Gemini Embedding 2
Google's first natively multimodal embedding model built on Gemini architecture, supporting text (8,192 tokens), images, video, audio, and documents in a single unified embedding space across 100+ languages. Features Matryoshka Representation Learning with flexible output dimensions (3072/1536/768). Available in Public Preview via Gemini API. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- License
- Proprietary
- Price per 1M tokens
- $0.000
- Dimensions
- 3072
- Release Date
- 2026-03-10
- Model Name
- gemini-embedding-2-preview
- Total Evaluations
- 1065
Performance Record
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Performance Overview
ELO ratings by dataset
Gemini Embedding 2's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Gemini Embedding 2 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
FiQa
Accuracy Metrics
- nDCG@5
- 0.843
- nDCG@10
- 0.835
- Recall@5
- 0.763
- Recall@10
- 0.816
Latency Distribution
- Mean
- 466ms
- P50 (Median)
- 454ms
- P90
- 605ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.956
- nDCG@10
- 0.939
- Recall@5
- 0.122
- Recall@10
- 0.221
Latency Distribution
- Mean
- 441ms
- P50 (Median)
- 446ms
- P90
- 584ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.871
- nDCG@10
- 0.871
- Recall@5
- 0.959
- Recall@10
- 0.959
Latency Distribution
- Mean
- 404ms
- P50 (Median)
- 360ms
- P90
- 537ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.788
- nDCG@10
- 0.792
- Recall@5
- 0.061
- Recall@10
- 0.120
Latency Distribution
- Mean
- 436ms
- P50 (Median)
- 432ms
- P90
- 592ms
business reports
Accuracy Metrics
- nDCG@5
- 0.091
- nDCG@10
- 0.084
- Recall@5
- 0.012
- Recall@10
- 0.020
Latency Distribution
- Mean
- 439ms
- P50 (Median)
- 431ms
- P90
- 603ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.868
- nDCG@10
- 0.875
- Recall@5
- 0.940
- Recall@10
- 0.960
Latency Distribution
- Mean
- 410ms
- P50 (Median)
- 359ms
- P90
- 586ms
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 448ms
- P50 (Median)
- 431ms
- P90
- 595ms
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 Embedding 2 with other top embeddings to understand the differences in performance, accuracy, and latency.