Claude Opus 4.1 vs Gemini 2.5 Computer Use
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude Opus 4.1 by Anthropic and Gemini 2.5 Computer Use 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 2.5 Computer Use holds a notable pricing advantage for token ingestion. On output generation, Gemini 2.5 Computer Use is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude Opus 4.1 provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, Gemini 2.5 Computer Use 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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Claude Opus 4.1
高度な推論モデルAnthropic が提供する Claude Opus 4.1 は、最大 200K トークンのコンテキストウィンドウを備え、入力 $15/100万トークン・出力 $75/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$15.00 / 1M
$75.00 / 1M
200K tokens
74.5%
Gemini 2.5 Computer Use
高度な推論モデルGoogle が提供する Gemini 2.5 Computer Use は、最大 128K トークンのコンテキストウィンドウを備え、入力 $1.5/100万トークン・出力 $8/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$1.50 / 1M
$8.00 / 1M
128K tokens
N/A
ひと目でわかる主な違い
- Gemini 2.5 Computer Use is 9.2x cheaper for API usage (input + output tokens combined).
- Claude Opus 4.1 has a larger context window (200K vs 128K tokens).
- Gemini 2.5 Computer Use is faster at 85 tok/s vs 46 tok/s.
| Feature | Claude Opus 4.1 | Gemini 2.5 Computer Use |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 62 | 65 |
Provider | Anthropic | |
Input Price Cost per million input tokens | $15.00 / 1M | $1.50 / 1M |
Output Price Cost per million output tokens | $75.00 / 1M | $8.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $11.55 / 1M | $1.36 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 32K | 16K |
Throughput Speed Tokens generated per second | 46 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 74.5% | N/A |
GPQA Diamond Graduate-level reasoning | 74.4% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $1.50 / 1M | $0.38 / 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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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
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Claude Opus 4.1 · Strengths & Key Advantages
- Anthropic の高度な Refined Frontier Reasoning Transformer アーキテクチャと 200K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $15/1M、出力 $75/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Gemini 2.5 Computer Use · Strengths & Key Advantages
- Google の高度な Action-grounded vision-language agent model アーキテクチャと 128K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $1.5/1M、出力 $8/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Opus 4.1 | Gemini 2.5 Computer Use |
|---|---|---|
Cost per Task Estimated USD to finish one task | $16.46 | $1.15 |
Output Tokens / Task Tokens generated per task | 72K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 6K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Opus 4.1 | Gemini 2.5 Computer Use |
|---|---|---|
| Official API Model ID (SDK) | claude-opus-4-1-20250801 | models/gemini-2-5-computer-use |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-08 | 2025-10 |
| Knowledge Cutoff | March 2025 | June 2025 |
| Architecture | Refined Frontier Reasoning Transformer | Action-grounded vision-language agent model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 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) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($1.5/1M in) across full 128K window |
| Prompt Cache Policy & TTL | $1.5/1M cached read (90% off); 5-min default & 1-hour extended TTL | Implicit + Explicit Context Caching at $0.375/1M with configurable 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 | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search Citations + Code Execution Tool + MCP Connectors | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | 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 | text, vision |
| Output Modalities | text | text |
Claude Opus 4.1 vs Gemini 2.5 Computer Use: よくある質問 (FAQ)
Which is more intelligent, Claude Opus 4.1 or Gemini 2.5 Computer Use?
Gemini 2.5 Computer Use is more intelligent. It scores 65 compared with 62 on the composite Intelligence Index.
Which model is faster?
Gemini 2.5 Computer Use is faster, generating 85 tokens per second compared with 46 tokens per second.
Which model is cheaper to run?
Gemini 2.5 Computer Use is cheaper at $1.36 per 1M tokens blended, compared with $11.55 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 2.5 Computer Use has lower latency, with a time to first token of 0.65s compared with 1.70s.
Which model has a larger context window?
Claude Opus 4.1 has a larger context window, supporting 200K tokens compared with 128K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Claude Opus 4.1 if your priority is Automated bug-fixing swarms, complex architecture refactoring, and academic research synthesis. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 2.5 Computer Use if your workflow prioritizes Autonomous web scraping, QA test automation, and RPA desktop workflows. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Claude Opus 4.1 compare to Gemini 2.5 Computer Use in API token pricing and cost per token?+
Claude Opus 4.1 costs $15.00 per 1M input tokens and $75.00 per 1M output tokens, whereas Gemini 2.5 Computer Use costs $1.50 input and $8.00 output. Overall, Gemini 2.5 Computer Use is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Claude Opus 4.1 or Gemini 2.5 Computer Use?+
Claude Opus 4.1 supports a context window of 200K tokens (~150k words), while Gemini 2.5 Computer Use supports 128K tokens (~96k words). Therefore, Claude Opus 4.1 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Claude Opus 4.1 or Gemini 2.5 Computer Use?+
Claude Opus 4.1 scores 74.5% on SWE-bench Verified, while Gemini 2.5 Computer Use scores competitively for software generation. For complex autonomous debugging and agentic engineering, Claude Opus 4.1 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Claude Opus 4.1 and Gemini 2.5 Computer Use?+
Claude Opus 4.1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 2.5 Computer Use provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Claude Opus 4.1 and Gemini 2.5 Computer Use?+
Claude Opus 4.1 generates approximately 46 tokens per second, while Gemini 2.5 Computer Use delivers 85 tokens per second. In latency-sensitive production environments, Gemini 2.5 Computer Use provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Claude Opus 4.1 and Gemini 2.5 Computer Use to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 2.5 Computer Use to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Opus 4.1. 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.
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