Gemini text-embedding-004
Supports 3,000 token context length with task-type specification for retrieval and classification. Legacy model scheduled for deprecation on January 14, 2026, replaced by gemini-embedding-001. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- License
- Proprietary
- Price per 1M tokens
- $0.020
- Dimensions
- 768
- Release Date
- 2024-05-14
- Model Name
- text-embedding-004
- Total Evaluations
- 830
Performance Record
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Performance Overview
ELO ratings by dataset
Gemini text-embedding-004's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Gemini text-embedding-004 - 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
- 15ms
- P50 (Median)
- 15ms
- P90
- 15ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.747
- nDCG@10
- 0.737
- Recall@5
- 0.057
- Recall@10
- 0.108
Latency Distribution
- Mean
- 14ms
- P50 (Median)
- 14ms
- P90
- 14ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.744
- nDCG@10
- 0.730
- Recall@5
- 0.647
- Recall@10
- 0.752
Latency Distribution
- Mean
- 16ms
- P50 (Median)
- 16ms
- P90
- 16ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.728
- nDCG@10
- 0.729
- Recall@5
- 0.813
- Recall@10
- 0.857
Latency Distribution
- Mean
- 15ms
- P50 (Median)
- 15ms
- P90
- 15ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.932
- nDCG@10
- 0.918
- Recall@5
- 0.117
- Recall@10
- 0.208
Latency Distribution
- Mean
- 18ms
- P50 (Median)
- 18ms
- P90
- 18ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.021
- nDCG@10
- 0.027
- Recall@5
- 0.040
- Recall@10
- 0.060
Latency Distribution
- Mean
- 15ms
- P50 (Median)
- 15ms
- P90
- 15ms
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 Gemini text-embedding-004 with other top embeddings to understand the differences in performance, accuracy, and latency.