GPT-5.2 Pro vs Claude Opus 4.7
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.2 Pro da OpenAI e Claude Opus 4.7 da Anthropic são dois modelos de IA de destaque. O GPT-5.2 Pro oferece uma janela de contexto de 400K tokens a $17.50/$140.00 por milhão de tokens, enquanto o Claude Opus 4.7 oferece 1M tokens de contexto a $5.00/$25.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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GPT-5.2 Pro
Motor de Raciocínio ProfundoO GPT-5.2 Pro da OpenAI é um modelo de IA projetado para raciocínio lógico profundo, matemática avançada e análise científica passo a passo, oferecendo janela de contexto de 400K, velocidade de 50 tokens/s, índice de inteligência de 69/100
$17.50 / 1M
$140.00 / 1M
400K tokens
79.4%
Claude Opus 4.7
Modelo Principal de FronteiraO Claude Opus 4.7 da Anthropic é um modelo de IA projetado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, oferecendo janela de contexto de 1M, velocidade de 58 tokens/s, índice de inteligência de 66/100
$5.00 / 1M
$25.00 / 1M
1M tokens
79.5%
Principais Diferenças em Resumo
- Claude Opus 4.7 is 5.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 4.7 has a larger context window (1M vs 400K tokens).
- Claude Opus 4.7 is faster at 58 tok/s vs 50 tok/s.
- Claude Opus 4.7 leads on SWE-bench coding (79.5% vs 79.4%).
| Feature | GPT-5.2 Pro | Claude Opus 4.7 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 69 | 66 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $17.50 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $140.00 / 1M | $25.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $18.73 / 1M | $3.85 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 50 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 79.4% | 79.5% |
GPQA Diamond Graduate-level reasoning | 79% | 77.8% |
Cached Input Price Prompt caching discount per 1M tokens | $1.75 / 1M | $0.50 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.2 Pro · Strengths & Key Advantages
- Janela de contexto de 400K com limite máximo de saída de 128K tokens (SWE-bench Verified 79.4% · GPQA Diamond 79%)
- Taxa de transferência rápida de 50 tokens/segundo com latência inicial (TTFT) de 4400 ms
- Economia de API competitiva ($17.5/$140 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Claude Opus 4.7 · Strengths & Key Advantages
- Janela de contexto de 1M com limite máximo de saída de 128K tokens (SWE-bench Verified 79.5% · GPQA Diamond 77.8%)
- Taxa de transferência rápida de 58 tokens/segundo com latência inicial (TTFT) de 1850 ms
- Economia de API competitiva ($5/$25 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.2 Pro | Claude Opus 4.7 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $23.24 | $5.49 |
Output Tokens / Task Tokens generated per task | 70K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 46K | 37K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.2 Pro | Claude Opus 4.7 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-2-pro | claude-opus-4-7-20260401 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-12 | 2026-04 |
| Knowledge Cutoff | August 2025 | February 2026 |
| Architecture | Maximum-compute unified reasoning model | Adaptive Enterprise 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 ($17.5/1M in) across full 400K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $1.75/1M (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 |
GPT-5.2 Pro vs Claude Opus 4.7: Respostas Rápidas (FAQ)
Which is more intelligent, GPT-5.2 Pro or Claude Opus 4.7?
GPT-5.2 Pro is more intelligent. It scores 69 compared with 66 on the composite Intelligence Index.
Which model is faster?
Claude Opus 4.7 is faster, generating 58 tokens per second compared with 50 tokens per second.
Which model is cheaper to run?
Claude Opus 4.7 is cheaper at $3.85 per 1M tokens blended, compared with $18.73 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Opus 4.7 has lower latency, with a time to first token of 1.85s compared with 4.40s.
Which model has a larger context window?
Claude Opus 4.7 has a larger context window, supporting 1M tokens compared with 400K tokens.
Veredito
Tanto GPT-5.2 Pro (Indicado para raciocínio lógico profundo, matemática avançada e análise científica passo a passo, arquiteturas RAG corporativas e integração escalável via API.) quanto Claude Opus 4.7 (Indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: GPT-5.2 Pro lidera em inteligência geral e programação, Claude Opus 4.7 oferece geração mais rápida e Claude Opus 4.7 apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, GPT-5.2 Pro ou Claude Opus 4.7?+
Nas avaliações de inteligência e código, GPT-5.2 Pro leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, GPT-5.2 Pro ou Claude Opus 4.7?+
O GPT-5.2 Pro custa $17.50 / $140.00 por 1M de tokens (entrada/saída) e o Claude Opus 4.7 custa $5.00 / $25.00. No custo combinado, Claude Opus 4.7 é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O GPT-5.2 Pro suporta 400K tokens de contexto contra 1M tokens do Claude Opus 4.7, enquanto Claude Opus 4.7 entrega maior velocidade de geração.
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.
Fontes oficiais: Documentação oficial de API da OpenAI e Anthropic e benchmarks independentes (2026).