zembed-1 vs Cohere Embed Multilingual v3
Detailed comparison between zembed-1 and Cohere Embed Multilingual v3. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.
Model Comparison
zembed-1 takes the lead.
Both zembed-1 and Cohere Embed Multilingual v3 are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.
Why zembed-1:
- zembed-1 has 76 higher ELO rating
- Cohere Embed Multilingual v3 delivers better accuracy (nDCG@10: 0.701 vs 0.619)
- Cohere Embed Multilingual v3 is 243ms faster on average
- zembed-1 has a 10.8% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
zembed-1
Cohere Embed Multilingual v3
Win Rate
Head-to-head performance
zembed-1
Cohere Embed Multilingual v3
Accuracy (nDCG@10)
Ranking quality metric
zembed-1
Cohere Embed Multilingual v3
Average Latency
Response time
zembed-1
Cohere Embed Multilingual v3
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Visual Performance Analysis
Performance
ELO Rating Comparison
Win/Loss/Tie Breakdown
Accuracy Across Datasets (nDCG@10)
Latency Distribution (ms)
Breakdown
How the models stack up
| Metric | zembed-1 | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1595 | 1519 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 59.2% | 48.4% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.050 | $0.100 | Cost per million tokens processed |
| Dimensions | 2048 | 512 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2026-03-02 | 2024-02-07 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.619 | 0.701 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 250ms | 7ms | Average response time across all datasets |
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);
}Dataset Performance
By field
Comprehensive comparison of accuracy metrics (nDCG, Recall) and latency percentiles for each benchmark dataset.
business reports
| Metric | zembed-1 | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.000 | 0.000 | Ranking quality at top 5 results |
| nDCG@10 | 0.000 | 0.000 | Ranking quality at top 10 results |
| Recall@5 | 0.000 | 0.000 | % of relevant docs in top 5 |
| Recall@10 | 0.000 | 0.000 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 8ms | Average response time |
| P50 | 250ms | 8ms | 50th percentile (median) |
| P90 | 250ms | 8ms | 90th percentile |
DBPedia
| Metric | zembed-1 | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.832 | 0.786 | Ranking quality at top 5 results |
| nDCG@10 | 0.811 | 0.783 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.061 | % of relevant docs in top 5 |
| Recall@10 | 0.121 | 0.122 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 7ms | Average response time |
| P50 | 250ms | 7ms | 50th percentile (median) |
| P90 | 250ms | 7ms | 90th percentile |
FiQa
| Metric | zembed-1 | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.862 | 0.804 | Ranking quality at top 5 results |
| nDCG@10 | 0.855 | 0.812 | Ranking quality at top 10 results |
| Recall@5 | 0.668 | 0.624 | % of relevant docs in top 5 |
| Recall@10 | 0.712 | 0.696 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 7ms | Average response time |
| P50 | 250ms | 7ms | 50th percentile (median) |
| P90 | 250ms | 7ms | 90th percentile |
SciFact
| Metric | zembed-1 | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.767 | 0.696 | Ranking quality at top 5 results |
| nDCG@10 | 0.777 | 0.702 | Ranking quality at top 10 results |
| Recall@5 | 0.888 | 0.804 | % of relevant docs in top 5 |
| Recall@10 | 0.929 | 0.830 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 7ms | Average response time |
| P50 | 250ms | 7ms | 50th percentile (median) |
| P90 | 250ms | 7ms | 90th percentile |
MSMARCO
| Metric | zembed-1 | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.955 | 0.952 | Ranking quality at top 5 results |
| nDCG@10 | 0.946 | 0.941 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.121 | % of relevant docs in top 5 |
| Recall@10 | 0.223 | 0.218 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 8ms | Average response time |
| P50 | 250ms | 8ms | 50th percentile (median) |
| P90 | 250ms | 8ms | 90th percentile |
ARCD
| Metric | zembed-1 | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.851 | 0.868 | Ranking quality at top 5 results |
| nDCG@10 | 0.858 | 0.875 | Ranking quality at top 10 results |
| Recall@5 | 0.920 | 0.940 | % of relevant docs in top 5 |
| Recall@10 | 0.940 | 0.960 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 7ms | Average response time |
| P50 | 250ms | 7ms | 50th percentile (median) |
| P90 | 250ms | 7ms | 90th percentile |
Explore More
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