OpenAI text-embedding-3-small vs OpenAI text-embedding-3-large
Detailed comparison between OpenAI text-embedding-3-small 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
OpenAI text-embedding-3-large takes the lead.
Both OpenAI text-embedding-3-small and OpenAI text-embedding-3-large are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.
Why OpenAI text-embedding-3-large:
- OpenAI text-embedding-3-large has 83 higher ELO rating
- OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.709 vs 0.689)
- OpenAI text-embedding-3-large has a 12.5% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-small
OpenAI text-embedding-3-large
Win Rate
Head-to-head performance
OpenAI text-embedding-3-small
OpenAI text-embedding-3-large
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-small
OpenAI text-embedding-3-large
Average Latency
Response time
OpenAI text-embedding-3-small
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 | OpenAI text-embedding-3-small | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1480 | 1563 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 43.9% | 56.4% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.020 | $0.130 | Cost per million tokens processed |
| Dimensions | 1536 | 3072 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2024-01-25 | 2024-01-25 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.689 | 0.709 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 15ms | 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 | OpenAI text-embedding-3-small | 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 | 16ms | 21ms | Average response time |
| P50 | 16ms | 21ms | 50th percentile (median) |
| P90 | 16ms | 21ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-small | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.858 | 0.815 | Ranking quality at top 5 results |
| nDCG@10 | 0.807 | 0.795 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.062 | % of relevant docs in top 5 |
| Recall@10 | 0.123 | 0.123 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 9ms | 19ms | Average response time |
| P50 | 9ms | 19ms | 50th percentile (median) |
| P90 | 9ms | 19ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-small | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.801 | 0.881 | Ranking quality at top 5 results |
| nDCG@10 | 0.814 | 0.867 | Ranking quality at top 10 results |
| Recall@5 | 0.624 | 0.701 | % of relevant docs in top 5 |
| Recall@10 | 0.682 | 0.783 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 16ms | 13ms | Average response time |
| P50 | 16ms | 13ms | 50th percentile (median) |
| P90 | 16ms | 13ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-small | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.663 | 0.702 | Ranking quality at top 5 results |
| nDCG@10 | 0.684 | 0.727 | Ranking quality at top 10 results |
| Recall@5 | 0.774 | 0.764 | % of relevant docs in top 5 |
| Recall@10 | 0.840 | 0.861 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 17ms | 19ms | Average response time |
| P50 | 17ms | 19ms | 50th percentile (median) |
| P90 | 17ms | 19ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-small | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.959 | 0.956 | Ranking quality at top 5 results |
| nDCG@10 | 0.946 | 0.947 | Ranking quality at top 10 results |
| Recall@5 | 0.122 | 0.123 | % of relevant docs in top 5 |
| Recall@10 | 0.212 | 0.223 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 20ms | 28ms | Average response time |
| P50 | 20ms | 28ms | 50th percentile (median) |
| P90 | 20ms | 28ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-small | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.786 | 0.829 | Ranking quality at top 5 results |
| nDCG@10 | 0.793 | 0.829 | Ranking quality at top 10 results |
| Recall@5 | 0.900 | 0.940 | % of relevant docs in top 5 |
| Recall@10 | 0.920 | 0.940 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 15ms | 10ms | Average response time |
| P50 | 15ms | 10ms | 50th percentile (median) |
| P90 | 15ms | 10ms | 90th percentile |
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