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.

Elastic Inference Service | ArXiv | Release Note | Blog

Model Overview

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

Training and Evaluation

For training details and evaluation results, see our technical report.

Usage

Requirements

The following Python packages are required:

transformers>=4.57.0
torch>=2.8.0
peft>=0.15.2

Optional / Recommended

flash-attention: Installing flash-attention is recommended for improved inference speed and efficiency, but not mandatory.
sentence-transformers: If you want to use the model via the sentence-transformers interface, install this package as well.

via Elastic Inference Service

The fastest way to use v5-text in production. Elastic Inference Service (EIS) provides managed embedding inference with built-in scaling, so you can generate embeddings directly within your Elastic deployment.

bash

PUT _inference/text_embedding/jina-v5
{
  "service": "elastic",
  "service_settings": {
    "model_id": "jina-embeddings-v5-text-nano"
  }
}

See the Elastic Inference Service documentation for setup details.

via Jina AI Embeddings API

bash

curl https://api.jina.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $JINA_AI_API_TOKEN" \
  -d @- <<EOFEOF
  {
    "model": "jina-embeddings-v5-text-nano",
    "task": "text-matching",
    "input": [
        {
            "text": "غروب جميل على الشاطئ"
        },
        {
            "text": "海滩上美丽的日落"
        },
        {
            "text": "A beautiful sunset over the beach"
        },
        {
            "text": "Un beau coucher de soleil sur la plage"
        },
        {
            "text": "Ein wunderschöner Sonnenuntergang am Strand"
        },
        {
            "text": "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία"
        },
        {
            "text": "समुद्र तट पर एक खूबसूरत सूर्यास्त"
        },
        {
            "text": "Un bellissimo tramonto sulla spiaggia"
        },
        {
            "text": "浜辺に沈む美しい夕日"
        },
        {
            "text": "해변 위로 아름다운 일몰"
        },
        {
            "image": "https://i.ibb.co/nQNGqL0/beach1.jpg"
        },
        {
            "image": "https://i.ibb.co/r5w8hG8/beach2.jpg"
        }
    ]
  }
EOFEOF

via transformers

python

from transformers import AutoModel
import torch

model = AutoModel.from_pretrained(
    "jinaai/jina-embeddings-v5-text-nano",
    trust_remote_code=True,
    _attn_implementation="flash_attention_2",  # Recommended but optional
    dtype=torch.bfloat16,  # Recommended for GPUs
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device=device)

# Optional: set truncate_dim and max_length in encode() to control embedding size and input length

# ========================
# 1. Retrieval Task
# ========================
# Encode query
query_embeddings = model.encode(
    texts=["Overview of climate change impacts on coastal cities"],
    task="retrieval",
    prompt_name="query",
)
# Encode document
document_embeddings = model.encode(
    texts=[
        "Climate change has led to rising sea levels, increased frequency of extreme weather events..."
    ],
    task="retrieval",
    prompt_name="document",
)

# ========================
# 2. Text Matching Task
# ========================
texts = [
    "غروب جميل على الشاطئ",  # Arabic
    "海滩上美丽的日落",  # Chinese
    "Un beau coucher de soleil sur la plage",  # French
    "Ein wunderschöner Sonnenuntergang am Strand",  # German
    "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία",  # Greek
    "समुद्र तट पर एक खूबसूरत सूर्यास्त",  # Hindi
    "Un bellissimo tramonto sulla spiaggia",  # Italian
    "浜辺に沈む美しい夕日",  # Japanese
    "해변 위로 아름다운 일몰",  # Korean
]
text_embeddings = model.encode(texts=texts, task="text-matching")

# ========================
# 3. Classification Task
# ========================
texts = [
    "My order hasn't arrived yet and it's been two weeks.",
    "How do I reset my password?",
    "I'd like a refund for my recent purchase.",
    "Your product exceeded my expectations. Great job!",
]
classification_embeddings = model.encode(texts=texts, task="classification")

