What are the strengths of this model?

Developed by Google DeepMind Sufficient 8K context length Efficient vector representation capability

What are the weaknesses of this model?

Non-open-source license Closed model specifications No external access to internal structure

What are the best use cases?

Building semantic search Determining document similarity Constructing vector DB for RAG

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Google Deep MindProprietary

Gemini Embedding 2

Name: Gemini Embedding 2
Author: Google Deep Mind

Gemini Embedding 2 is an embedding model developed by Google DeepMind. With a context length of 8K, it assists in advanced vectorization of text representations.

Parameters

Undisclosed

Context Window

License

Proprietary

Release Date

2026-03-10

API Pricing

API pricing for this model is not yet available

Strengths

・Developed by Google DeepMind
・Sufficient 8K context length
・Efficient vector representation capability

Weaknesses

・Non-open-source license
・Closed model specifications
・No external access to internal structure

Use Cases

・Building semantic search
・Determining document similarity
・Constructing vector DB for RAG

Deep Analysis

Model Type

Multimodal Embedding

Input Token Limit

8,192

Output Dimensions

128-3072 (recommended: 768, 1536, 3072)

Supported Modalities

Text, Image, Video, Audio, PDF

Latest Update

April 2026

Languages

100+

Strengths

・First natively multimodal embedding model from Google
・State-of-the-art on MTEB Multilingual (69.9) and MTEB Code (84.0)
・Flexible output dimensions via Matryoshka Representation Learning
・Supports 100+ languages for cross-lingual tasks
・Single unified embedding space for all modalities

Weaknesses

・Higher cost than text-only embedding models
・Requires Google API access (no open-source weights)
・Large input processing may have latency for video/audio
・Complex multimodal pipelines still need careful orchestration
・Limited fine-tuning options for domain-specific use cases

Competitor Comparison

Model	Arena	SWE	GPQA	Price
Amazon Nova 2 Multimodal	N/A	N/A	N/A	Custom pricing
Voyage Multimodal 3.5	N/A	N/A	N/A	$0.12/1M tokens
OpenAI text-embedding-3-large	N/A	N/A	N/A	$0.13/1M tokens
Cohere Embed v4	N/A	N/A	N/A	$0.10/1M tokens

Overview

Gemini Embedding 2 is Google's first natively multimodal embedding model, mapping text, images, video, audio, and PDFs into a single unified embedding space. Released in March 2026 and now generally available, it achieves state-of-the-art performance on cross-modal retrieval benchmarks while supporting 100+ languages.

Benchmarks & Performance

Achieves 69.9 on MTEB Multilingual (vs 68.4 for gemini-embedding-001), 84.0 on MTEB Code (vs 76.0). TextCaps recall@1 of 89.6 for text-to-image, 97.4 for image-to-text. Docci recall@1 of 93.4 for text-to-image. ViDoRe v2 ndcg@10 of 64.9 for text-document retrieval. Significantly outperforms legacy Google models and competitors on most benchmarks.

Detailed Comparison

Outperforms Amazon Nova 2 Multimodal on most benchmarks (e.g., TextCaps 89.6 vs 76.0 text-to-image). Beats Voyage Multimodal 3.5 on cross-modal tasks. Key advantage is native multimodality without separate models per modality. Trade-off is API-only access vs. open-source alternatives.

Community Feedback

Strong positive reception from RAG developers and search engineers. E-commerce and video analysis use cases reported during preview. GA announcement signals production readiness. Developers appreciate the unified embedding space reducing pipeline complexity.

Use Cases

Ideal for multimodal search engines, RAG systems with mixed content, recommendation systems, document retrieval, video analysis, and cross-lingual semantic search. The flexible dimension support (128-3072) allows cost-performance optimization. Particularly valuable for applications needing to search across text, images, and video simultaneously.

Latest News

Introduced March 10, 2026. Generally available as of April 2026 via Gemini API and Gemini Enterprise Agent Platform. Powers many internal Google products.

Sources

Analysis generated: 2026-05-24