Kanon 2 vs Gemini text-embedding-004
Detailed comparison between Kanon 2 and Gemini text-embedding-004. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.
Model Comparison
Kanon 2 takes the lead.
Both Kanon 2 and Gemini text-embedding-004 are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.
Why Kanon 2:
- Kanon 2 has 72 higher ELO rating
- Gemini text-embedding-004 delivers better accuracy (nDCG@10: 0.538 vs 0.484)
- Gemini text-embedding-004 is 234ms faster on average
- Kanon 2 has a 5.0% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Kanon 2
Gemini text-embedding-004
Win Rate
Head-to-head performance
Kanon 2
Gemini text-embedding-004
Accuracy (nDCG@10)
Ranking quality metric
Kanon 2
Gemini text-embedding-004
Average Latency
Response time
Kanon 2
Gemini text-embedding-004
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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 | Kanon 2 | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1450 | 1378 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 33.5% | 28.4% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.350 | $0.020 | Cost per million tokens processed |
| Dimensions | 1792 | 768 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2025-10-16 | 2024-05-14 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.484 | 0.538 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 250ms | 16ms | 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 | Kanon 2 | Gemini text-embedding-004 | 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 | 250ms | 15ms | Average response time |
| P50 | 250ms | 15ms | 50th percentile (median) |
| P90 | 250ms | 15ms | 90th percentile |
DBPedia
| Metric | Kanon 2 | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.806 | 0.747 | Ranking quality at top 5 results |
| nDCG@10 | 0.777 | 0.737 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.057 | % of relevant docs in top 5 |
| Recall@10 | 0.120 | 0.108 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 14ms | Average response time |
| P50 | 250ms | 14ms | 50th percentile (median) |
| P90 | 250ms | 14ms | 90th percentile |
FiQa
| Metric | Kanon 2 | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.839 | 0.744 | Ranking quality at top 5 results |
| nDCG@10 | 0.836 | 0.730 | Ranking quality at top 10 results |
| Recall@5 | 0.689 | 0.647 | % of relevant docs in top 5 |
| Recall@10 | 0.763 | 0.752 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 16ms | Average response time |
| P50 | 250ms | 16ms | 50th percentile (median) |
| P90 | 250ms | 16ms | 90th percentile |
SciFact
| Metric | Kanon 2 | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.718 | 0.728 | Ranking quality at top 5 results |
| nDCG@10 | 0.744 | 0.729 | Ranking quality at top 10 results |
| Recall@5 | 0.772 | 0.813 | % of relevant docs in top 5 |
| Recall@10 | 0.861 | 0.857 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 15ms | Average response time |
| P50 | 250ms | 15ms | 50th percentile (median) |
| P90 | 250ms | 15ms | 90th percentile |
MSMARCO
| Metric | Kanon 2 | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.941 | 0.932 | Ranking quality at top 5 results |
| nDCG@10 | 0.931 | 0.918 | Ranking quality at top 10 results |
| Recall@5 | 0.117 | 0.117 | % of relevant docs in top 5 |
| Recall@10 | 0.223 | 0.208 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 18ms | Average response time |
| P50 | 250ms | 18ms | 50th percentile (median) |
| P90 | 250ms | 18ms | 90th percentile |
ARCD
| Metric | Kanon 2 | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.009 | 0.021 | Ranking quality at top 5 results |
| nDCG@10 | 0.009 | 0.027 | Ranking quality at top 10 results |
| Recall@5 | 0.020 | 0.040 | % of relevant docs in top 5 |
| Recall@10 | 0.020 | 0.060 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 250ms | 15ms | Average response time |
| P50 | 250ms | 15ms | 50th percentile (median) |
| P90 | 250ms | 15ms | 90th percentile |
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