# ========================
# 4. Clustering Task
# ========================
texts = [
    "We propose a novel neural network architecture for image segmentation.",
    "This paper analyzes the effects of monetary policy on inflation.",
    "Our method achieves state-of-the-art results on object detection benchmarks.",
    "We study the relationship between interest rates and housing prices.",
    "A new attention mechanism is introduced for visual recognition tasks.",
]
clustering_embeddings = model.encode(texts=texts, task="clustering")

via sentence-transformers

python

from sentence_transformers import SentenceTransformer
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = SentenceTransformer(
    "jinaai/jina-embeddings-v5-text-nano",
    trust_remote_code=True,
    device=device,
    model_kwargs={"dtype": torch.bfloat16},  # Recommended for GPUs
    config_kwargs={"_attn_implementation": "flash_attention_2"},  # Recommended but optional
)

# Optional: set truncate_dim in encode() to control embedding size

# ========================
# 1. Retrieval Task
# ========================
# Encode query
query_embeddings = model.encode(
    sentences=["Overview of climate change impacts on coastal cities"],
    task="retrieval",
    prompt_name="query",
)
# Encode document
document_embeddings = model.encode(
    sentences=[
        "Climate change has led to rising sea levels, increased frequency of extreme weather events..."
    ],
    task="retrieval",
    prompt_name="document",
)

# ========================
# 2. Text Matching Task
# ========================
texts = [
    "غروب جميل على الشاطئ",  # Arabic
    "海滩上美丽的日落",  # Chinese
    "Un beau coucher de soleil sur la plage",  # French
    "Ein wunderschöner Sonnenuntergang am Strand",  # German
    "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία",  # Greek
    "समुद्र तट पर एक खूबसूरत सूर्यास्त",  # Hindi
    "Un bellissimo tramonto sulla spiaggia",  # Italian
    "浜辺に沈む美しい夕日",  # Japanese
    "해변 위로 아름다운 일몰",  # Korean
]
text_embeddings = model.encode(sentences=texts, task="text-matching")

# ========================
# 3. Classification Task
# ========================
texts = [
    "My order hasn't arrived yet and it's been two weeks.",
    "How do I reset my password?",
    "I'd like a refund for my recent purchase.",
    "Your product exceeded my expectations. Great job!",
]
classification_embeddings = model.encode(sentences=texts, task="classification")

# ========================
# 4. Clustering Task
# ========================
texts = [
    "We propose a novel neural network architecture for image segmentation.",
    "This paper analyzes the effects of monetary policy on inflation.",
    "Our method achieves state-of-the-art results on object detection benchmarks.",
    "We study the relationship between interest rates and housing prices.",
    "A new attention mechanism is introduced for visual recognition tasks.",
]
clustering_embeddings = model.encode(sentences=texts, task="clustering")

via vLLM

We provide separate model versions for each task (retrieval, text-matching, classification, clustering). For each model, the task-specific adapter is merged into the base model weights.
This modification enables simpler compatibility with vLLM.

Instructions and usage examples for each task are available in their respective model repositories:

via GGUF

We provide GGUF versions with various quantization levels. Instructions and usage examples for each task are available in their respective model repository:

via ONNX and Optimum

We provide separate model versions for each task (retrieval, text-matching, classification, clustering). For each model, the task-specific adapter is merged into the base model weights. This enables inference using ONNX Runtime and Hugging Face Optimum.

We provide ONNX-formatted weights located within the onnx subfolder of each model repository. Instructions and usage examples for each task are available in their respective model repository:

License

jina-embeddings-v5-text-nano is licensed under CC BY-NC 4.0. For commercial use, please contact us.

Citation

If you find jina-embeddings-v5-text-nano useful in your research, please cite the following paper:

bibtex

@article{akram2026jina,
  title={jina-embeddings-v5-text: Task-Targeted Embedding Distillation},
  author={Mohammad Kalim Akram and Saba Sturua and Nastia Havriushenko and Quentin Herreros and Michael G{\"u}nther and Maximilian Werk and Han Xiao},
  journal={arXiv preprint arXiv:2602.15547},
  year={2026}
}