Google 推出的 Gemini Embedding 2 是一款专为高维向量表征、语义检索与企业级 RAG 知识库构建的大模型，具备 8K 上下文窗口、350 Token/秒生成速度与 78/100 综合智能指数（每百万 Token $0.2 输入 / $0 输出）。
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
- 提供 8K 上下文窗口与最高 4K 输出容量，核心基准表现优异（Arena Elo 1340）
- 高达 350 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 200 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.2 输入 / $0 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Limitations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Editorial Assessment
Gemini Embedding 2 凭借 78/100 的智能得分、8K 上下文容量与 350 Token/秒的响应速度，在 Google 模型矩阵中提供了极佳的性能与成本平衡。
Best for: 专为高维向量表征、语义检索与企业级 RAG 知识库构建的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。
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: 专为高维向量表征、语义检索与企业级 RAG 知识库构建的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。. Assessment: Gemini Embedding 2 凭借 78/100 的智能得分、8K 上下文容量与 350 Token/秒的响应速度，在 Google 模型矩阵中提供了极佳的性能与成本平衡。
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.