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Head-to-Head Model Comparison

Grok-4 vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Grok-4 vs Gemini 4 Argon: Benchmarks, Pricing & Speed Comparison

Grok-4 von xAI und Gemini 4 Argon von Google gehören zu den führenden KI-Modellen. Grok-4 bietet ein Kontextfenster von 1M Tokens bei API-Preisen von $2.50/$10.00 pro 1M Tokens, während Gemini 4 Argon 1M Tokens Kontext zu $2.00/$10.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Grok-4

Frontier-Flaggschiff-Modell

Grok-4 von xAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 125 Token/s Durchsatz, einem Intelligenz-Index von 92/100 und $2.5/$10 pro 1M Token.

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

Nativ Multimodale KI

Gemini 4 Argon von Google ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 1M Kontextfenster, 115 Token/s Durchsatz, einem Intelligenz-Index von 97/100 und $2/$10 pro 1M Token.

Intelligence Index97 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

Wichtigste Unterschiede auf einen Blick

  • 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 →
FeatureGrok-4Gemini 4 Argon
Intelligence Index
Composite reasoning & knowledge score (0-100)
9297
Provider
xAIGoogle
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 text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
64K1000K
Throughput Speed
Tokens generated per second
125 tok/s115 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

100K / day
10K500K1M
30K / day
5K150K300K
50% 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

  • 1M Kontextfenster und bis zu 64K maximale Output-Token (SWE-bench Verified 76.5% · GPQA Diamond 77.8%)
  • Hohe Generierungsgeschwindigkeit von 125 Token/Sekunde bei nur 680 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2.5/$10 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Gemini 4 Argon · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 1M maximale Output-Token (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
  • Hohe Generierungsgeschwindigkeit von 115 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$10 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Token Use & Task Economics

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

MetricGrok-4Gemini 4 Argon
Cost per Task
Estimated USD to finish one task
$1.65$2.45
Output Tokens / Task
Tokens generated per task
48K75K
Reasoning Tokens / Task
Thinking tokens burned per task
24K36K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGrok-4Gemini 4 Argon
Official API Model ID (SDK)grok-4-latestmodels/gemini-4-argon
API ProtocolxAI REST API (OpenAI-Compatible)Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-072026-09
Knowledge CutoffReal-time (Up to October 2026)June 2026
ArchitectureColossus-Scale Dense Mixture-of-ExpertsPathways Multimodal Sparse Mixture-of-Experts
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM10,000 – 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 windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.25/1M tokensImplicit + Explicit Context Caching at $0.1/1M with configurable TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxReal-time Web & X (Twitter) Search Grounding + Code ExecutionBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationVertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationxAI Production Safety & Alignment VerifiedGoogle DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision, audio, video
Output Modalitiestexttext

Grok-4 vs Gemini 4 Argon: Schnelle Antworten (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.

Fazit

Sowohl Grok-4 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 4 Argon (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 4 Argon führt bei komplexem Reasoning und Coding, Grok-4 liefert den höheren Token-Durchsatz und Gemini 4 Argon ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Grok-4 oder Gemini 4 Argon?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 4 Argon vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Grok-4 und Gemini 4 Argon?+

Grok-4 kostet $2.50 / $10.00 pro 1M Input-/Output-Tokens, Gemini 4 Argon liegt bei $2.00 / $10.00. Beim gewichteten Gesamtpreis ist Gemini 4 Argon günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Grok-4 bietet 1M Tokens Kontext gegenüber 1M Tokens bei Gemini 4 Argon. Bei der Generierungsgeschwindigkeit liegt Grok-4 vorn.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Offizielle Quellen: Offizielle API-Dokumentation von xAI und Google, Systemkarten und unabhängige Benchmark-Auswertungen (2026).