GPT-5.2 Chat vs Claude Opus 4.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5.2 Chat 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, GPT-5.2 Chat holds a notable pricing advantage for token ingestion. On output generation, GPT-5.2 Chat is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude Opus 4.5 provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, GPT-5.2 Chat 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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GPT-5.2 Chat
심층 추론 모델OpenAI의 GPT-5.2 Chat 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
128K tokens
75.4%
Claude Opus 4.5
심층 추론 모델Anthropic의 Claude Opus 4.5 모델은 200K 토큰 컨텍스트 윈도우와 입력 $5/1M 토큰, 출력 $25/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$5.00 / 1M
$25.00 / 1M
200K tokens
77.2%
한눈에 보는 핵심 차이점
- GPT-5.2 Chat is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 4.5 has a larger context window (200K vs 128K tokens).
- GPT-5.2 Chat is faster at 135 tok/s vs 62 tok/s.
- Claude Opus 4.5 leads on SWE-bench coding (77.2% vs 75.4%).
| Feature | GPT-5.2 Chat | Claude Opus 4.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 63 | 63 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $0.00 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $25.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $3.85 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 16K | 64K |
Throughput Speed Tokens generated per second | 135 tok/s | 62 tok/s |
SWE-bench Verified Real-world software engineering | 75.4% | 77.2% |
GPQA Diamond Graduate-level reasoning | 76.5% | 74.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.50 / 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.
GPT-5.2 Chat · Strengths & Key Advantages
- OpenAI의 첨단 Chat-tuned unified reasoning model 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 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 4.5 · Strengths & Key Advantages
- Anthropic의 첨단 Hybrid Reasoning 아키텍처와 최대 200K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $5/1M, 출력 $25/1M)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 | GPT-5.2 Chat | Claude Opus 4.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.90 | $5.86 |
Output Tokens / Task Tokens generated per task | 54K | 73K |
Reasoning Tokens / Task Thinking tokens burned per task | 29K | 43K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.2 Chat | Claude Opus 4.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-2-chat | claude-opus-4-5-20251101 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-12 | 2025-11 |
| Knowledge Cutoff | August 2025 | March 2025 |
| Architecture | Chat-tuned unified reasoning model | 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) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | $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 | 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 |
GPT-5.2 Chat vs Claude Opus 4.5: 핵심 요약 문답 (FAQ)
Which model is faster?
GPT-5.2 Chat is faster, generating 135 tokens per second compared with 62 tokens per second.
Which model is cheaper to run?
GPT-5.2 Chat is cheaper at $0.00 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.2 Chat has lower latency, with a time to first token of 1.10s compared with 2.10s.
Which model has a larger context window?
Claude Opus 4.5 has a larger context window, supporting 200K tokens compared with 128K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose GPT-5.2 Chat if your priority is Everyday conversational use in ChatGPT. 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.
자주 묻는 질문 (FAQ)
How does GPT-5.2 Chat compare to Claude Opus 4.5 in API token pricing and cost per token?+
GPT-5.2 Chat costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Claude Opus 4.5 costs $5.00 input and $25.00 output. Overall, GPT-5.2 Chat 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: GPT-5.2 Chat or Claude Opus 4.5?+
GPT-5.2 Chat supports a context window of 128K tokens (~96k words), while Claude Opus 4.5 supports 200K tokens (~150k words). Therefore, Claude Opus 4.5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.2 Chat or Claude Opus 4.5?+
GPT-5.2 Chat scores 75.4% 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 GPT-5.2 Chat and Claude Opus 4.5?+
GPT-5.2 Chat 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 GPT-5.2 Chat and Claude Opus 4.5?+
GPT-5.2 Chat generates approximately 135 tokens per second, while Claude Opus 4.5 delivers 62 tokens per second. In latency-sensitive production environments, GPT-5.2 Chat provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.2 Chat and Claude Opus 4.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.2 Chat 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
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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: GetInfoUs Independent AI Benchmarks & Official API Specs