Gemini Embedding 2 vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini Embedding 2 by Google against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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Gemini Embedding 2
고성능 AI 모델Google의 Gemini Embedding 2 모델은 8K 토큰 컨텍스트 윈도우와 입력 $0.2/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.20 / 1M
$0.00 / 1M
8.2K tokens
N/A
Claude Opus 5.5
심층 추론 모델Anthropic의 Claude Opus 5.5 모델은 1M 토큰 컨텍스트 윈도우와 입력 $4.0/1M 토큰, 출력 $20.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
한눈에 보는 핵심 차이점
- Gemini Embedding 2 is 82.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 8K tokens).
- Gemini Embedding 2 is faster at 350 tok/s vs 78 tok/s.
| Feature | Gemini Embedding 2 | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 94 |
Provider | Anthropic | |
Input Price Cost per million input tokens | $0.20 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.04 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 350 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 84.6% |
GPQA Diamond Graduate-level reasoning | N/A | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Embedding 2 · Strengths & Key Advantages
- Google의 첨단 Unified multimodal joint-embedding transformer 아키텍처와 최대 8K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.2/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Claude Opus 5.5 · Strengths & Key Advantages
- Anthropic의 첨단 Extended Autonomous Hybrid Reasoning Transformer 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $4.0/1M, 출력 $20.0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 2 | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.08 | $3.85 |
Output Tokens / Task Tokens generated per task | 0K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Embedding 2 | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-2 | claude-opus-5-5-20260901 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | February 2026 | June 2026 |
| Architecture | Unified multimodal joint-embedding transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 8K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Custom Model Tuning via Amazon Bedrock & Prompt 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) · Vision (~258–765 tokens per image tile) |
| 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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, vision |
| Output Modalities | embedding | text |
Gemini Embedding 2 vs Claude Opus 5.5: 핵심 요약 문답 (FAQ)
Which is more intelligent, Gemini Embedding 2 or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 78 on the composite Intelligence Index.
Which model is faster?
Gemini Embedding 2 is faster, generating 350 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Gemini Embedding 2 is cheaper at $0.04 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini Embedding 2 has lower latency, with a time to first token of 0.20s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 8.2K tokens.
종합 평가
Choose Gemini Embedding 2 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 google 기반 프로덕션 파이프라인, or choose Claude Opus 5.5 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 anthropic 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: Gemini Embedding 2 or Claude Opus 5.5?+
Gemini Embedding 2 costs $0.2/1M input and $0/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Gemini Embedding 2 or Claude Opus 5.5?+
Gemini Embedding 2 supports 8K tokens whereas Claude Opus 5.5 supports 1M tokens.
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.
공식 출처: Google & Anthropic Official Documentation