Jina Embeddings v5 Text Small
677M parameter multilingual embedding model built on Qwen3-0.6B-Base with 32K token context length supporting 119+ languages. Features four task-specific LoRA adapters for retrieval, text-matching, clustering, and classification, with Matryoshka learning enabling dimension reduction down to 32. Achieves 67.0 average on MMTEB, best among sub-1B multilingual models. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- Jina AI
- License
- CC BY-NC 4.0
- Price per 1M tokens
- $0.050
- Dimensions
- 1024
- Release Date
- 2026-02-18
- Model Name
- jina-embeddings-v5-text-small
- Total Evaluations
- 1120
Performance Record
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Performance Overview
ELO ratings by dataset
Jina Embeddings v5 Text Small's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Jina Embeddings v5 Text Small - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 291ms
- P50 (Median)
- 241ms
- P90
- 290ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 283ms
- P50 (Median)
- 247ms
- P90
- 322ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.823
- nDCG@10
- 0.805
- Recall@5
- 0.062
- Recall@10
- 0.123
Latency Distribution
- Mean
- 270ms
- P50 (Median)
- 239ms
- P90
- 264ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.838
- nDCG@10
- 0.831
- Recall@5
- 0.677
- Recall@10
- 0.771
Latency Distribution
- Mean
- 300ms
- P50 (Median)
- 241ms
- P90
- 419ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.703
- nDCG@10
- 0.734
- Recall@5
- 0.789
- Recall@10
- 0.898
Latency Distribution
- Mean
- 267ms
- P50 (Median)
- 240ms
- P90
- 265ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.960
- nDCG@10
- 0.954
- Recall@5
- 0.122
- Recall@10
- 0.219
Latency Distribution
- Mean
- 273ms
- P50 (Median)
- 239ms
- P90
- 313ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.842
- nDCG@10
- 0.842
- Recall@5
- 0.940
- Recall@10
- 0.940
Latency Distribution
- Mean
- 336ms
- P50 (Median)
- 248ms
- P90
- 305ms
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked embedding models and smart retrieval built in. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Compare Models
See how it stacks up
Compare Jina Embeddings v5 Text Small with other top embeddings to understand the differences in performance, accuracy, and latency.