Kanon 2
Isaacus Kanon 2 embedding model optimized for legal and regulatory text retrieval. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- Isaacus
- License
- Open Source
- Price per 1M tokens
- $0.350
- Dimensions
- 1792
- Release Date
- 2025-10-16
- Model Name
- kanon-2
- Total Evaluations
- 1139
Performance Record
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Performance Overview
ELO ratings by dataset
Kanon 2's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Kanon 2 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.806
- nDCG@10
- 0.777
- Recall@5
- 0.062
- Recall@10
- 0.120
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.839
- nDCG@10
- 0.836
- Recall@5
- 0.689
- Recall@10
- 0.763
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.718
- nDCG@10
- 0.744
- Recall@5
- 0.772
- Recall@10
- 0.861
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.941
- nDCG@10
- 0.931
- Recall@5
- 0.117
- Recall@10
- 0.223
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.009
- nDCG@10
- 0.009
- Recall@5
- 0.020
- Recall@10
- 0.020
Latency Distribution
- Mean
- 250ms
- P50 (Median)
- 250ms
- P90
- 250ms
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);
}Compare Models
See how it stacks up
Compare Kanon 2 with other top embeddings to understand the differences in performance, accuracy, and latency.