Google의 Gemini Embedding 2 모델은 고차원 시맨틱 벡터 검색, 문서 인덱싱 및 엔터프라이즈 RAG 파이프라인에 최적화된 AI 모델로, 8K 컨텍스트 윈도우, 초당 350 토큰의 생성 속도, 종합 지능 지수 78/100점(1M 토큰당 입력 $0.2 / 출력 $0)을 제공합니다.
Technical Specifications
- Provider
- Category
- lightweight
- Release Date
- 2026-04
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- Output Modalities
- embedding
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- models/gemini-embedding-2
- Supported SDK & API Protocol
- Google Gemini API (generateContent) + OpenAI-Compatible SDK
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.2/1M in) across full 8K window
- Prompt Cache Write & TTL Policy
- Implicit + Explicit Context Caching at $0/1M with configurable TTL
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- Web Search Grounding & Code Sandbox
- Built-in Google Search Grounding + Managed Python Code Execution Sandbox
- Fine-Tuning & Distillation Support
- Vertex AI Supervised Adapter Tuning & Context Distillation
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed Google Cloud API
- System Card & Safety Framework
- Google DeepMind Frontier Safety Framework Verified
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Gemini Embedding 2 Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $14.00/mo vs $20 flat plan).
Break-even point: ~1,667 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Gemini Embedding 2:
Benchmark Scores & 6-Axis Capability Radar
- Chatbot Arena Elo
- 1340pts
Strengths
- 8K 컨텍스트 윈도우 및 최대 4K 출력 토큰 생성 지원 (Arena Elo 1340)
- 초당 350 토큰의 빠른 처리 속도와 200ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $0.2 / 출력 $0)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Limitations & Trade-offs
- 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
- 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다
Editorial Assessment
지능 지수 78/100점, 8K 컨텍스트, 초당 350 토큰의 속도를 갖춘 Gemini Embedding 2 모델은 Google 생태계에서 성능과 경제성을 모두 만족시키는 검증된 선택입니다.
Best for: 고차원 시맨틱 벡터 검색, 문서 인덱싱 및 엔터프라이즈 RAG 파이프라인, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.
Compare Gemini Embedding 2
Frequently Asked Questions about Gemini Embedding 2
How much does the Gemini Embedding 2 API cost per 1M tokens?+
Gemini Embedding 2 by Google is priced at $0.2 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Gemini Embedding 2 pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Gemini Embedding 2's context window?+
Gemini Embedding 2 features a 8K tokens context window, which equals approximately 6,144 English words or ~16 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.
Can I run Gemini Embedding 2 locally on my own GPU (Open Weights)?+
No, Gemini Embedding 2 is a proprietary closed-weights model hosted by Google. It cannot be downloaded for local GPU execution and must be accessed via Google's official API or cloud platform.
Does Google train on data sent to the Gemini Embedding 2 API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (Google Terms). Full commercial ownership of generated outputs. Supported deployment channels include: Google AI Studio API, Google Cloud Vertex AI.
How does Gemini Embedding 2 perform on coding and reasoning benchmarks?+
Gemini Embedding 2 achieves a composite Intelligence Index of 78/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, N/A% on MMLU-Pro, and 1340 pts on Chatbot Arena Elo.
What is Gemini Embedding 2 best used for?+
Gemini Embedding 2 is best suited for: 고차원 시맨틱 벡터 검색, 문서 인덱싱 및 엔터프라이즈 RAG 파이프라인, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.. Assessment: 지능 지수 78/100점, 8K 컨텍스트, 초당 350 토큰의 속도를 갖춘 Gemini Embedding 2 모델은 Google 생태계에서 성능과 경제성을 모두 만족시키는 검증된 선택입니다.
Google Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Google's flagship reasoning tier:
Gemini 4 Argon
Gemini 3.8 Flash
Gemini 3.7 Flash
Gemini 3.6 Flash
Gemini 3.5 Flash-Lite
Gemini Omni Flash
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Gemini Embedding 2:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
A multimodal neural network engineered to jointly process, reason over, and synthesize both visual content (images, diagrams, UI screenshots, video) and natural language text within a single framework.
The neural network architecture introduced in 2017 that powers modern LLMs, using self-attention mechanisms to process all tokens in a sequence concurrently rather than sequentially.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.