Cohere Embed v3
Optimized for English text with 512 token context length supporting retrieval, classification, and clustering tasks. Requires input_type specification distinguishing between search documents and queries for optimal performance. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- Cohere
- License
- Proprietary
- Price per 1M tokens
- $0.100
- Dimensions
- 1024
- Release Date
- 2024-02-07
- Model Name
- cohere-embed-v3
- Total Evaluations
- 830
Performance Record
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Performance Overview
ELO ratings by dataset
Cohere Embed v3's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Cohere Embed v3 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.810
- nDCG@10
- 0.797
- Recall@5
- 0.062
- Recall@10
- 0.122
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.806
- nDCG@10
- 0.800
- Recall@5
- 0.640
- Recall@10
- 0.681
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.707
- nDCG@10
- 0.740
- Recall@5
- 0.784
- Recall@10
- 0.898
Latency Distribution
- Mean
- 8ms
- P50 (Median)
- 8ms
- P90
- 8ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.961
- nDCG@10
- 0.942
- Recall@5
- 0.124
- Recall@10
- 0.218
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.330
- nDCG@10
- 0.376
- Recall@5
- 0.380
- Recall@10
- 0.520
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
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 Cohere Embed v3 with other top embeddings to understand the differences in performance, accuracy, and latency.