jina-embeddings-text-nano

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.

Training and Evaluation

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

Usage

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.

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.

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.

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

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 me