zembed-1 vs OpenAI text-embedding-3-large
Detailed comparison between zembed-1 and OpenAI text-embedding-3-large. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.
Model Comparison
Two competitive embeddings, closely matched.
Both zembed-1 and OpenAI text-embedding-3-large are powerful embedding models designed to improve retrieval quality in RAG applications. They show comparable performance across key metrics.
Key similarities:
- zembed-1 has 22 higher ELO rating
- OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.709 vs 0.619)
- OpenAI text-embedding-3-large is 232ms faster on average
Overview
Key metrics
ELO Rating
Overall ranking quality
zembed-1
OpenAI text-embedding-3-large
Win Rate
Head-to-head performance
zembed-1
OpenAI text-embedding-3-large
Accuracy (nDCG@10)
Ranking quality metric
zembed-1
OpenAI text-embedding-3-large
Average Latency
Response time
zembed-1
OpenAI text-embedding-3-large
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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 | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1595 | 1573 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 59.2% | 56.4% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.050 | $0.130 | Cost per million tokens processed |
| Dimensions | 2048 | 3072 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2026-03-02 | 2024-01-25 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.619 | 0.709 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 250ms | 18ms | 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 | OpenAI text-embedding-3-large | 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 | 21ms | Average response time |
| P50 | 250ms | 21ms | 50th percentile (median) |
| P90 | 250ms | 21ms | 90th percentile |
DBPedia
| Metric | zembed-1 | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.832 | 0.815 | Ranking quality at top 5 results |
| nDCG@10 | 0.811 | 0.795 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.062 | % of relevant docs in top 5 |
| Recall@10 | 0.121 | 0.123 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 19ms | Average response time |
| P50 | 250ms | 19ms | 50th percentile (median) |
| P90 | 250ms | 19ms | 90th percentile |
FiQa
| Metric | zembed-1 | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.862 | 0.881 | Ranking quality at top 5 results |
| nDCG@10 | 0.855 | 0.867 | Ranking quality at top 10 results |
| Recall@5 | 0.668 | 0.701 | % of relevant docs in top 5 |
| Recall@10 | 0.712 | 0.783 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 13ms | Average response time |
| P50 | 250ms | 13ms | 50th percentile (median) |
| P90 | 250ms | 13ms | 90th percentile |
SciFact
| Metric | zembed-1 | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.767 | 0.702 | Ranking quality at top 5 results |
| nDCG@10 | 0.777 | 0.727 | Ranking quality at top 10 results |
| Recall@5 | 0.888 | 0.764 | % of relevant docs in top 5 |
| Recall@10 | 0.929 | 0.861 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 19ms | Average response time |
| P50 | 250ms | 19ms | 50th percentile (median) |
| P90 | 250ms | 19ms | 90th percentile |
MSMARCO
| Metric | zembed-1 | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.955 | 0.956 | Ranking quality at top 5 results |
| nDCG@10 | 0.946 | 0.947 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.123 | % of relevant docs in top 5 |
| Recall@10 | 0.223 | 0.223 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 28ms | Average response time |
| P50 | 250ms | 28ms | 50th percentile (median) |
| P90 | 250ms | 28ms | 90th percentile |
ARCD
| Metric | zembed-1 | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.851 | 0.829 | Ranking quality at top 5 results |
| nDCG@10 | 0.858 | 0.829 | Ranking quality at top 10 results |
| Recall@5 | 0.920 | 0.940 | % of relevant docs in top 5 |
| Recall@10 | 0.940 | 0.940 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 10ms | Average response time |
| P50 | 250ms | 10ms | 50th percentile (median) |
| P90 | 250ms | 10ms | 90th percentile |
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