GetInfoUs
Head-to-Head Model Comparison

Grok 4.7 vs GPT-6 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
Teilen
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Grok 4.7 vs GPT-6 Sol: Benchmarks, Pricing & Speed Comparison

Grok 4.7 von xAI und GPT-6 Sol von OpenAI gehören zu den führenden KI-Modellen. Grok 4.7 bietet ein Kontextfenster von 500K Tokens bei API-Preisen von $2.00/$6.00 pro 1M Tokens, während GPT-6 Sol 1M Tokens Kontext zu $2.00/$10.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

Beliebige Modelle wechseln & vergleichen

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

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
xAI

Grok 4.7

Frontier-Flaggschiff-Modell

Grok 4.7 von xAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 500K Kontextfenster, 110 Token/s Durchsatz, einem Intelligenz-Index von 46/100 und $2/$6 pro 1M Token.

Intelligence Index46 / 100
Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

500K tokens

SWE-bench Verified:

77.2%

OpenAI

GPT-6 Sol

Frontier-Flaggschiff-Modell

GPT-6 Sol von OpenAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 116 Token/s Durchsatz, einem Intelligenz-Index von 93/100 und $2/$10 pro 1M Token.

Intelligence Index93 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.1%

Wichtigste Unterschiede auf einen Blick

  • Grok 4.7 is 1.3x cheaper for API usage (input + output tokens combined).
  • GPT-6 Sol has a larger context window (1M vs 500K tokens).
  • GPT-6 Sol is faster at 116 tok/s vs 110 tok/s.
  • GPT-6 Sol leads on SWE-bench coding (82.1% vs 77.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGrok 4.7GPT-6 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
4693
Provider
xAIOpenAI
Input Price
Cost per million input tokens
$2.00 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$6.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.35 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
500K tokens~750 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
66K128K
Throughput Speed
Tokens generated per second
110 tok/s116 tok/s
SWE-bench Verified
Real-world software engineering
77.2%82.1%
GPQA Diamond
Graduate-level reasoning
78.5%80.6%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.10 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
GPT-6 Sol
+4.9% SWE-bench lead
Cost
Grok 4.7
1.3x cheaper monthly runtime
Speed
GPT-6 Sol
+5% faster generation
Context
GPT-6 Sol
1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Grok 4.7GPT-6 Sol
SWE-bench Verified (Autonomous Coding)

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

77.2%vs82.1%
GPQA Diamond (Doctorate-Level Science & Math)

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

78.5%vs80.6%
MMLU-Pro (Multi-task Language Understanding)

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

88.6%vs89.8%
LMSYS Chatbot Arena Elo

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

1465ptsvs1516pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

15.2%vs18%
Terminal-Bench (Agentic Coding & Terminal Use)

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

38%vs63.2%
SciCode (Scientific Computing)

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

52.4%vs54.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

76.5%vs79.5%
AIME (Advanced Math Competition)

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

91.2%vs93.4%
Long-Context Reasoning

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

89.5%vs90.8%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs116 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 4.70.62s
GPT-6 Sol1.16s
Est. End-to-End Response (500 tokens)TTFT + generation time
Grok 4.75.17s
GPT-6 Sol5.47s

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 4.7 Monthly API Bill
$9.15 / month
$2/1M in · $6/1M out
GPT-6 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Grok 4.7 saves approximately $3.00 / month (25%) under this workload.1.3x Savings

Grok 4.7 · Strengths & Key Advantages

  • 500K Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 77.2% · GPQA Diamond 78.5%)
  • Hohe Generierungsgeschwindigkeit von 110 Token/Sekunde bei nur 620 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$6 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

GPT-6 Sol · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 82.1% · GPQA Diamond 80.6%)
  • Hohe Generierungsgeschwindigkeit von 116 Token/Sekunde bei nur 1160 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 4.7GPT-6 Sol
Cost per Task
Estimated USD to finish one task
$0.68$2.00
Output Tokens / Task
Tokens generated per task
46K62K
Reasoning Tokens / Task
Thinking tokens burned per task
23K36K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGrok 4.7GPT-6 Sol
Official API Model ID (SDK)grok-4-7-latestgpt-6-sol
API ProtocolxAI REST API (OpenAI-Compatible)OpenAI Responses API & Chat Completions v1
Release Date2026-092026-09
Knowledge CutoffApril 2026April 2026
Architecture2.1T Multimodal Sparse MoECost-efficient frontier unified reasoning model
Parameters2.1T (MoE)Confidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 500K windowFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.5/1M tokensAutomatic prefix caching at $0.1/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)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 ExecutionNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
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)
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 VerifiedOpenAI Preparedness Framework Audited & System Card Published
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
Output Modalitiestexttext

Grok 4.7 vs GPT-6 Sol: Schnelle Antworten (FAQ)

Which is more intelligent, Grok 4.7 or GPT-6 Sol?

GPT-6 Sol is more intelligent. It scores 93 compared with 46 on the composite Intelligence Index.

Which model is faster?

GPT-6 Sol is faster, generating 116 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

Grok 4.7 is cheaper at $1.35 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Grok 4.7 has lower latency, with a time to first token of 0.62s compared with 1.16s.

Which model has a larger context window?

GPT-6 Sol has a larger context window, supporting 1M tokens compared with 500K tokens.

Fazit

Sowohl Grok 4.7 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-6 Sol (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: GPT-6 Sol führt bei komplexem Reasoning und Coding, GPT-6 Sol liefert den höheren Token-Durchsatz und Grok 4.7 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Grok 4.7 oder GPT-6 Sol?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt GPT-6 Sol vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Grok 4.7 und GPT-6 Sol?+

Grok 4.7 kostet $2.00 / $6.00 pro 1M Input-/Output-Tokens, GPT-6 Sol liegt bei $2.00 / $10.00. Beim gewichteten Gesamtpreis ist Grok 4.7 günstiger.

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

Grok 4.7 bietet 500K Tokens Kontext gegenüber 1M Tokens bei GPT-6 Sol. Bei der Generierungsgeschwindigkeit liegt GPT-6 Sol 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 OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).