Head-to-Head Model Comparison

Grok 4.7 vs GPT-6 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

Grok 4.7Profile

GPT-6 SolProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

xAI

Grok 4.7

Model Unggulan Garis Depan

Grok 4.7 dari xAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 500K, kecepatan 110 token/detik, indeks kecerdasan 46/100, serta tarif $2/$6

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

Model Unggulan Garis Depan

GPT-6 Sol dari OpenAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1M, kecepatan 116 token/detik, indeks kecerdasan 93/100, serta tarif $2/$

Intelligence Index93 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.1%

Perbedaan Utama Sekilas

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 →

Feature	Grok 4.7	GPT-6 Sol
Intelligence Index Composite reasoning & knowledge score (0-100)	46	93
Provider	xAI	OpenAI
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 text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	66K	128K
Throughput Speed Tokens generated per second	110 tok/s	116 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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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 4.7 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

Jendela konteks 500K dengan kapasitas output maksimum hingga 66K token (SWE-bench Verified 77.2% · GPQA Diamond 78.5%)
Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 620 ms
Skema harga API kompetitif (tarif $2/$6 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

GPT-6 Sol · Strengths & Key Advantages

Jendela konteks 1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 82.1% · GPQA Diamond 80.6%)
Kecepatan generasi tinggi 116 token/detik dengan latensi token pertama (TTFT) hanya 1160 ms
Skema harga API kompetitif (tarif $2/$10 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

Token Use & Task Economics

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

Metric	Grok 4.7	GPT-6 Sol
Cost per Task Estimated USD to finish one task	$0.68	$2.00
Output Tokens / Task Tokens generated per task	46K	62K
Reasoning Tokens / Task Thinking tokens burned per task	23K	36K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Grok 4.7	GPT-6 Sol
Official API Model ID (SDK)	grok-4-7-latest	gpt-6-sol
API Protocol	xAI REST API (OpenAI-Compatible)	OpenAI Responses API & Chat Completions v1
Release Date	2026-09	2026-09
Knowledge Cutoff	April 2026	April 2026
Architecture	2.1T Multimodal Sparse MoE	Cost-efficient frontier unified reasoning model
Parameters	2.1T (MoE)	Confidential
Enterprise Rate Limits (RPM / TPM)	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 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 500K window	Flat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTL	Cached input at $0.5/1M tokens	Automatic prefix caching at $0.1/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	API Prompt Engineering & RAG Distillation	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
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	xAI Production Safety & Alignment Verified	OpenAI Preparedness Framework Audited & System Card Published
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

Grok 4.7 vs GPT-6 Sol: Jawaban Cepat (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.

Kesimpulan

Baik Grok 4.7 (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-6 Sol (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT-6 Sol unggul dalam penalaran dan pemrograman, GPT-6 Sol memberikan kecepatan generasi token lebih tinggi, dan Grok 4.7 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Grok 4.7 atau GPT-6 Sol?+

Dalam indeks kecerdasan gabungan dan benchmark coding, GPT-6 Sol memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Grok 4.7 vs GPT-6 Sol?+

Grok 4.7 mengenakan biaya $2.00 / $6.00 per 1M token input/output, sedangkan GPT-6 Sol sebesar $2.00 / $10.00. Secara keseluruhan, Grok 4.7 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Grok 4.7 mendukung konteks 500K token dibandingkan 1M token pada GPT-6 Sol, sementara GPT-6 Sol unggul dalam kecepatan output token per detik.

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 4.7 vs GPT-6 Sol

Ganti & Bandingkan Model Apa Saja

Grok 4.7

GPT-6 Sol

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Grok 4.7 · Strengths & Key Advantages

Considerations & Trade-offs

GPT-6 Sol · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Grok 4.7 vs GPT-6 Sol: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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