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OpenAI text-embedding-3-large vs Cohere Embed v3

Detailed comparison between OpenAI text-embedding-3-large and Cohere Embed v3. 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 Cohere Embed v3

Model Comparison

OpenAI text-embedding-3-large takes the lead.

Both OpenAI text-embedding-3-large and Cohere Embed v3 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 91 higher ELO rating
  • OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.709 vs 0.624)
  • OpenAI text-embedding-3-large has a 13.6% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

OpenAI text-embedding-3-large

1563

Cohere Embed v3

1472

Win Rate

Head-to-head performance

OpenAI text-embedding-3-large

56.4%

Cohere Embed v3

42.8%

Accuracy (nDCG@10)

Ranking quality metric

OpenAI text-embedding-3-large

0.709

Cohere Embed v3

0.624

Average Latency

Response time

OpenAI text-embedding-3-large

18ms

Cohere Embed v3

7ms

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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-largeCohere Embed v3Description
Overall Performance
ELO Rating
1563
1472
Overall ranking quality based on pairwise comparisons
Win Rate
56.4%
42.8%
Percentage of comparisons won against other models
Pricing & Availability
Price per 1M tokens
$0.130
$0.100
Cost per million tokens processed
Dimensions
3072
1024
Vector embedding dimensions (lower is more efficient)
Release Date
2024-01-25
2024-02-07
Model release date
Accuracy Metrics
Avg nDCG@10
0.709
0.624
Normalized discounted cumulative gain at position 10
Performance Metrics
Avg Latency
18ms
7ms
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-largeCohere Embed v3Description
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
21ms
7ms
Average response time
P50
21ms
7ms
50th percentile (median)
P90
21ms
7ms
90th percentile

DBPedia

MetricOpenAI text-embedding-3-largeCohere Embed v3Description
Accuracy Metrics
nDCG@5
0.815
0.810
Ranking quality at top 5 results
nDCG@10
0.795
0.797
Ranking quality at top 10 results
Recall@5
0.062
0.062
% of relevant docs in top 5
Recall@10
0.123
0.122
% of relevant docs in top 10
Latency Metrics
Mean
19ms
7ms
Average response time
P50
19ms
7ms
50th percentile (median)
P90
19ms
7ms
90th percentile

FiQa

MetricOpenAI text-embedding-3-largeCohere Embed v3Description
Accuracy Metrics
nDCG@5
0.881
0.806
Ranking quality at top 5 results
nDCG@10
0.867
0.800
Ranking quality at top 10 results
Recall@5
0.701
0.640
% of relevant docs in top 5
Recall@10
0.783
0.681
% of relevant docs in top 10
Latency Metrics
Mean
13ms
7ms
Average response time
P50
13ms
7ms
50th percentile (median)
P90
13ms
7ms
90th percentile

SciFact

MetricOpenAI text-embedding-3-largeCohere Embed v3Description
Accuracy Metrics
nDCG@5
0.702
0.707
Ranking quality at top 5 results
nDCG@10
0.727
0.740
Ranking quality at top 10 results
Recall@5
0.764
0.784
% of relevant docs in top 5
Recall@10
0.861
0.898
% of relevant docs in top 10
Latency Metrics
Mean
19ms
8ms
Average response time
P50
19ms
8ms
50th percentile (median)
P90
19ms
8ms
90th percentile

MSMARCO

MetricOpenAI text-embedding-3-largeCohere Embed v3Description
Accuracy Metrics
nDCG@5
0.956
0.961
Ranking quality at top 5 results
nDCG@10
0.947
0.942
Ranking quality at top 10 results
Recall@5
0.123
0.124
% of relevant docs in top 5
Recall@10
0.223
0.218
% of relevant docs in top 10
Latency Metrics
Mean
28ms
7ms
Average response time
P50
28ms
7ms
50th percentile (median)
P90
28ms
7ms
90th percentile

ARCD

MetricOpenAI text-embedding-3-largeCohere Embed v3Description
Accuracy Metrics
nDCG@5
0.829
0.330
Ranking quality at top 5 results
nDCG@10
0.829
0.376
Ranking quality at top 10 results
Recall@5
0.940
0.380
% of relevant docs in top 5
Recall@10
0.940
0.520
% of relevant docs in top 10
Latency Metrics
Mean
10ms
7ms
Average response time
P50
10ms
7ms
50th percentile (median)
P90
10ms
7ms
90th percentile

Explore More

Compare more embeddings

See how all embedding models stack up. Compare OpenAI, Cohere, Jina AI, Voyage, and more. View comprehensive benchmarks, compare performance metrics, and find the perfect embedding for your RAG application.

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