Claude 3.7 Sonnet vs GPT-4o
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Claude 3.7 Sonnet by Anthropic and GPT-4o by OpenAI 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, GPT-4o holds a notable pricing advantage for token ingestion. On output generation, GPT-4o is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude 3.7 Sonnet provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, GPT-4o 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.
원하는 AI 모델 선택 및 실시간 비교
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Claude 3.7 Sonnet
심층 추론 모델Anthropic의 Claude 3.7 Sonnet 모델은 200K 토큰 컨텍스트 윈도우와 입력 $3/1M 토큰, 출력 $15/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
GPT-4o
플래그십 프론티어 모델OpenAI의 GPT-4o 모델은 128K 토큰 컨텍스트 윈도우와 입력 $2.5/1M 토큰, 출력 $10/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$2.50 / 1M
$10.00 / 1M
128K tokens
38.8%
한눈에 보는 핵심 차이점
- GPT-4o is 1.3x cheaper for API usage (input + output tokens combined).
- Claude 3.7 Sonnet has a larger context window (200K vs 128K tokens).
- GPT-4o is faster at 105 tok/s vs 72 tok/s.
- Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 38.8%).
| Feature | Claude 3.7 Sonnet | GPT-4o |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 41 |
Provider | Anthropic | OpenAI |
Input Price Cost per million input tokens | $3.00 / 1M | $2.50 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.31 / 1M | $2.38 / 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 | 64K | 16K |
Throughput Speed Tokens generated per second | 72 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 70.3% | 38.8% |
GPQA Diamond Graduate-level reasoning | 65% | 53.6% |
Cached Input Price Prompt caching discount per 1M tokens | $0.30 / 1M | $1.25 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 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.
Claude 3.7 Sonnet · Strengths & Key Advantages
- Anthropic의 첨단 Hybrid Reasoning MoE 아키텍처와 최대 200K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $3/1M, 출력 $15/1M)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
GPT-4o · Strengths & Key Advantages
- OpenAI의 첨단 Omni-Multimodal Dense 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2.5/1M, 출력 $10/1M)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 | Claude 3.7 Sonnet | GPT-4o |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.42 | $0.68 |
Output Tokens / Task Tokens generated per task | 41K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude 3.7 Sonnet | GPT-4o |
|---|---|---|
| Official API Model ID (SDK) | claude-3-7-sonnet-20250201 | gpt-4o |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-02 | 2024-05 |
| Knowledge Cutoff | November 2024 | October 2023 |
| Architecture | Hybrid Reasoning MoE | Omni-Multimodal Dense |
| 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 ($2.5/1M in) across full 128K window |
| Prompt Cache Policy & TTL | $0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL | Automatic prefix caching at $1.25/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) · Audio (~25–32 tokens/sec) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published |
| 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, audio |
| Output Modalities | text | text, audio |
Claude 3.7 Sonnet vs GPT-4o: 핵심 요약 문답 (FAQ)
Which is more intelligent, Claude 3.7 Sonnet or GPT-4o?
Claude 3.7 Sonnet is more intelligent. It scores 48 compared with 41 on the composite Intelligence Index.
Which model is faster?
GPT-4o is faster, generating 105 tokens per second compared with 72 tokens per second.
Which model is cheaper to run?
Claude 3.7 Sonnet is cheaper at $2.31 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.52s.
Which model has a larger context window?
Claude 3.7 Sonnet has a larger context window, supporting 200K tokens compared with 128K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose Claude 3.7 Sonnet if your priority is Full-stack software architecture, complex refactoring, and agentic workflows. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4o if your workflow prioritizes Omni-modal voice applications, conversational agents, and structured enterprise APIs. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does Claude 3.7 Sonnet compare to GPT-4o in API token pricing and cost per token?+
Claude 3.7 Sonnet costs $3.00 per 1M input tokens and $15.00 per 1M output tokens, whereas GPT-4o costs $2.50 input and $10.00 output. Overall, GPT-4o 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 3.7 Sonnet or GPT-4o?+
Claude 3.7 Sonnet supports a context window of 200K tokens (~150k words), while GPT-4o supports 128K tokens (~96k words). Therefore, Claude 3.7 Sonnet can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Claude 3.7 Sonnet or GPT-4o?+
Claude 3.7 Sonnet scores 70.3% on SWE-bench Verified while GPT-4o scores 38.8%. For autonomous software engineering and multi-file debugging, Claude 3.7 Sonnet holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Claude 3.7 Sonnet and GPT-4o?+
Claude 3.7 Sonnet features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-4o focuses on standard fast inference.
How does inference speed (tokens per second) compare between Claude 3.7 Sonnet and GPT-4o?+
Claude 3.7 Sonnet generates approximately 72 tokens per second, while GPT-4o delivers 105 tokens per second. In latency-sensitive production environments, GPT-4o provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Claude 3.7 Sonnet and GPT-4o to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-4o to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude 3.7 Sonnet. 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 3.5 Sonnet vs GPT-4o: Benchmarks, Pricing & Speed Comparison비교
- Claude 3.7 Sonnet vs DeepSeek R1: Benchmarks, Pricing & Speed Comparison비교
- Claude 3.7 Sonnet vs o3-mini: Benchmarks, Pricing & Speed Comparison비교
- Claude Sonnet 4 vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed Comparison비교
- DeepSeek V3 vs GPT-4o: Benchmarks, Pricing & Speed Comparison비교
- Gemini 2.0 Pro vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed Comparison비교
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: GetInfoUs Independent AI Benchmarks & Official API Specs