Head-to-Head Model Comparison

Grok-4 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

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Grok-4Profile

Gemini 4 ArgonProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

xAI

Grok-4

Modello Ammiraglia di Frontiera

Grok-4 di xAI 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 125 token/s, indice di intelligenza di 92/100 e tariff

Intelligence Index92 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

76.5%

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

Gemini 4 Argon is 1.1x cheaper for API usage (input + output tokens combined).
Grok-4 is faster at 125 tok/s vs 115 tok/s.
Gemini 4 Argon leads on SWE-bench coding (81.9% vs 76.5%).

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

Feature	Grok-4	Gemini 4 Argon
Intelligence Index Composite reasoning & knowledge score (0-100)	92	97
Provider	xAI	Google
Input Price Cost per million input tokens	$2.50 / 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	$1.68 / 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	64K	1000K
Throughput Speed Tokens generated per second	125 tok/s	115 tok/s
SWE-bench Verified Real-world software engineering	76.5%	81.9%
GPQA Diamond Graduate-level reasoning	77.8%	81.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.25 / 1M	$0.10 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Gemini 4 Argon

+5.4% SWE-bench lead

Cost

Gemini 4 Argon

1.1x cheaper monthly runtime

Speed

Grok-4

+9% faster generation

Context

Tied

1M tokens window

Punteggi Benchmark Verificati

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

Grok-4Gemini 4 Argon

SWE-bench Verified (Autonomous Coding)

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

76.5%vs81.9%

GPQA Diamond (Doctorate-Level Science & Math)

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

77.8%vs81.5%

MMLU-Pro (Multi-task Language Understanding)

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

87.4%vs90.8%

LMSYS Chatbot Arena Elo

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

1507ptsvs1525pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

15.6%vs18.9%

Terminal-Bench (Agentic Coding & Terminal Use)

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

54.2%vs57.4%

SciCode (Scientific Computing)

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

48.7%vs56.4%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

75.3%vs80.5%

AIME (Advanced Math Competition)

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

89.2%vs94.8%

Long-Context Reasoning

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

86.5%vs96.4%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

125 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

Grok-40.68s

Gemini 4 Argon0.85s

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

Grok-44.68s

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)

Grok-4 Monthly API Bill

$13.13 / month

$2.5/1M in · $10/1M out

Gemini 4 Argon Monthly API Bill

$12.15 / month

$2/1M in · $10/1M out

Gemini 4 Argon saves approximately $0.97 / month (7%) under this workload.1.1x Savings

Grok-4 · Strengths & Key Advantages

Finestra di contesto da 1M con capacità massima di output fino a 64K token (SWE-bench Verified 76.5% · GPQA Diamond 77.8%)
Throughput elevato di 125 token/secondo con latenza al primo token (TTFT) di soli 680 ms
Economia API competitiva ($2.5/$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	Grok-4	Gemini 4 Argon
Cost per Task Estimated USD to finish one task	$1.65	$2.45
Output Tokens / Task Tokens generated per task	48K	75K
Reasoning Tokens / Task Thinking tokens burned per task	24K	36K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Grok-4	Gemini 4 Argon
Official API Model ID (SDK)	grok-4-latest	models/gemini-4-argon
API Protocol	xAI REST API (OpenAI-Compatible)	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-07	2026-09
Knowledge Cutoff	Real-time (Up to October 2026)	June 2026
Architecture	Colossus-Scale Dense Mixture-of-Experts	Pathways Multimodal Sparse Mixture-of-Experts
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	5,000 – 15,000 RPM (Production Tier) · 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 ($2.5/1M in) across full 1000K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Cached input at $0.25/1M tokens	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	Real-time Web & X (Twitter) Search Grounding + Code Execution	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	API Prompt Engineering & RAG Distillation	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	xAI Production Safety & Alignment Verified	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

Grok-4 vs Gemini 4 Argon: Risposte Rapide (FAQ)

Which is more intelligent, Grok-4 or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 92 on the composite Intelligence Index.

Which model is faster?

Grok-4 is faster, generating 125 tokens per second compared with 115 tokens per second.

Which model is cheaper to run?

Gemini 4 Argon is cheaper at $1.47 per 1M tokens blended, compared with $1.68 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Grok-4 has lower latency, with a time to first token of 0.68s compared with 0.85s.

Verdetto

Sia Grok-4 (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, Grok-4 garantisce una generazione più rapida e Gemini 4 Argon offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Grok-4 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 Grok-4 e Gemini 4 Argon?+

Grok-4 costa $2.50 / $10.00 per milione di token di input/output, mentre Gemini 4 Argon costa $2.00 / $10.00. Nel costo combinato, Gemini 4 Argon risulta più economico.

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

Grok-4 supporta 1M token di contesto contro 1M token di Gemini 4 Argon, mentre Grok-4 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)

Grok-4 vs Gemini 4 Argon

Cambia e Confronta Qualsiasi Modello

Grok-4

Gemini 4 Argon

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Grok-4 · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 4 Argon · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Grok-4 vs Gemini 4 Argon: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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