequence Length | 8192 | | Embedding Dimension | 768 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768 | | Pooling Strategy | Last-token pooling | | Base Model | jinaai/jina-embeddings-v5-text-nano |
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GGUF quantizations of jina-embeddings-v5-text-nano-retrieval using llama.cpp. A 239M parameter multilingual embedding model quantized for efficient inference. > [!IMPORTANT] > We highly recommend to first read this blog post for more technical details and customized llama.cpp build. `jina-embeddings-v5-text-nano-retrieval` is a task-specific embedding model for **retrieval**, part of the jina-embeddings-v5-text model family. | Feature | Value | | --- | --- | | Parameters | 239M | | Task | `retrieval` | | Embedding Dimension | 768 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768 | | Pooling Strategy | Last-token pooling | | Base Model | jina-embeddings-v5-text-nano |
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`jina-embeddings-v5-text-nano-text-matching` is a compact, high-performance text embedding model designed for text-matching. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-small, for better performance at a bigger size. Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-nano-text-matching` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 239M | | Supported Tasks | `text-matching` | | Max Sequence Length | 8192 | | Embedding Dimension | 768 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768 | | Pooling Strategy | Last-token pooling | | Base Model | jinaai/jina-embeddings-v5-text-nano |
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`jina-embeddings-v5-text-small` is the fifth generation of Jina AI's multilingual embedding models, released on February 18, 2026. For a lighter alternative, see jina-embeddings-v5-text-nano (239M parameters). `jina-embeddings-v5-text-small` scores 71.7 average on MTEB English v2 and 67.7 on MMTEB with 677M parameters, the highest among multilingual embedding models under 1B parameters. Built on Qwen3-0.6B-Base and trained by combining embedding distillation from Qwen3-Embedding-4B with task-specific contrastive losses, it supports 119+ languages with up to 32K tokens and produces embeddings robust under truncation and binary quantization. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-nano, a smaller model for resource-constrained use cases. | Feature | Value | | --- | --- | | Parameters | 677M | | Supported Tasks | `retrieval`, `text-matching`, `clustering`, `classification` | | Max Sequence Length | 32768 | | Embedding Dimension | 1024 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768, 1024 | | Pooling Strategy | Last-token pooling | | Base Model | Qwen/Qwen3-0.6B-Base |
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`jina-embeddings-v5-text-small-classification` is a compact, high-performance text embedding model designed for classification. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-nano, a smaller model for more resource-constrained use cases. Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-small-classification` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 677M | | Supported Tasks | `classification`| | Max Sequence Length | 32768 | | Embedding Dimension | 1024 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768, 1024 | | Pooling Strategy | Last-token pooling | | Base Model | jinaai/jina-embeddings-v5-text-small |
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`jina-embeddings-v5-text-small-clustering` is a compact, high-performance text embedding model designed for clustering. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-nano, a smaller model for more resource-constrained use cases. Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-small-clustering` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 677M | | Supported Tasks | `clustering`| | Max Sequence Length | 32768 | | Embedding Dimension | 1024 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768, 1024 | | Pooling Strategy | Last-token pooling | | Base Model | jinaai/jina-embeddings-v5-text-small |
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`jina-embeddings-v5-text-small-retrieval` is a compact, high-performance text embedding model designed for information retrieval. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-nano, a smaller model for more resource-constrained use cases. Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-small-retrieval` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 677M | | Supported Tasks | `retrieval`| | Max Sequence Length | 32768 | | Embedding Dimension | 1024 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768, 1024 | | Pooling Strategy | Last-token pooling | | Base Model | jinaai/jina-embeddings-v5-text-small |
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