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Gemini Embedding 2 vs Gemini Embedding 001

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini Embedding 2 vs Gemini Embedding 001: Benchmarks, Pricing & Speed Comparison

Google 的 Gemini Embedding 2 与 Google 的 Gemini Embedding 001 是当前备受关注的两款主力 AI 模型。Gemini Embedding 2 提供 8.2K Token 上下文窗口，API 定价为每百万 Token 输入 $0.20 / 输出 $0.00；而 Gemini Embedding 001 提供 2K Token 上下文窗口，定价为每百万 Token 输入 $0.15 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。

模型直接对比 (VS)

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Google

Gemini Embedding 2

高维向量嵌入与检索

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）。

Intelligence Index78 / 100
Input Token:

$0.20 / 1M

Output Token:

$0.00 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

N/A

Google

Gemini Embedding 001

高维向量嵌入与检索

Google 推出的 Gemini Embedding 001 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 2K 上下文窗口、400 Token/秒生成速度与 70/100 综合智能指数（每百万 Token $0.15 输入 / $0 输出）。

Intelligence Index70 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.00 / 1M

Context Window:

2K tokens

SWE-bench Verified:

N/A

核心差异一览

  • Gemini Embedding 001 is 1.3x cheaper for API usage (input + output tokens combined).
  • Gemini Embedding 2 has a larger context window (8K vs 2K tokens).
  • Gemini Embedding 001 is faster at 400 tok/s vs 350 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini Embedding 2Gemini Embedding 001
Intelligence Index
Composite reasoning & knowledge score (0-100)
7870
Provider
GoogleGoogle
Input Price
Cost per million input tokens
$0.20 / 1M$0.15 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.04 / 1M$0.03 / 1M
Context Window
Maximum input + output tokens
8.2K tokens~12 A4 pages of text2K tokens~3 A4 pages of text
Max Output Tokens
Per single request
4K4K
Throughput Speed
Tokens generated per second
350 tok/s400 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A

各项指标谁更胜一筹？

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Gemini Embedding 001
1.3x cheaper monthly runtime
Speed
Gemini Embedding 001
+14% faster generation
Context
Gemini Embedding 2
8.2K tokens window

权威基准测试得分对比

Official evaluation metrics verified by independent third-party research labs.

Gemini Embedding 2Gemini Embedding 001
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1340ptsvs1280pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

350 tok/s vs400 tok/s

延迟与响应速度

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Gemini Embedding 20.20s
Gemini Embedding 0010.15s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini Embedding 21.63s
Gemini Embedding 0011.40s

实时 Token 成本模拟器

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemini Embedding 2 Monthly API Bill
$0.30 / month
$0.2/1M in · $0/1M out
Gemini Embedding 001 Monthly API Bill
$0.22 / month
$0.15/1M in · $0/1M out
Gemini Embedding 001 saves approximately $0.08 / month (25%) under this workload.1.3x Savings

Gemini Embedding 2 · Strengths & Key Advantages

  • 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
Considerations & 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

Gemini Embedding 001 · Strengths & Key Advantages

  • 提供 2K 上下文窗口与最高 4K 输出容量，核心基准表现优异（Arena Elo 1280）
  • 高达 400 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 150 毫秒
  • 极具竞争力的 API 定价（每百万 Token $0.15 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
Considerations & 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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGemini Embedding 2Gemini Embedding 001
Cost per Task
Estimated USD to finish one task
$0.08$0.05
Output Tokens / Task
Tokens generated per task
0K0K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureGemini Embedding 2Gemini Embedding 001
Official API Model ID (SDK)models/gemini-embedding-2models/gemini-embedding-001
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-042025-05
Knowledge CutoffFebruary 2026March 2025
ArchitectureUnified multimodal joint-embedding transformerDense semantic text embedding transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.2/1M in) across full 8K windowFlat rate ($0.15/1M in) across full 2K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0/1M with configurable TTLImplicit + Explicit Context Caching at $0/1M with configurable TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Text-only completion (No GUI action loop)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyPrompt-based JSON extraction only
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationVertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationGoogle DeepMind Frontier Safety Framework VerifiedGoogle DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, vision, audio, videotext
Output Modalitiesembeddingembedding

Gemini Embedding 2 vs Gemini Embedding 001: 常见问题解答 (FAQ)

Which is more intelligent, Gemini Embedding 2 or Gemini Embedding 001?

Gemini Embedding 2 is more intelligent. It scores 78 compared with 70 on the composite Intelligence Index.

Which model is faster?

Gemini Embedding 001 is faster, generating 400 tokens per second compared with 350 tokens per second.

Which model is cheaper to run?

Gemini Embedding 001 is cheaper at $0.03 per 1M tokens blended, compared with $0.04 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini Embedding 001 has lower latency, with a time to first token of 0.15s compared with 0.20s.

Which model has a larger context window?

Gemini Embedding 2 has a larger context window, supporting 8.2K tokens compared with 2K tokens.

综合结论

综合来看，Gemini Embedding 2（适合：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。）与 Gemini Embedding 001（适合：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。）各具优势：Gemini Embedding 2 在综合智能与编程基准测试中表现更强，Gemini Embedding 001 拥有更快的 Token 生成速度，而 Gemini Embedding 001 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面，Gemini Embedding 2 和 Gemini Embedding 001 哪个更强？+

在综合智能指数与编程基准评测中，Gemini Embedding 2 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Gemini Embedding 2 与 Gemini Embedding 001 的 API 价格哪个更便宜？+

Gemini Embedding 2 的 API 价格为输入 $0.20 / 输出 $0.00（每百万 Token），Gemini Embedding 001 为输入 $0.15 / 输出 $0.00。综合混合成本来看，Gemini Embedding 001 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度？+

Gemini Embedding 2 支持 8.2K Token 上下文窗口，而 Gemini Embedding 001 支持 2K Token。在流式生成速度方面，Gemini Embedding 001 的吞吐表现更快。

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

数据来源: Google 与 Google 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。