zembed-1
4B parameter multilingual embedding model distilled from zerank-2 reranker. Supports dimension reduction (2048 to 40) and quantization (32-bit to binary). Strong performance on finance, healthcare, and legal domains. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- ZeroEntropy
- License
- CC BY-NC 4.0
- Price per 1M tokens
- $0.050
- Dimensions
- 2048
- Release Date
- 2026-03-02
- Model Name
- zembed-1
- Total Evaluations
- 1000
Performance Record
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Performance Overview
ELO ratings by dataset
zembed-1's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
zembed-1 - 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
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.832
- nDCG@10
- 0.811
- Recall@5
- 0.062
- Recall@10
- 0.121
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.862
- nDCG@10
- 0.855
- Recall@5
- 0.668
- Recall@10
- 0.712
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.767
- nDCG@10
- 0.777
- Recall@5
- 0.888
- Recall@10
- 0.929
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.955
- nDCG@10
- 0.946
- Recall@5
- 0.123
- Recall@10
- 0.223
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.851
- nDCG@10
- 0.858
- Recall@5
- 0.920
- Recall@10
- 0.940
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
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 zembed-1 with other top embeddings to understand the differences in performance, accuracy, and latency.