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OpenAI text-embedding-3-large vs Gemini Embedding 2

Detailed comparison between OpenAI text-embedding-3-large and Gemini Embedding 2. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.

Explore OpenAI text-embedding-3-largeExplore Gemini Embedding 2

Model Comparison

Two competitive embeddings, closely matched.

Both OpenAI text-embedding-3-large and Gemini Embedding 2 are powerful embedding models designed to improve retrieval quality in RAG applications. They show comparable performance across key metrics.

Key similarities:

  • Gemini Embedding 2 has 42 higher ELO rating
  • OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.709 vs 0.628)
  • OpenAI text-embedding-3-large is 416ms faster on average

Overview

Key metrics

ELO Rating

Overall ranking quality

OpenAI text-embedding-3-large

1563

Gemini Embedding 2

1605

Win Rate

Head-to-head performance

OpenAI text-embedding-3-large

56.4%

Gemini Embedding 2

59.5%

Accuracy (nDCG@10)

Ranking quality metric

OpenAI text-embedding-3-large

0.709

Gemini Embedding 2

0.628

Average Latency

Response time

OpenAI text-embedding-3-large

18ms

Gemini Embedding 2

435ms

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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

MetricOpenAI text-embedding-3-largeGemini Embedding 2Description
Overall Performance
ELO Rating
1563
1605
Overall ranking quality based on pairwise comparisons
Win Rate
56.4%
59.5%
Percentage of comparisons won against other models
Pricing & Availability
Price per 1M tokens
$0.130
$0.000
Cost per million tokens processed
Dimensions
3072
3072
Vector embedding dimensions (lower is more efficient)
Release Date
2024-01-25
2026-03-10
Model release date
Accuracy Metrics
Avg nDCG@10
0.709
0.628
Normalized discounted cumulative gain at position 10
Performance Metrics
Avg Latency
18ms
435ms
Average response time across all datasets

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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.

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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

MetricOpenAI text-embedding-3-largeGemini Embedding 2Description
Accuracy Metrics
nDCG@5
0.000
0.091
Ranking quality at top 5 results
nDCG@10
0.000
0.084
Ranking quality at top 10 results
Recall@5
0.000
0.012
% of relevant docs in top 5
Recall@10
0.000
0.020
% of relevant docs in top 10
Latency Metrics
Mean
21ms
439ms
Average response time
P50
21ms
431ms
50th percentile (median)
P90
21ms
603ms
90th percentile

DBPedia

MetricOpenAI text-embedding-3-largeGemini Embedding 2Description
Accuracy Metrics
nDCG@5
0.815
0.788
Ranking quality at top 5 results
nDCG@10
0.795
0.792
Ranking quality at top 10 results
Recall@5
0.062
0.061
% of relevant docs in top 5
Recall@10
0.123
0.120
% of relevant docs in top 10
Latency Metrics
Mean
19ms
436ms
Average response time
P50
19ms
432ms
50th percentile (median)
P90
19ms
592ms
90th percentile

FiQa

MetricOpenAI text-embedding-3-largeGemini Embedding 2Description
Accuracy Metrics
nDCG@5
0.881
0.843
Ranking quality at top 5 results
nDCG@10
0.867
0.835
Ranking quality at top 10 results
Recall@5
0.701
0.763
% of relevant docs in top 5
Recall@10
0.783
0.816
% of relevant docs in top 10
Latency Metrics
Mean
13ms
466ms
Average response time
P50
13ms
454ms
50th percentile (median)
P90
13ms
605ms
90th percentile

SciFact

MetricOpenAI text-embedding-3-largeGemini Embedding 2Description
Accuracy Metrics
nDCG@5
0.702
0.871
Ranking quality at top 5 results
nDCG@10
0.727
0.871
Ranking quality at top 10 results
Recall@5
0.764
0.959
% of relevant docs in top 5
Recall@10
0.861
0.959
% of relevant docs in top 10
Latency Metrics
Mean
19ms
404ms
Average response time
P50
19ms
360ms
50th percentile (median)
P90
19ms
537ms
90th percentile

MSMARCO

MetricOpenAI text-embedding-3-largeGemini Embedding 2Description
Accuracy Metrics
nDCG@5
0.956
0.956
Ranking quality at top 5 results
nDCG@10
0.947
0.939
Ranking quality at top 10 results
Recall@5
0.123
0.122
% of relevant docs in top 5
Recall@10
0.223
0.221
% of relevant docs in top 10
Latency Metrics
Mean
28ms
441ms
Average response time
P50
28ms
446ms
50th percentile (median)
P90
28ms
584ms
90th percentile

ARCD

MetricOpenAI text-embedding-3-largeGemini Embedding 2Description
Accuracy Metrics
nDCG@5
0.829
0.868
Ranking quality at top 5 results
nDCG@10
0.829
0.875
Ranking quality at top 10 results
Recall@5
0.940
0.940
% of relevant docs in top 5
Recall@10
0.940
0.960
% of relevant docs in top 10
Latency Metrics
Mean
10ms
410ms
Average response time
P50
10ms
359ms
50th percentile (median)
P90
10ms
586ms
90th percentile

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