OpenAI text-embedding-3-large vs Kanon 2
Detailed comparison between OpenAI text-embedding-3-large and Kanon 2. 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-large and Kanon 2 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 123 higher ELO rating
- OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.709 vs 0.484)
- OpenAI text-embedding-3-large is 232ms faster on average
- OpenAI text-embedding-3-large has a 22.9% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-large
Kanon 2
Win Rate
Head-to-head performance
OpenAI text-embedding-3-large
Kanon 2
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-large
Kanon 2
Average Latency
Response time
OpenAI text-embedding-3-large
Kanon 2
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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-large | Kanon 2 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1573 | 1450 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 56.4% | 33.5% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.130 | $0.350 | Cost per million tokens processed |
| Dimensions | 3072 | 1792 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2024-01-25 | 2025-10-16 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.709 | 0.484 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 18ms | 250ms | 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.
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-large | Kanon 2 | 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 | 21ms | 250ms | Average response time |
| P50 | 21ms | 250ms | 50th percentile (median) |
| P90 | 21ms | 250ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-large | Kanon 2 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.815 | 0.806 | Ranking quality at top 5 results |
| nDCG@10 | 0.795 | 0.777 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.062 | % of relevant docs in top 5 |
| Recall@10 | 0.123 | 0.120 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 19ms | 250ms | Average response time |
| P50 | 19ms | 250ms | 50th percentile (median) |
| P90 | 19ms | 250ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-large | Kanon 2 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.881 | 0.839 | Ranking quality at top 5 results |
| nDCG@10 | 0.867 | 0.836 | Ranking quality at top 10 results |
| Recall@5 | 0.701 | 0.689 | % of relevant docs in top 5 |
| Recall@10 | 0.783 | 0.763 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 13ms | 250ms | Average response time |
| P50 | 13ms | 250ms | 50th percentile (median) |
| P90 | 13ms | 250ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-large | Kanon 2 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.702 | 0.718 | Ranking quality at top 5 results |
| nDCG@10 | 0.727 | 0.744 | Ranking quality at top 10 results |
| Recall@5 | 0.764 | 0.772 | % of relevant docs in top 5 |
| Recall@10 | 0.861 | 0.861 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 19ms | 250ms | Average response time |
| P50 | 19ms | 250ms | 50th percentile (median) |
| P90 | 19ms | 250ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-large | Kanon 2 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.956 | 0.941 | Ranking quality at top 5 results |
| nDCG@10 | 0.947 | 0.931 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.117 | % of relevant docs in top 5 |
| Recall@10 | 0.223 | 0.223 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 28ms | 250ms | Average response time |
| P50 | 28ms | 250ms | 50th percentile (median) |
| P90 | 28ms | 250ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-large | Kanon 2 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.829 | 0.009 | Ranking quality at top 5 results |
| nDCG@10 | 0.829 | 0.009 | Ranking quality at top 10 results |
| Recall@5 | 0.940 | 0.020 | % of relevant docs in top 5 |
| Recall@10 | 0.940 | 0.020 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 10ms | 250ms | Average response time |
| P50 | 10ms | 250ms | 50th percentile (median) |
| P90 | 10ms | 250ms | 90th percentile |
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