Head-to-Head Model Comparison

GPT-6 Sol vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

GPT-6 SolProfile

Gemini 4 ArgonProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-6 Sol

Modello Ammiraglia di Frontiera

GPT-6 Sol di OpenAI è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1M, velocità di 116 token/s, indice di intelligenza di 93/100 e

Intelligence Index93 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.1%

Google

Gemini 4 Argon

IA Multimodale Nativa

Gemini 4 Argon di Google è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 1M, velocità di 115 token/s, indice di intelligenza di 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%

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
GPT-6 Sol is faster at 116 tok/s vs 115 tok/s.
GPT-6 Sol leads on SWE-bench coding (82.1% vs 81.9%).

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

Feature	GPT-6 Sol	Gemini 4 Argon
Intelligence Index Composite reasoning & knowledge score (0-100)	93	97
Provider	OpenAI	Google
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	$1.47 / 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	1000K
Throughput Speed Tokens generated per second	116 tok/s	115 tok/s
SWE-bench Verified Real-world software engineering	82.1%	81.9%
GPQA Diamond Graduate-level reasoning	80.6%	81.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.10 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

GPT-6 Sol

+0.2% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

GPT-6 Sol

+1% faster generation

Context

Tied

1M tokens window

Punteggi Benchmark Verificati

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

GPT-6 SolGemini 4 Argon

SWE-bench Verified (Autonomous Coding)

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

82.1%vs81.9%

GPQA Diamond (Doctorate-Level Science & Math)

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

80.6%vs81.5%

MMLU-Pro (Multi-task Language Understanding)

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

89.8%vs90.8%

LMSYS Chatbot Arena Elo

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

1516ptsvs1525pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

18%vs18.9%

Terminal-Bench (Agentic Coding & Terminal Use)

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

63.2%vs57.4%

SciCode (Scientific Computing)

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

54.5%vs56.4%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

79.5%vs80.5%

AIME (Advanced Math Competition)

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

93.4%vs94.8%

Long-Context Reasoning

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

90.8%vs96.4%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

116 tok/s vs115 tok/s

Latenza e Tempo di Risposta

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

Gemini 4 Argon0.85s

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

GPT-6 Sol5.47s

Gemini 4 Argon5.20s

Simulatore Interattivo dei Costi Token

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

Gemini 4 Argon Monthly API Bill

$12.15 / month

$2/1M in · $10/1M out

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

GPT-6 Sol · Strengths & Key Advantages

Finestra di contesto da 1M con capacità massima di output fino a 128K token (SWE-bench Verified 82.1% · GPQA Diamond 80.6%)
Throughput elevato di 116 token/secondo con latenza al primo token (TTFT) di soli 1160 ms
Economia API competitiva ($2/$10 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Gemini 4 Argon · Strengths & Key Advantages

Finestra di contesto da 1M con capacità massima di output fino a 1M token (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
Throughput elevato di 115 token/secondo con latenza al primo token (TTFT) di soli 850 ms
Economia API competitiva ($2/$10 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Token Use & Task Economics

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

Metric	GPT-6 Sol	Gemini 4 Argon
Cost per Task Estimated USD to finish one task	$2.00	$2.45
Output Tokens / Task Tokens generated per task	62K	75K
Reasoning Tokens / Task Thinking tokens burned per task	36K	36K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-6 Sol	Gemini 4 Argon
Official API Model ID (SDK)	gpt-6-sol	models/gemini-4-argon
API Protocol	OpenAI Responses API & Chat Completions v1	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-09	2026-09
Knowledge Cutoff	April 2026	June 2026
Architecture	Cost-efficient 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 ($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)	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-6 Sol vs Gemini 4 Argon: Risposte Rapide (FAQ)

Which is more intelligent, GPT-6 Sol or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 93 on the composite Intelligence Index.

Which model is faster?

GPT-6 Sol is faster, generating 116 tokens per second compared with 115 tokens per second.

Which model has lower latency?

Gemini 4 Argon has lower latency, with a time to first token of 0.85s compared with 1.16s.

Verdetto

Sia GPT-6 Sol (Ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi RAG aziendali e integrazioni API scalabili.) sia Gemini 4 Argon (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Gemini 4 Argon guida nei benchmark di ragionamento e programmazione, GPT-6 Sol garantisce una generazione più rapida e GPT-6 Sol offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GPT-6 Sol o Gemini 4 Argon?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Gemini 4 Argon. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GPT-6 Sol e Gemini 4 Argon?+

GPT-6 Sol costa $2.00 / $10.00 per milione di token di input/output, mentre Gemini 4 Argon costa $2.00 / $10.00. Nel costo combinato, GPT-6 Sol risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

GPT-6 Sol supporta 1M token di contesto contro 1M token di Gemini 4 Argon, mentre GPT-6 Sol offre una velocità di generazione superiore.

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 Gemini 4 Argon

Cambia e Confronta Qualsiasi Modello

GPT-6 Sol

Gemini 4 Argon

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

GPT-6 Sol · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 4 Argon · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

GPT-6 Sol vs Gemini 4 Argon: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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