Gemini Embedding 001 vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini Embedding 001 by Google against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Gemini Embedding 001
ベクトル埋め込み・RAG検索Google の Gemini Embedding 001 は、高次元ベクトル検索、セマンティック検索、高精度なRAG（検索拡張生成）構築向けに設計されたAIモデルです。2K のコンテキストウィンドウ、毎秒 400 トークンの生成速度、知能指数 70/100（1Mトークンあたり入力$0.15・出力$0）を実現しています。
$0.15 / 1M
$0.00 / 1M
2K tokens
N/A
Claude Opus 5.5
フラッグシップ最先端モデルAnthropic の Claude Opus 5.5 は、最先端のエンタープライズ推論、ソフトウェア工学、ミッションクリティカルな本番運用向けに設計されたAIモデルです。1M のコンテキストウィンドウ、毎秒 78 トークンの生成速度、知能指数 94/100（1Mトークンあたり入力$4・出力$20）を実現しています。
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
ひと目でわかる主な違い
- Gemini Embedding 001 is 109.3x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 2K tokens).
- Gemini Embedding 001 is faster at 400 tok/s vs 78 tok/s.
| Feature | Gemini Embedding 001 | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 70 | 94 |
Provider | Anthropic | |
Input Price Cost per million input tokens | $0.15 / 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 | $0.03 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 2K tokens~3 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 | 400 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 |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Embedding 001 · Strengths & Key Advantages
- 2K のコンテキストウィンドウと最大 4K トークンの出力容量（Arena Elo 1280）
- 毎秒 400 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 150ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$0.15・出力$0）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Claude Opus 5.5 · Strengths & Key Advantages
- 1M のコンテキストウィンドウと最大 128K トークンの出力容量（SWE-bench Verified 84.6% · GPQA Diamond 82.3%）
- 毎秒 78 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 1400ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$4・出力$20）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 大量のトークンを処理する本番環境では、プロンプトキャッシュや Batch API の併用によるコスト最適化が推奨されます
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 001 | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $3.85 |
Output Tokens / Task Tokens generated per task | 0K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Embedding 001 | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-001 | claude-opus-5-5-20260901 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-05 | 2026-09 |
| Knowledge Cutoff | March 2025 | June 2026 |
| Architecture | Dense semantic text 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 ($0.15/1M in) across full 2K 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 | Text-only completion (No GUI action loop) | 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) | 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 | text, vision |
| Output Modalities | embedding | text |
Gemini Embedding 001 vs Claude Opus 5.5: よくある質問 (FAQ)
Which is more intelligent, Gemini Embedding 001 or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 70 on the composite Intelligence Index.
Which model is faster?
Gemini Embedding 001 is faster, generating 400 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Gemini Embedding 001 is cheaper at $0.03 per 1M tokens blended, compared with $3.08 (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 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 2K tokens.
総合評価
Choose Gemini Embedding 001 for 高次元ベクトル検索、セマンティック検索、高精度なrag（検索拡張生成）構築、エンタープライズrag基盤、スケーラブルな本番api統合に最適。, or choose Claude Opus 5.5 for 最先端のエンタープライズ推論、ソフトウェア工学、ミッションクリティカルな本番運用、エンタープライズrag基盤、スケーラブルな本番api統合に最適。.
よくある質問 (FAQ)
Which is cheaper: Gemini Embedding 001 or Claude Opus 5.5?+
Gemini Embedding 001 costs $0.15/1M 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 001 or Claude Opus 5.5?+
Gemini Embedding 001 supports 2K 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.
公的ソース: Google & Anthropic Official Documentation