Head-to-Head Model Comparison

GPT-6 Sol vs Claude Sonnet 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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GPT-6 SolProfile

Claude Sonnet 5.5Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-6 Sol

Modelo Insignia de Frontera

GPT-6 Sol de OpenAI es un modelo de IA diseñado para razonamiento empresarial de frontera, programación avanzada y flujos de producción, con ventana de contexto de 1M, velocidad de 116 tokens/s, índice de inteligencia de 93/100 y tarifa de

Intelligence Index93 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.1%

Anthropic

Claude Sonnet 5.5

Modelo Insignia de Frontera

Claude Sonnet 5.5 de Anthropic es un modelo de IA diseñado para razonamiento empresarial de frontera, programación avanzada y flujos de producción, con ventana de contexto de 1M, velocidad de 105 tokens/s, índice de inteligencia de 78/100 y

Intelligence Index78 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.9%

Diferencias Clave de un Vistazo

GPT-6 Sol is 1.0x cheaper for API usage (input + output tokens combined).
GPT-6 Sol is faster at 116 tok/s vs 105 tok/s.
Claude Sonnet 5.5 leads on SWE-bench coding (82.9% vs 82.1%).

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

Característica	GPT-6 Sol	Claude Sonnet 5.5
Intelligence Index Composite reasoning & knowledge score (0-100)	93	78
Provider	OpenAI	Anthropic
Input Price Cost per million input tokens	$2.00 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$10.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.47 / 1M	$1.54 / 1M
Context Window Maximum input + output tokens	1M tokens~1,500 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	116 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	82.1%	82.9%
GPQA Diamond Graduate-level reasoning	80.6%	79.4%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.20 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Claude Sonnet 5.5

+0.8% SWE-bench lead

Costo

GPT-6 Sol

1.0x cheaper monthly runtime

Velocidad

GPT-6 Sol

+10% faster generation

Contexto

Tied

1M tokens window

Puntuaciones de Benchmarks Verificadas

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

GPT-6 SolClaude Sonnet 5.5

SWE-bench Verified (Autonomous Coding)

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

82.1%vs82.9%

GPQA Diamond (Doctorate-Level Science & Math)

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

80.6%vs79.4%

MMLU-Pro (Multi-task Language Understanding)

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

89.8%vs90.1%

LMSYS Chatbot Arena Elo

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

1516ptsvs1490pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

18%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

63.2%vs58.9%

SciCode (Scientific Computing)

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

54.5%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

79.5%vsN/A

AIME (Advanced Math Competition)

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

93.4%vsN/A

Long-Context Reasoning

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

90.8%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

116 tok/s vs105 tok/s

Latencia y Tiempo de Respuesta

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-6 Sol1.16s

Claude Sonnet 5.50.55s

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

GPT-6 Sol5.47s

Claude Sonnet 5.55.31s

Simulador Interactivo de Costos 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-6 Sol Monthly API Bill

$12.15 / month

$2/1M in · $10/1M out

Claude Sonnet 5.5 Monthly API Bill

$12.30 / month

$2/1M in · $10/1M out

GPT-6 Sol saves approximately $0.15 / month (1%) under this workload.1.0x Savings

GPT-6 Sol · Strengths & Key Advantages

Ventana de contexto de 1M con hasta 128K tokens de salida máxima (SWE-bench Verified 82.1% · GPQA Diamond 80.6%)
Rendimiento ágil de 116 tokens/segundo con latencia inicial (TTFT) de solo 1160 ms
Economía de API competitiva ($2/$10 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

Claude Sonnet 5.5 · Strengths & Key Advantages

Ventana de contexto de 1M con hasta 128K tokens de salida máxima (SWE-bench Verified 82.9% · GPQA Diamond 79.4%)
Rendimiento ágil de 105 tokens/segundo con latencia inicial (TTFT) de solo 550 ms
Economía de API competitiva ($2/$10 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	GPT-6 Sol	Claude Sonnet 5.5
Cost per Task Estimated USD to finish one task	$2.00	$3.29
Output Tokens / Task Tokens generated per task	62K	72K
Reasoning Tokens / Task Thinking tokens burned per task	36K	37K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	GPT-6 Sol	Claude Sonnet 5.5
Official API Model ID (SDK)	gpt-6-sol	claude-sonnet-5-5-20260901
API Protocol	OpenAI Responses API & Chat Completions v1	Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date	2026-09	2026-09
Knowledge Cutoff	April 2026	June 2026
Architecture	Cost-efficient frontier unified reasoning model	Adaptive Frontier Multimodal 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 ($2/1M in) across full 1000K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix caching at $0.1/1M (no extra cache-write fee)	$0.2/1M 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-6 Sol vs Claude Sonnet 5.5: Respuestas Rápidas (FAQ)

Which is more intelligent, GPT-6 Sol or Claude Sonnet 5.5?

GPT-6 Sol is more intelligent. It scores 93 compared with 78 on the composite Intelligence Index.

Which model is faster?

GPT-6 Sol is faster, generating 116 tokens per second compared with 105 tokens per second.

Which model is cheaper to run?

GPT-6 Sol is cheaper at $1.47 per 1M tokens blended, compared with $1.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Sonnet 5.5 has lower latency, with a time to first token of 0.55s compared with 1.16s.

Veredicto

Tanto GPT-6 Sol (Ideal para razonamiento empresarial de frontera, programación avanzada y flujos de producción, sistemas RAG empresariales y automatización mediante API.) como Claude Sonnet 5.5 (Ideal para razonamiento empresarial de frontera, programación avanzada y flujos de producción, sistemas RAG empresariales y automatización mediante API.) destacan en producción: GPT-6 Sol lidera en inteligencia general y programación, GPT-6 Sol ofrece mayor velocidad de generación de tokens y GPT-6 Sol resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, GPT-6 Sol o Claude Sonnet 5.5?+

En las pruebas de inteligencia compuesta y resolución de código, GPT-6 Sol obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de GPT-6 Sol frente a Claude Sonnet 5.5?+

GPT-6 Sol cuesta $2.00 / $10.00 por millón de tokens de entrada/salida, mientras que Claude Sonnet 5.5 cuesta $2.00 / $10.00. En costo combinado, GPT-6 Sol es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

GPT-6 Sol cuenta con 1M tokens de contexto frente a 1M tokens de Claude Sonnet 5.5. En velocidad de inferencia, GPT-6 Sol ofrece un rendimiento superior.

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-6 Sol vs Claude Sonnet 5.5

Cambia y Compara Cualquier Modelo

GPT-6 Sol

Claude Sonnet 5.5

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

GPT-6 Sol · Strengths & Key Advantages

Considerations & Trade-offs

Claude Sonnet 5.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

GPT-6 Sol vs Claude Sonnet 5.5: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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