Head-to-Head Model Comparison

GPT-5.6 Sol vs Gemini 4 Argon

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

Gemini 4 ArgonProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

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%

Google

Gemini 4 Argon

IA Multimodal Nativa

O Gemini 4 Argon da Google é um modelo de IA projetado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, oferecendo janela de contexto de 1M, velocidade de 115 tokens/s, índice de inteligência de 97/100 e

Intelligence Index97 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

Principais Diferenças em Resumo

Gemini 4 Argon is 2.0x cheaper for API usage (input + output tokens combined).
GPT-5.6 Sol has a larger context window (1M vs 1M tokens).
Gemini 4 Argon is faster at 115 tok/s vs 108 tok/s.
GPT-5.6 Sol leads on SWE-bench coding (83.8% vs 81.9%).

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

Feature	GPT-5.6 Sol	Gemini 4 Argon
Intelligence Index Composite reasoning & knowledge score (0-100)	89	97
Provider	OpenAI	Google
Input Price Cost per million input tokens	$4.00 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$20.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$3.08 / 1M	$1.47 / 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	1000K
Throughput Speed Tokens generated per second	108 tok/s	115 tok/s
SWE-bench Verified Real-world software engineering	83.8%	81.9%
GPQA Diamond Graduate-level reasoning	82%	81.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.40 / 1M	$0.10 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

GPT-5.6 Sol

+1.9% SWE-bench lead

Cost

Gemini 4 Argon

2.0x cheaper monthly runtime

Speed

Gemini 4 Argon

+6% 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 SolGemini 4 Argon

SWE-bench Verified (Autonomous Coding)

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

83.8%vs81.9%

GPQA Diamond (Doctorate-Level Science & Math)

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

82%vs81.5%

MMLU-Pro (Multi-task Language Understanding)

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

90.8%vs90.8%

LMSYS Chatbot Arena Elo

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

1496ptsvs1525pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

17.8%vs18.9%

Terminal-Bench (Agentic Coding & Terminal Use)

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

61%vs57.4%

SciCode (Scientific Computing)

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

55%vs56.4%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

80.6%vs80.5%

AIME (Advanced Math Competition)

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

95.8%vs94.8%

Long-Context Reasoning

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

89.6%vs96.4%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

108 tok/s vs115 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

Gemini 4 Argon0.85s

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

GPT-5.6 Sol5.84s

Gemini 4 Argon5.20s

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

Gemini 4 Argon Monthly API Bill

$12.15 / month

$2/1M in · $10/1M out

Gemini 4 Argon saves approximately $12.45 / month (51%) under this workload.2.0x Savings

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

Gemini 4 Argon · Strengths & Key Advantages

Janela de contexto de 1M com limite máximo de saída de 1M tokens (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
Taxa de transferência rápida de 115 tokens/segundo com latência inicial (TTFT) de 850 ms
Economia de API competitiva ($2/$10 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	Gemini 4 Argon
Cost per Task Estimated USD to finish one task	$3.30	$2.45
Output Tokens / Task Tokens generated per task	64K	75K
Reasoning Tokens / Task Thinking tokens burned per task	38K	36K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-5.6 Sol	Gemini 4 Argon
Official API Model ID (SDK)	gpt-5-6-sol	models/gemini-4-argon
API Protocol	OpenAI Responses API & Chat Completions v1	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-07	2026-09
Knowledge Cutoff	February 2026	June 2026
Architecture	Frontier unified reasoning model	Pathways Multimodal Sparse Mixture-of-Experts
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 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)	Implicit + Explicit Context Caching at $0.1/1M with configurable 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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Vertex AI Supervised Adapter Tuning & Context 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) · Audio (~25–32 tokens/sec) · Video (~258–300 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	OpenAI Preparedness Framework Audited & System Card Published	Google DeepMind Frontier Safety Framework Verified
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, video
Output Modalities	text	text

GPT-5.6 Sol vs Gemini 4 Argon: Respostas Rápidas (FAQ)

Which is more intelligent, GPT-5.6 Sol or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 89 on the composite Intelligence Index.

Which model is faster?

Gemini 4 Argon is faster, generating 115 tokens per second compared with 108 tokens per second.

Which model is cheaper to run?

Gemini 4 Argon is cheaper at $1.47 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 4 Argon has lower latency, with a time to first token of 0.85s compared with 1.21s.

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 Gemini 4 Argon (Indicado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Gemini 4 Argon lidera em inteligência geral e programação, Gemini 4 Argon oferece geração mais rápida e Gemini 4 Argon apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, GPT-5.6 Sol ou Gemini 4 Argon?+

Nas avaliações de inteligência e código, Gemini 4 Argon leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, GPT-5.6 Sol ou Gemini 4 Argon?+

O GPT-5.6 Sol custa $4.00 / $20.00 por 1M de tokens (entrada/saída) e o Gemini 4 Argon custa $2.00 / $10.00. No custo combinado, Gemini 4 Argon 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 Gemini 4 Argon, enquanto Gemini 4 Argon 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 Gemini 4 Argon

Troque e Compare Qualquer Modelo

GPT-5.6 Sol

Gemini 4 Argon

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

Gemini 4 Argon · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

GPT-5.6 Sol vs Gemini 4 Argon: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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