Gemini Embedding 2 vs Gemini Embedding 001
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini Embedding 2 by Google and Gemini Embedding 001 by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini Embedding 001 holds a notable pricing advantage for token ingestion. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Gemini Embedding 2 provides an expanded memory horizon with 8K tokens. In latency-sensitive production environments, Gemini Embedding 001 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Gemini Embedding 2
高性能 AI 模型Google 推出的 Gemini Embedding 2 人工智能模型，具备 8K 上下文窗口，输入价格为 $0.2/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.20 / 1M
$0.00 / 1M
8.2K tokens
N/A
Gemini Embedding 001
高性能 AI 模型Google 推出的 Gemini Embedding 001 人工智能模型，具备 2K 上下文窗口，输入价格为 $0.15/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.15 / 1M
$0.00 / 1M
2K tokens
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.
| Feature | Gemini Embedding 2 | Gemini Embedding 001 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 70 |
Provider | ||
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 text | 2K tokens~3 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 350 tok/s | 400 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
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.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Embedding 2 · Strengths & Key Advantages
- 由 Google 研发的高精度 Unified multimodal joint-embedding transformer 架构，支持 8K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.2/1M，输出 $0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Gemini Embedding 001 · Strengths & Key Advantages
- 由 Google 研发的高精度 Dense semantic text embedding transformer 架构，支持 2K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.15/1M，输出 $0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 2 | Gemini Embedding 001 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.08 | $0.05 |
Output Tokens / Task Tokens generated per task | 0K | 0K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Embedding 2 | Gemini Embedding 001 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-2 | models/gemini-embedding-001 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-04 | 2025-05 |
| Knowledge Cutoff | February 2026 | March 2025 |
| Architecture | Unified multimodal joint-embedding transformer | Dense semantic text embedding transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 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 window | Flat rate ($0.15/1M in) across full 2K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text 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 VRAM | Cloud-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 Classification | Google DeepMind Frontier Safety Framework Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio, video | text |
| Output Modalities | embedding | embedding |
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
Verdict & Model Selection Recommendation: Choose Gemini Embedding 2 if your priority is Cross-modal retrieval, multimodal RAG, and video/audio search engines. Its performance in demanding tasks justifies its operational token cost. Choose Gemini Embedding 001 if your workflow prioritizes Enterprise vector search, document indexing, and semantic clustering. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Gemini Embedding 2 compare to Gemini Embedding 001 in API token pricing and cost per token?+
Gemini Embedding 2 costs $0.20 per 1M input tokens and $0.00 per 1M output tokens, whereas Gemini Embedding 001 costs $0.15 input and $0.00 output. Overall, Gemini Embedding 001 is the more cost-effective model for high-volume API pipelines.
Which model has a larger context window: Gemini Embedding 2 or Gemini Embedding 001?+
Gemini Embedding 2 supports a context window of 8K tokens (~6k words), while Gemini Embedding 001 supports 2K tokens (~2k words). Therefore, Gemini Embedding 2 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini Embedding 2 or Gemini Embedding 001?+
Gemini Embedding 2 scores at the frontier of coding benchmarks, while Gemini Embedding 001 scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini Embedding 2 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemini Embedding 2 and Gemini Embedding 001?+
Gemini Embedding 2 is optimized for low-latency direct generation. In contrast, Gemini Embedding 001 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemini Embedding 2 and Gemini Embedding 001?+
Gemini Embedding 2 generates approximately 350 tokens per second, while Gemini Embedding 001 delivers 400 tokens per second. In latency-sensitive production environments, Gemini Embedding 001 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini Embedding 2 and Gemini Embedding 001 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini Embedding 001 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini Embedding 2. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs