o3 vs Claude Opus 4.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o3 by OpenAI and Claude Opus 4.5 by Anthropic 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, o3 holds a notable pricing advantage for token ingestion. On output generation, o3 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 200K tokens. In latency-sensitive production environments, o3 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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o3
高度な推論モデルOpenAI が提供する o3 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 $2/100万トークン・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。
$2.00 / 1M
$8.00 / 1M
200K tokens
69.1%
Claude Opus 4.5
高度な推論モデルAnthropic が提供する Claude Opus 4.5 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 $5/100万トークン・出力 $25/100万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。
$5.00 / 1M
$25.00 / 1M
200K tokens
77.2%
ひと目でわかる主な違い
- o3 is 2.8x cheaper for API usage (input + output tokens combined).
- o3 is faster at 75 tok/s vs 62 tok/s.
- Claude Opus 4.5 leads on SWE-bench coding (77.2% vs 69.1%).
| Feature | o3 | Claude Opus 4.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 63 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $2.00 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $25.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $3.85 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 100K | 64K |
Throughput Speed Tokens generated per second | 75 tok/s | 62 tok/s |
SWE-bench Verified Real-world software engineering | 69.1% | 77.2% |
GPQA Diamond Graduate-level reasoning | 76.5% | 74.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.50 / 1M |
分野別勝者スコアカード
At-a-glance decision framework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.
o3 · Strengths & Key Advantages
- OpenAI の高度な Reasoning transformer with adjustable thinking effort 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 $2/1M、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
Claude Opus 4.5 · Strengths & Key Advantages
- Anthropic の高度な Hybrid Reasoning 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 $5/1M、出力 $25/1M）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 | o3 | Claude Opus 4.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.35 | $5.86 |
Output Tokens / Task Tokens generated per task | 48K | 73K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 43K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | o3 | Claude Opus 4.5 |
|---|---|---|
| Official API Model ID (SDK) | o3 | claude-opus-4-5-20251101 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-04 | 2025-11 |
| Knowledge Cutoff | June 2024 | March 2025 |
| Architecture | Reasoning transformer with adjustable thinking effort | Hybrid Reasoning |
| 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 200K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | $0.5/1M 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 | 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 tool + Hosted Code Interpreter & File Search RAG | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Custom Model Tuning via Amazon Bedrock & Prompt 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 | OpenAI Preparedness Framework Audited & System Card Published | 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 | text, vision |
| Output Modalities | text | text |
o3 vs Claude Opus 4.5: よくある質問 (FAQ)
Which is more intelligent, o3 or Claude Opus 4.5?
Claude Opus 4.5 is more intelligent. It scores 63 compared with 56 on the composite Intelligence Index.
Which model is faster?
o3 is faster, generating 75 tokens per second compared with 62 tokens per second.
Which model is cheaper to run?
o3 is cheaper at $1.55 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Opus 4.5 has lower latency, with a time to first token of 2.10s compared with 2.80s.
総合評価
Verdict & Model Selection Recommendation: Choose o3 if your priority is Hard maths, science and agentic coding where accuracy beats latency. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 4.5 if your workflow prioritizes Complex reasoning, creative work, and high-stakes decision making where quality outweighs cost. 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 o3 compare to Claude Opus 4.5 in API token pricing and cost per token?+
o3 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens, whereas Claude Opus 4.5 costs $5.00 input and $25.00 output. Overall, o3 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: o3 or Claude Opus 4.5?+
Both o3 and Claude Opus 4.5 offer an identical context window of 200K tokens (~150k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: o3 or Claude Opus 4.5?+
o3 scores 69.1% on SWE-bench Verified while Claude Opus 4.5 scores 77.2%. For autonomous software engineering and multi-file debugging, Claude Opus 4.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o3 and Claude Opus 4.5?+
o3 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 4.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o3 and Claude Opus 4.5?+
o3 generates approximately 75 tokens per second, while Claude Opus 4.5 delivers 62 tokens per second. In latency-sensitive production environments, o3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o3 and Claude Opus 4.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to o3 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Opus 4.5. 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.
Related reading
- Claude Sonnet 4.6 vs Claude Opus 4.5: Benchmarks, Pricing & Speed Comparison比較
- Command A Reasoning vs o3: Benchmarks, Pricing & Speed Comparison比較
- GPT-5.1 vs Claude Opus 4.5: Benchmarks, Pricing & Speed Comparison比較
- GPT-5.2 Chat vs Claude Opus 4.5: Benchmarks, Pricing & Speed Comparison比較
- Grok 4.20 (Reasoning) vs o3: Benchmarks, Pricing & Speed Comparison比較
- Kimi K2 Thinking Turbo vs Claude Opus 4.5: Benchmarks, Pricing & Speed Comparison比較
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: GetInfoUs Independent AI Benchmarks & Official API Specs