Head-to-Head Model Comparison

GPT-5.6 Sol vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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GPT-5.6 SolProfile

Claude Opus 5.5Profile

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5-Model Matrix Workbench

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OpenAI

GPT-5.6 Sol

Modelo Principal de Fronteira

O GPT-5.6 Sol da OpenAI é um modelo de IA projetado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, oferecendo janela de contexto de 1.1M, velocidade de 108 tokens/s, índice de inteligência de 89/100 e t

Intelligence Index89 / 100

Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

83.8%

Anthropic

Claude Opus 5.5

Modelo Principal de Fronteira

O Claude Opus 5.5 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 78 tokens/s, índice de inteligência de 94/100

Intelligence Index94 / 100

Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Principais Diferenças em Resumo

Both models cost the same under this workload — choose on capability, not price.
GPT-5.6 Sol has a larger context window (1M vs 1M tokens).
GPT-5.6 Sol is faster at 108 tok/s vs 78 tok/s.
Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 83.8%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	GPT-5.6 Sol	Claude Opus 5.5
Intelligence Index Composite reasoning & knowledge score (0-100)	89	94
Provider	OpenAI	Anthropic
Input Price Cost per million input tokens	$4.00 / 1M	$4.00 / 1M
Output Price Cost per million output tokens	$20.00 / 1M	$20.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$3.08 / 1M	$3.08 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,575 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	108 tok/s	78 tok/s
SWE-bench Verified Real-world software engineering	83.8%	84.6%
GPQA Diamond Graduate-level reasoning	82%	82.3%
Cached Input Price Prompt caching discount per 1M tokens	$0.40 / 1M	$0.40 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Claude Opus 5.5

+0.8% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

GPT-5.6 Sol

+38% faster generation

Context

GPT-5.6 Sol

1.05M tokens window

Pontuações de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

GPT-5.6 SolClaude Opus 5.5

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

83.8%vs84.6%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

82%vs82.3%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

90.8%vs91.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1496ptsvs1507pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

17.8%vs19.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

61%vs66.4%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

55%vs58.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

80.6%vs83.1%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

95.8%vs95.4%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

89.6%vs92.6%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

108 tok/s vs78 tok/s

Latência e Tempo de Resposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

GPT-5.6 Sol1.21s

Claude Opus 5.51.40s

Est. End-to-End Response (500 tokens)TTFT + generation time

GPT-5.6 Sol5.84s

Claude Opus 5.57.81s

Simulador Interativo de Custo de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

GPT-5.6 Sol Monthly API Bill

$24.60 / month

$4/1M in · $20/1M out

Claude Opus 5.5 Monthly API Bill

$24.60 / month

$4/1M in · $20/1M out

These models cost the same under this workload — no savings either way.Break-even

GPT-5.6 Sol · Strengths & Key Advantages

Janela de contexto de 1.1M com limite máximo de saída de 128K tokens (SWE-bench Verified 83.8% · GPQA Diamond 82%)
Taxa de transferência rápida de 108 tokens/segundo com latência inicial (TTFT) de 1210 ms
Economia de API competitiva ($4/$20 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 5.5 · Strengths & Key Advantages

Janela de contexto de 1M com limite máximo de saída de 128K tokens (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
Taxa de transferência rápida de 78 tokens/segundo com latência inicial (TTFT) de 1400 ms
Economia de API competitiva ($4/$20 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-5.6 Sol	Claude Opus 5.5
Cost per Task Estimated USD to finish one task	$3.30	$3.85
Output Tokens / Task Tokens generated per task	64K	68K
Reasoning Tokens / Task Thinking tokens burned per task	38K	42K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-5.6 Sol	Claude Opus 5.5
Official API Model ID (SDK)	gpt-5-6-sol	claude-opus-5-5-20260901
API Protocol	OpenAI Responses API & Chat Completions v1	Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date	2026-07	2026-09
Knowledge Cutoff	February 2026	June 2026
Architecture	Frontier unified reasoning model	Extended Autonomous Hybrid Reasoning Transformer
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($4/1M in) across full 1050K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix caching at $0.4/1M (no extra cache-write fee)	$0.4/1M 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-5.6 Sol vs Claude Opus 5.5: Respostas Rápidas (FAQ)

Which is more intelligent, GPT-5.6 Sol or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 89 on the composite Intelligence Index.

Which model is faster?

GPT-5.6 Sol is faster, generating 108 tokens per second compared with 78 tokens per second.

Which model has lower latency?

GPT-5.6 Sol has lower latency, with a time to first token of 1.21s compared with 1.40s.

Which model has a larger context window?

GPT-5.6 Sol has a larger context window, supporting 1.05M tokens compared with 1M tokens.

Veredito

Tanto GPT-5.6 Sol (Indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas RAG corporativas e integração escalável via API.) quanto Claude Opus 5.5 (Indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Claude Opus 5.5 lidera em inteligência geral e programação, GPT-5.6 Sol oferece geração mais rápida e GPT-5.6 Sol apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, GPT-5.6 Sol ou Claude Opus 5.5?+

Nas avaliações de inteligência e código, Claude Opus 5.5 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, GPT-5.6 Sol ou Claude Opus 5.5?+

O GPT-5.6 Sol custa $4.00 / $20.00 por 1M de tokens (entrada/saída) e o Claude Opus 5.5 custa $4.00 / $20.00. No custo combinado, GPT-5.6 Sol é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O GPT-5.6 Sol suporta 1.05M tokens de contexto contra 1M tokens do Claude Opus 5.5, enquanto GPT-5.6 Sol entrega maior velocidade de geração.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

GPT-5.6 Sol vs Claude Opus 5.5

Troque e Compare Qualquer Modelo

GPT-5.6 Sol

Claude Opus 5.5

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

GPT-5.6 Sol · Strengths & Key Advantages

Considerations & Trade-offs

Claude Opus 5.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

GPT-5.6 Sol vs Claude Opus 5.5: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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