Head-to-Head Model Comparison

Grok 4.3 vs GPT-6.1 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Compare Grok 4.3 by xAI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.

Confronto Diretto (VS)

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xAI

Grok 4.3

Modello Ammiraglia di Frontiera

Grok 4.3 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 110 token/s, indice di intelligenza di 71/100 e tari

Intelligence Index71 / 100
Input Token:

$1.25 / 1M

Output Token:

$2.50 / 1M

Context Window:

1M tokens

SWE-bench Verified:

71.4%

OpenAI

GPT-6.1 Sol

Modello Ammiraglia di Frontiera

GPT-6.1 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 120 token/s, indice di intelligenza di 94/100

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

Differenze Chiave a Colpo d’Occhio

  • Grok 4.3 is 2.6x cheaper for API usage (input + output tokens combined).
  • GPT-6.1 Sol is faster at 120 tok/s vs 110 tok/s.
  • GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 71.4%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGrok 4.3GPT-6.1 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
7194
Provider
xAIOpenAI
Input Price
Cost per million input tokens
$1.25 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$2.50 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.72 / 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
66K128K
Throughput Speed
Tokens generated per second
110 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
71.4%82.3%
GPQA Diamond
Graduate-level reasoning
73.5%80.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.31 / 1M$0.10 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
GPT-6.1 Sol
+10.9% SWE-bench lead
Cost
Grok 4.3
2.6x cheaper monthly runtime
Speed
GPT-6.1 Sol
+9% faster generation
Context
Tied
1M tokens window

Punteggi Benchmark Verificati

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

Grok 4.3GPT-6.1 Sol
SWE-bench Verified (Autonomous Coding)

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

71.4%vs82.3%
GPQA Diamond (Doctorate-Level Science & Math)

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

73.5%vs80.8%
MMLU-Pro (Multi-task Language Understanding)

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

85.2%vs89.9%
LMSYS Chatbot Arena Elo

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

1410ptsvs1519pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

12.8%vs18.1%
Terminal-Bench (Agentic Coding & Terminal Use)

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

47%vs63.6%
SciCode (Scientific Computing)

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

44.5%vs54.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

71.2%vs79.7%
AIME (Advanced Math Competition)

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

85%vs93.5%
Long-Context Reasoning

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

88.5%vs91%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs120 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 4.30.65s
GPT-6.1 Sol1.12s
Est. End-to-End Response (500 tokens)TTFT + generation time
Grok 4.35.20s
GPT-6.1 Sol5.29s

Simulatore Interattivo dei Costi Token

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 4.3 Monthly API Bill
$4.59 / month
$1.25/1M in · $2.5/1M out
GPT-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Grok 4.3 saves approximately $7.56 / month (62%) under this workload.2.6x Savings

Grok 4.3 · Strengths & Key Advantages

  • Finestra di contesto da 1M con capacità massima di output fino a 66K token (SWE-bench Verified 71.4% · GPQA Diamond 73.5%)
  • Throughput elevato di 110 token/secondo con latenza al primo token (TTFT) di soli 650 ms
  • Economia API competitiva ($1.25/$2.5 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
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

GPT-6.1 Sol · Strengths & Key Advantages

  • Finestra di contesto da 1M con capacità massima di output fino a 128K token (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
  • Throughput elevato di 120 token/secondo con latenza al primo token (TTFT) di soli 1120 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.

MetricGrok 4.3GPT-6.1 Sol
Cost per Task
Estimated USD to finish one task
$1.35$2.05
Output Tokens / Task
Tokens generated per task
46K62K
Reasoning Tokens / Task
Thinking tokens burned per task
22K36K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureGrok 4.3GPT-6.1 Sol
Official API Model ID (SDK)grok-4-3-latestgpt-6-1-sol
API ProtocolxAI REST API (OpenAI-Compatible)OpenAI Responses API & Chat Completions v1
Release Date2026-042026-09
Knowledge CutoffJanuary 2026April 2026
ArchitectureColossus-Scale Mixture-of-ExpertsNear-Astra unified reasoning model
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 ($1.25/1M in) across full 1000K windowFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.31/1M 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 4.3 vs GPT-6.1 Sol: Risposte Rapide (FAQ)

Which is more intelligent, Grok 4.3 or GPT-6.1 Sol?

GPT-6.1 Sol is more intelligent. It scores 94 compared with 71 on the composite Intelligence Index.

Which model is faster?

GPT-6.1 Sol is faster, generating 120 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

Grok 4.3 is cheaper at $0.72 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Grok 4.3 has lower latency, with a time to first token of 0.65s compared with 1.12s.

Verdetto

Choose Grok 4.3 for ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi rag aziendali e integrazioni api scalabili., or choose GPT-6.1 Sol for ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi rag aziendali e integrazioni api scalabili..

Domande Frequenti (FAQ)

Which is cheaper: Grok 4.3 or GPT-6.1 Sol?+

Grok 4.3 costs $1.25/1M input and $2.5/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: Grok 4.3 or GPT-6.1 Sol?+

Grok 4.3 supports 1M tokens whereas GPT-6.1 Sol supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

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.

Fonti ufficiali: xAI & OpenAI Official Documentation