Gemini Embedding 2 vs OpenAI text-embedding-3-small
Detailed comparison between Gemini Embedding 2 and OpenAI text-embedding-3-small. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.
Model Comparison
Gemini Embedding 2 takes the lead.
Both Gemini Embedding 2 and OpenAI text-embedding-3-small are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.
Why Gemini Embedding 2:
- Gemini Embedding 2 has 125 higher ELO rating
- OpenAI text-embedding-3-small delivers better accuracy (nDCG@10: 0.689 vs 0.628)
- OpenAI text-embedding-3-small is 419ms faster on average
- Gemini Embedding 2 has a 15.6% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Gemini Embedding 2
OpenAI text-embedding-3-small
Win Rate
Head-to-head performance
Gemini Embedding 2
OpenAI text-embedding-3-small
Accuracy (nDCG@10)
Ranking quality metric
Gemini Embedding 2
OpenAI text-embedding-3-small
Average Latency
Response time
Gemini Embedding 2
OpenAI text-embedding-3-small
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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 | Gemini Embedding 2 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1605 | 1480 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 59.5% | 43.9% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.000 | $0.020 | Cost per million tokens processed |
| Dimensions | 3072 | 1536 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2026-03-10 | 2024-01-25 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.628 | 0.689 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 435ms | 15ms | 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.
FiQa
| Metric | Gemini Embedding 2 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.843 | 0.801 | Ranking quality at top 5 results |
| nDCG@10 | 0.835 | 0.814 | Ranking quality at top 10 results |
| Recall@5 | 0.763 | 0.624 | % of relevant docs in top 5 |
| Recall@10 | 0.816 | 0.682 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 466ms | 16ms | Average response time |
| P50 | 454ms | 16ms | 50th percentile (median) |
| P90 | 605ms | 16ms | 90th percentile |
MSMARCO
| Metric | Gemini Embedding 2 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.956 | 0.959 | Ranking quality at top 5 results |
| nDCG@10 | 0.939 | 0.946 | Ranking quality at top 10 results |
| Recall@5 | 0.122 | 0.122 | % of relevant docs in top 5 |
| Recall@10 | 0.221 | 0.212 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 441ms | 20ms | Average response time |
| P50 | 446ms | 20ms | 50th percentile (median) |
| P90 | 584ms | 20ms | 90th percentile |
SciFact
| Metric | Gemini Embedding 2 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.871 | 0.663 | Ranking quality at top 5 results |
| nDCG@10 | 0.871 | 0.684 | Ranking quality at top 10 results |
| Recall@5 | 0.959 | 0.774 | % of relevant docs in top 5 |
| Recall@10 | 0.959 | 0.840 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 404ms | 17ms | Average response time |
| P50 | 360ms | 17ms | 50th percentile (median) |
| P90 | 537ms | 17ms | 90th percentile |
DBPedia
| Metric | Gemini Embedding 2 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.788 | 0.858 | Ranking quality at top 5 results |
| nDCG@10 | 0.792 | 0.807 | Ranking quality at top 10 results |
| Recall@5 | 0.061 | 0.062 | % of relevant docs in top 5 |
| Recall@10 | 0.120 | 0.123 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 436ms | 9ms | Average response time |
| P50 | 432ms | 9ms | 50th percentile (median) |
| P90 | 592ms | 9ms | 90th percentile |
business reports
| Metric | Gemini Embedding 2 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.091 | 0.000 | Ranking quality at top 5 results |
| nDCG@10 | 0.084 | 0.000 | Ranking quality at top 10 results |
| Recall@5 | 0.012 | 0.000 | % of relevant docs in top 5 |
| Recall@10 | 0.020 | 0.000 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 439ms | 16ms | Average response time |
| P50 | 431ms | 16ms | 50th percentile (median) |
| P90 | 603ms | 16ms | 90th percentile |
ARCD
| Metric | Gemini Embedding 2 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.868 | 0.786 | Ranking quality at top 5 results |
| nDCG@10 | 0.875 | 0.793 | Ranking quality at top 10 results |
| Recall@5 | 0.940 | 0.900 | % of relevant docs in top 5 |
| Recall@10 | 0.960 | 0.920 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 410ms | 15ms | Average response time |
| P50 | 359ms | 15ms | 50th percentile (median) |
| P90 | 586ms | 15ms | 90th percentile |
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